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Logistic Regression can not only be used for modeling binary outcomes but also multinomial outcome with some extension. In this talk, DB will talk about basic idea of binary logistic regression step by step, and then extend to multinomial one. He will show how easy it's with Spark to parallelize this iterative algorithm by utilizing the in-memory RDD cache to scale horizontally (the numbers of training data.) However, there is mathematical limitation on scaling vertically (the numbers of training features) while many recent applications from document classification and computational linguistics are of this type. He will talk about how to address this problem by L-BFGS optimizer instead of Newton optimizer. Bio: DB Tsai is a machine learning engineer working at Alpine Data Labs. He is recently working with Spark MLlib team to add support of L-BFGS optimizer and multinomial logistic regression in the upstream. He also led the Apache Spark development at Alpine Data Labs. Before joining Alpine Data labs, he was working on large-scale optimization of optical quantum circuits at Stanford as a PhD student.

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Graphs, Trees, Paths and Their Representations

Graphs can be weighted/unweighted, directed, undirected. Lecture 2 Slides - Advanced Data Structures - GWU

ADA - Minimum Spanning Tree Prim Kruskal and Dijkstra

Topics Covered
Minimum Spanning Tree,
Prim's Algorithm
Kruskal's Algorith and
Shortest Path Algorithm (Dijkstra)

Policy Gradient Theorem

The document summarizes the policy gradient theorem, which provides a way to perform policy improvement in reinforcement learning using gradient ascent on the expected returns with respect to the policy parameters. It begins by motivating policy gradients as a way to do policy improvement when the action space is large or continuous. It then defines the necessary notation, expected returns objective function, and discounted state visitation measure. The main part of the document proves the policy gradient theorem, which expresses the policy gradient as an expectation over the discounted state visitation measure and action-value function. It notes that in practice the action-value function must be estimated, and proves the compatible function approximation theorem, which ensures the policy gradient is computed correctly when using an estimated action-value

Chapter 6: OPERATIONS ON GRAPHS

Operations on GRAPHS!

Lecture 5 6_7 - divide and conquer and method of solving recurrences

The document discusses divide and conquer algorithms and solving recurrences. It covers asymptotic notations, examples of divide and conquer including finding the largest number in a list, recurrence relations, and methods for solving recurrences including iteration, substitution, and recursion trees. The iteration method involves unfolding the recurrence into a summation. The recursion tree method visually depicts recursive calls in a tree to help solve the recurrence. Divide and conquer algorithms break problems into smaller subproblems, solve the subproblems recursively, and combine the solutions.

BCA_Semester-II-Discrete Mathematics_unit-iii_Lattices and boolean algebra

The document discusses lattices and Boolean algebra. It defines lattices as sets closed under binary operations of meet and join. It describes properties of lattices including completeness and conditional completeness. It also defines distributive lattices, complemented lattices, bounded lattices, and sub lattices. Boolean algebra is introduced as a complemented distributive lattice. Basic properties and examples of Boolean algebra are provided. Boolean expressions and equivalence are discussed along with logic gates like AND, OR, NOT, NAND, NOR, XOR, and XNOR.

Amortized Analysis of Algorithms

Worst-case analysis is sometimes overly pessimistic.
Amortized analysis of an algorithm involves computing the maximum total number of all operations on the various data structures.
Amortized cost applies to each operation, even when there are several types of operations in the sequence.
In amortized analysis, time required to perform a sequence of data structure operations is averaged over all the successive operations performed. That is, a large cost of one operation is spread out over many operations (amortized), where the others are less expensive.
Therefore, amortized anaysis can be used to show that the average cost of an operation is small, if one averages over a sequence of operations, even though one of the single operations might be very expensive.

0 1 knapsack using branch and bound

Given two integer arrays val[0...n-1] and wt[0...n-1] that represents values and weights associated with n items respectively. Find out the maximum value subset of val[] such that sum of the weights of this subset is smaller than or equal to knapsack capacity W. Here the BRANCH AND BOUND ALGORITHM is discussed .

Graphs, Trees, Paths and Their Representations

Graphs can be weighted/unweighted, directed, undirected. Lecture 2 Slides - Advanced Data Structures - GWU

ADA - Minimum Spanning Tree Prim Kruskal and Dijkstra

Topics Covered
Minimum Spanning Tree,
Prim's Algorithm
Kruskal's Algorith and
Shortest Path Algorithm (Dijkstra)

Policy Gradient Theorem

The document summarizes the policy gradient theorem, which provides a way to perform policy improvement in reinforcement learning using gradient ascent on the expected returns with respect to the policy parameters. It begins by motivating policy gradients as a way to do policy improvement when the action space is large or continuous. It then defines the necessary notation, expected returns objective function, and discounted state visitation measure. The main part of the document proves the policy gradient theorem, which expresses the policy gradient as an expectation over the discounted state visitation measure and action-value function. It notes that in practice the action-value function must be estimated, and proves the compatible function approximation theorem, which ensures the policy gradient is computed correctly when using an estimated action-value

Chapter 6: OPERATIONS ON GRAPHS

Operations on GRAPHS!

Lecture 5 6_7 - divide and conquer and method of solving recurrences

The document discusses divide and conquer algorithms and solving recurrences. It covers asymptotic notations, examples of divide and conquer including finding the largest number in a list, recurrence relations, and methods for solving recurrences including iteration, substitution, and recursion trees. The iteration method involves unfolding the recurrence into a summation. The recursion tree method visually depicts recursive calls in a tree to help solve the recurrence. Divide and conquer algorithms break problems into smaller subproblems, solve the subproblems recursively, and combine the solutions.

BCA_Semester-II-Discrete Mathematics_unit-iii_Lattices and boolean algebra

The document discusses lattices and Boolean algebra. It defines lattices as sets closed under binary operations of meet and join. It describes properties of lattices including completeness and conditional completeness. It also defines distributive lattices, complemented lattices, bounded lattices, and sub lattices. Boolean algebra is introduced as a complemented distributive lattice. Basic properties and examples of Boolean algebra are provided. Boolean expressions and equivalence are discussed along with logic gates like AND, OR, NOT, NAND, NOR, XOR, and XNOR.

Amortized Analysis of Algorithms

Worst-case analysis is sometimes overly pessimistic.
Amortized analysis of an algorithm involves computing the maximum total number of all operations on the various data structures.
Amortized cost applies to each operation, even when there are several types of operations in the sequence.
In amortized analysis, time required to perform a sequence of data structure operations is averaged over all the successive operations performed. That is, a large cost of one operation is spread out over many operations (amortized), where the others are less expensive.
Therefore, amortized anaysis can be used to show that the average cost of an operation is small, if one averages over a sequence of operations, even though one of the single operations might be very expensive.

0 1 knapsack using branch and bound

Given two integer arrays val[0...n-1] and wt[0...n-1] that represents values and weights associated with n items respectively. Find out the maximum value subset of val[] such that sum of the weights of this subset is smaller than or equal to knapsack capacity W. Here the BRANCH AND BOUND ALGORITHM is discussed .

Normalization 1 nf,2nf,3nf,bcnf

The document discusses database normalization and different normal forms. It defines normalization as removing redundant data to improve storage efficiency and integrity. It outlines Edgar Codd's introduction of normalization and the first three normal forms he proposed: 1NF, 2NF, 3NF. It also discusses Boyce-Codd Normal Form and defines the differences between 3NF and BCNF. Examples are provided to illustrate the different normal forms.

Hadoop Pig

The document describes Pig, an open-source platform for analyzing large datasets. Pig provides a high-level language called Pig Latin for expressing data analysis processes. Pig Latin scripts are compiled into sequences of MapReduce jobs that operate in parallel on a Hadoop cluster. The document discusses how to install and run Pig locally or on a Hadoop cluster, and provides an example Pig Latin script that calculates the maximum temperature by year from sample weather data.

Compiler Design LR parsing SLR ,LALR CLR

The document introduces LR parsing and simple LR parsing. It discusses LR(k) parsing which scans input from left to right and constructs a rightmost derivation in reverse. It then focuses on simple LR parsing and describes parser states and items. Examples of parsing expressions are provided to illustrate closure computation. The document also discusses more powerful LR parsers that use lookahead and the canonical LR and LALR methods. It provides examples of LR(1) item sets and describes constructing an LALR parsing table.

Dijkstra’s algorithm

The document describes Dijkstra's algorithm for finding the shortest paths between nodes in a graph. It begins with an overview of how the algorithm works by solving subproblems to find the shortest path from the source to increasingly more nodes. It then provides pseudocode for the algorithm and analyzes its time complexity of O(E+V log V) when using a Fibonacci heap. Some applications of the algorithm include traffic information systems and routing protocols like OSPF.

Deep reinforcement learning from scratch

1. The document provides an overview of deep reinforcement learning and the Deep Q-Network algorithm. It defines the key concepts of Markov Decision Processes including states, actions, rewards, and policies.
2. The Deep Q-Network uses a deep neural network as a function approximator to estimate the optimal action-value function. It employs experience replay and a separate target network to stabilize learning.
3. Experiments applying DQN to the Atari 2600 game Space Invaders are discussed, comparing different loss functions and optimizers. The standard DQN configuration with MSE loss and RMSProp performed best.

Operator precedence

This document discusses operator precedence parsing. It describes operator grammars that can be parsed efficiently using an operator precedence parser. It explains how precedence relations are defined between terminal symbols and how these relations are used during the shift-reduce parsing process to determine whether to shift or reduce at each step. It also addresses handling unary minus operators and recovering from shift/reduce errors during parsing.

Time and Space Complexity

1. Introduction to time and space complexity.
2. Different types of asymptotic notations and their limit definitions.
3. Growth of functions and types of time complexities.
4. Space and time complexity analysis of various algorithms.

1.8. equivalence of finite automaton and regular expressions

This document discusses Thompson's construction algorithm for converting regular expressions to equivalent nondeterministic finite automata (NFAs). It explains the rules for converting the operators in regular expressions, such as empty strings, symbols, union, concatenation, and Kleene star, into NFA transitions and states. Examples of converting regular expressions to NFAs are provided for practice. Thompson's construction allows regular expressions to be matched against strings using the resulting NFA.

Multi-armed Bandits

Hello~! :)
While studying the Sutton-Barto book, the traditional textbook for Reinforcement Learning, I created PPT about the Multi-armed Bandits, a Chapter 2.
If there are any mistakes, I would appreciate your feedback immediately.
Thank you.

Review on Algorithmic and Non Algorithmic Software Cost Estimation Techniques

Effective software cost estimation is the most challenging and important activities in software development. Developers want a simple and accurate method of efforts estimation. Estimation of the cost before starting of work is a prediction and prediction always not accurate. Software effort estimation is a very critical task in the software engineering and to control quality and efficiency a suitable estimation technique is crucial. This paper gives a review of various available software effort estimation methods, mainly focus on the algorithmic model and non algorithmic model. These existing methods for software cost estimation are illustrated and their aspect will be discussed. No single technique is best for all situations, and thus a careful comparison of the results of several approaches is most likely to produce realistic estimation. This paper provides a detailed overview of existing software cost estimation models and techniques. This paper presents the strength and weakness of various cost estimation methods. This paper focuses on some of the relevant reasons that cause inaccurate estimation. Pa Pa Win | War War Myint | Hlaing Phyu Phyu Mon | Seint Wint Thu "Review on Algorithmic and Non-Algorithmic Software Cost Estimation Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26511.pdfPaper URL: https://www.ijtsrd.com/engineering/-/26511/review-on-algorithmic-and-non-algorithmic-software-cost-estimation-techniques/pa-pa-win

Lexical analyzer generator lex

LEX is a tool that allows users to specify a lexical analyzer by defining patterns for tokens using regular expressions. The LEX compiler transforms these patterns into a transition diagram and generates C code. It takes a LEX source program as input, compiles it to produce lex.yy.c, which is then compiled with a C compiler to generate an executable that takes an input stream and returns a sequence of tokens. LEX programs have declarations, translation rules that map patterns to actions, and optional auxiliary functions. The actions are fragments of C code that execute when a pattern is matched.

Non- Deterministic Algorithms

This document discusses deterministic and non-deterministic algorithms. A deterministic algorithm always produces the same output for a given input, while a non-deterministic algorithm may have multiple possible outputs for one input. Non-deterministic algorithms have two stages: a guessing stage that generates a potential solution, and a verification stage that checks if the guess is correct. Examples of non-deterministic algorithms given are a search algorithm that guesses a location containing the search value, and a sorting algorithm that guesses the sorted order of elements.

Divide and Conquer - Part 1

Divide and Conquer Algorithms - D&C forms a distinct algorithm design technique in computer science, wherein a problem is solved by repeatedly invoking the algorithm on smaller occurrences of the same problem. Binary search, merge sort, Euclid's algorithm can all be formulated as examples of divide and conquer algorithms. Strassen's algorithm and Nearest Neighbor algorithm are two other examples.

Query trees

Hey friends, here is my "query tree" assignment. :-) I have searched a lot to get this master piece :p and I can guarantee you that this one gonna help you In Sha ALLAH more than any else document on the subject. Have a good day :-)

Apache Spark Core

Spark is an open-source cluster computing framework that uses in-memory processing to allow data sharing across jobs for faster iterative queries and interactive analytics, it uses Resilient Distributed Datasets (RDDs) that can survive failures through lineage tracking and supports programming in Scala, Java, and Python for batch, streaming, and machine learning workloads.

Parse Tree

1. A parse tree shows how the start symbol of a grammar derives a string in the language by using interior nodes labeled with nonterminals and children labeled with terminals or nonterminals according to the grammar's productions.
2. An ambiguous grammar is one where a single string can have more than one parse tree, leftmost derivation, rightmost derivation, or other derivation according to the grammar.
3. Operator precedence and associativity determine the order of operations when multiple operators are present in an expression. For example, multiplication generally has higher precedence than addition, and most operators associate left-to-right.

Biconnected components (13024116056)

This document discusses bi-connected components in graphs. It defines an articulation point as a vertex in a connected graph whose removal would disconnect the graph. A bi-connected component is a maximal subgraph that contains no articulation points. The document presents algorithms for identifying articulation points and bi-connected components in a graph using depth-first search (DFS). It introduces the concepts of tree edges, back edges, forward edges and cross edges in a DFS tree and explains how to use these edge types to determine if a vertex is an articulation point based on the minimum discovery time of its descendant vertices.

Transformer xl

This slide introduces transformer-xl which is the base paper for xl-net. You can understand what is the major contribution of this paper using this slide. This slide also explains the transformer for comparing differences between transformer and transformer-xl. Happy NLP!

Bellman Ford's Algorithm

The Bellman–Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted digraph.

1.7. eqivalence of nfa and dfa

The document discusses the conversion of non-deterministic finite automata (NFA) to deterministic finite automata (DFA). It states that an NFA with epsilon transitions can first be converted to an NFA without epsilon transitions, which can then be converted to an equivalent DFA. The conversion of NFA to DFA involves constructing a DFA with up to 2^n states for an n-state NFA, with the same input alphabet and transition and acceptance functions based on the NFA. Examples are provided of converting epsilon NFAs to NFAs without epsilon transitions and NFAs to equivalent DFAs.

Multinomial Logistic Regression with Apache Spark

Logistic Regression can not only be used for modeling binary outcomes but also multinomial outcome with some extension. In this talk, DB will talk about basic idea of binary logistic regression step by step, and then extend to multinomial one. He will show how easy it's with Spark to parallelize this iterative algorithm by utilizing the in-memory RDD cache to scale horizontally (the numbers of training data.) However, there is mathematical limitation on scaling vertically (the numbers of training features) while many recent applications from document classification and computational linguistics are of this type. He will talk about how to address this problem by L-BFGS optimizer instead of Newton optimizer.
Bio:
DB Tsai is a machine learning engineer working at Alpine Data Labs. He is recently working with Spark MLlib team to add support of L-BFGS optimizer and multinomial logistic regression in the upstream. He also led the Apache Spark development at Alpine Data Labs. Before joining Alpine Data labs, he was working on large-scale optimization of optical quantum circuits at Stanford as a PhD student.

2015-06-15 Large-Scale Elastic-Net Regularized Generalized Linear Models at S...

Nonlinear methods are widely used to produce higher performance compared with linear methods; however, nonlinear methods are generally more expensive in model size, training time, and scoring phase. With proper feature engineering techniques like polynomial expansion, the linear methods can be as competitive as those nonlinear methods. In the process of mapping the data to higher dimensional space, the linear methods will be subject to overfitting and instability of coefficients which can be addressed by penalization methods including Lasso and Elastic-Net. Finally, we'll show how to train linear models with Elastic-Net regularization using MLlib.
Several learning algorithms such as kernel methods, decision tress, and random forests are nonlinear approaches which are widely used to have better performance compared with linear methods. However, with feature engineering techniques like polynomial expansion by mapping the data into a higher dimensional space, the performance of linear methods can be as competitive as those nonlinear methods. As a result, linear methods remain to be very useful given that the training time of linear methods is significantly faster than the nonlinear ones, and the model is just a simple small vector which makes the prediction step very efficient and easy. However, by mapping the data into higher dimensional space, those linear methods are subject to overfitting and instability of coefficients, and those issues can be successfully addressed by penalization methods including Lasso and Elastic-Net. Lasso method with L1 penalty tends to result in many coefficients shrunk exactly to zero and a few other coefficients with comparatively little shrinkage. L2 penalty trends to result in all small but non-zero coefficients. Combining L1 and L2 penalties are called Elastic-Net method which tends to give a result in between. In the first part of the talk, we'll give an overview of linear methods including commonly used formulations and optimization techniques such as L-BFGS and OWLQN. In the second part of talk, we will talk about how to train linear models with Elastic-Net using our recent contribution to Spark MLlib. We'll also talk about how linear models are practically applied with big dataset, and how polynomial expansion can be used to dramatically increase the performance.
DB Tsai is an Apache Spark committer and a Senior Research Engineer at Netflix. He is recently working with Apache Spark community to add several new algorithms including Linear Regression and Binary Logistic Regression with ElasticNet (L1/L2) regularization, Multinomial Logistic Regression, and LBFGS optimizer. Prior to joining Netflix, DB was a Lead Machine Learning Engineer at Alpine Data Labs, where he developed innovative large-scale distributed linear algorithms, and then contributed back to open source Apache Spark project.

Normalization 1 nf,2nf,3nf,bcnf

The document discusses database normalization and different normal forms. It defines normalization as removing redundant data to improve storage efficiency and integrity. It outlines Edgar Codd's introduction of normalization and the first three normal forms he proposed: 1NF, 2NF, 3NF. It also discusses Boyce-Codd Normal Form and defines the differences between 3NF and BCNF. Examples are provided to illustrate the different normal forms.

Hadoop Pig

The document describes Pig, an open-source platform for analyzing large datasets. Pig provides a high-level language called Pig Latin for expressing data analysis processes. Pig Latin scripts are compiled into sequences of MapReduce jobs that operate in parallel on a Hadoop cluster. The document discusses how to install and run Pig locally or on a Hadoop cluster, and provides an example Pig Latin script that calculates the maximum temperature by year from sample weather data.

Compiler Design LR parsing SLR ,LALR CLR

The document introduces LR parsing and simple LR parsing. It discusses LR(k) parsing which scans input from left to right and constructs a rightmost derivation in reverse. It then focuses on simple LR parsing and describes parser states and items. Examples of parsing expressions are provided to illustrate closure computation. The document also discusses more powerful LR parsers that use lookahead and the canonical LR and LALR methods. It provides examples of LR(1) item sets and describes constructing an LALR parsing table.

Dijkstra’s algorithm

The document describes Dijkstra's algorithm for finding the shortest paths between nodes in a graph. It begins with an overview of how the algorithm works by solving subproblems to find the shortest path from the source to increasingly more nodes. It then provides pseudocode for the algorithm and analyzes its time complexity of O(E+V log V) when using a Fibonacci heap. Some applications of the algorithm include traffic information systems and routing protocols like OSPF.

Deep reinforcement learning from scratch

1. The document provides an overview of deep reinforcement learning and the Deep Q-Network algorithm. It defines the key concepts of Markov Decision Processes including states, actions, rewards, and policies.
2. The Deep Q-Network uses a deep neural network as a function approximator to estimate the optimal action-value function. It employs experience replay and a separate target network to stabilize learning.
3. Experiments applying DQN to the Atari 2600 game Space Invaders are discussed, comparing different loss functions and optimizers. The standard DQN configuration with MSE loss and RMSProp performed best.

Operator precedence

This document discusses operator precedence parsing. It describes operator grammars that can be parsed efficiently using an operator precedence parser. It explains how precedence relations are defined between terminal symbols and how these relations are used during the shift-reduce parsing process to determine whether to shift or reduce at each step. It also addresses handling unary minus operators and recovering from shift/reduce errors during parsing.

Time and Space Complexity

1. Introduction to time and space complexity.
2. Different types of asymptotic notations and their limit definitions.
3. Growth of functions and types of time complexities.
4. Space and time complexity analysis of various algorithms.

1.8. equivalence of finite automaton and regular expressions

This document discusses Thompson's construction algorithm for converting regular expressions to equivalent nondeterministic finite automata (NFAs). It explains the rules for converting the operators in regular expressions, such as empty strings, symbols, union, concatenation, and Kleene star, into NFA transitions and states. Examples of converting regular expressions to NFAs are provided for practice. Thompson's construction allows regular expressions to be matched against strings using the resulting NFA.

Multi-armed Bandits

Hello~! :)
While studying the Sutton-Barto book, the traditional textbook for Reinforcement Learning, I created PPT about the Multi-armed Bandits, a Chapter 2.
If there are any mistakes, I would appreciate your feedback immediately.
Thank you.

Review on Algorithmic and Non Algorithmic Software Cost Estimation Techniques

Effective software cost estimation is the most challenging and important activities in software development. Developers want a simple and accurate method of efforts estimation. Estimation of the cost before starting of work is a prediction and prediction always not accurate. Software effort estimation is a very critical task in the software engineering and to control quality and efficiency a suitable estimation technique is crucial. This paper gives a review of various available software effort estimation methods, mainly focus on the algorithmic model and non algorithmic model. These existing methods for software cost estimation are illustrated and their aspect will be discussed. No single technique is best for all situations, and thus a careful comparison of the results of several approaches is most likely to produce realistic estimation. This paper provides a detailed overview of existing software cost estimation models and techniques. This paper presents the strength and weakness of various cost estimation methods. This paper focuses on some of the relevant reasons that cause inaccurate estimation. Pa Pa Win | War War Myint | Hlaing Phyu Phyu Mon | Seint Wint Thu "Review on Algorithmic and Non-Algorithmic Software Cost Estimation Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26511.pdfPaper URL: https://www.ijtsrd.com/engineering/-/26511/review-on-algorithmic-and-non-algorithmic-software-cost-estimation-techniques/pa-pa-win

Lexical analyzer generator lex

LEX is a tool that allows users to specify a lexical analyzer by defining patterns for tokens using regular expressions. The LEX compiler transforms these patterns into a transition diagram and generates C code. It takes a LEX source program as input, compiles it to produce lex.yy.c, which is then compiled with a C compiler to generate an executable that takes an input stream and returns a sequence of tokens. LEX programs have declarations, translation rules that map patterns to actions, and optional auxiliary functions. The actions are fragments of C code that execute when a pattern is matched.

Non- Deterministic Algorithms

This document discusses deterministic and non-deterministic algorithms. A deterministic algorithm always produces the same output for a given input, while a non-deterministic algorithm may have multiple possible outputs for one input. Non-deterministic algorithms have two stages: a guessing stage that generates a potential solution, and a verification stage that checks if the guess is correct. Examples of non-deterministic algorithms given are a search algorithm that guesses a location containing the search value, and a sorting algorithm that guesses the sorted order of elements.

Divide and Conquer - Part 1

Divide and Conquer Algorithms - D&C forms a distinct algorithm design technique in computer science, wherein a problem is solved by repeatedly invoking the algorithm on smaller occurrences of the same problem. Binary search, merge sort, Euclid's algorithm can all be formulated as examples of divide and conquer algorithms. Strassen's algorithm and Nearest Neighbor algorithm are two other examples.

Query trees

Hey friends, here is my "query tree" assignment. :-) I have searched a lot to get this master piece :p and I can guarantee you that this one gonna help you In Sha ALLAH more than any else document on the subject. Have a good day :-)

Apache Spark Core

Spark is an open-source cluster computing framework that uses in-memory processing to allow data sharing across jobs for faster iterative queries and interactive analytics, it uses Resilient Distributed Datasets (RDDs) that can survive failures through lineage tracking and supports programming in Scala, Java, and Python for batch, streaming, and machine learning workloads.

Parse Tree

1. A parse tree shows how the start symbol of a grammar derives a string in the language by using interior nodes labeled with nonterminals and children labeled with terminals or nonterminals according to the grammar's productions.
2. An ambiguous grammar is one where a single string can have more than one parse tree, leftmost derivation, rightmost derivation, or other derivation according to the grammar.
3. Operator precedence and associativity determine the order of operations when multiple operators are present in an expression. For example, multiplication generally has higher precedence than addition, and most operators associate left-to-right.

Biconnected components (13024116056)

This document discusses bi-connected components in graphs. It defines an articulation point as a vertex in a connected graph whose removal would disconnect the graph. A bi-connected component is a maximal subgraph that contains no articulation points. The document presents algorithms for identifying articulation points and bi-connected components in a graph using depth-first search (DFS). It introduces the concepts of tree edges, back edges, forward edges and cross edges in a DFS tree and explains how to use these edge types to determine if a vertex is an articulation point based on the minimum discovery time of its descendant vertices.

Transformer xl

This slide introduces transformer-xl which is the base paper for xl-net. You can understand what is the major contribution of this paper using this slide. This slide also explains the transformer for comparing differences between transformer and transformer-xl. Happy NLP!

Bellman Ford's Algorithm

The Bellman–Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted digraph.

1.7. eqivalence of nfa and dfa

The document discusses the conversion of non-deterministic finite automata (NFA) to deterministic finite automata (DFA). It states that an NFA with epsilon transitions can first be converted to an NFA without epsilon transitions, which can then be converted to an equivalent DFA. The conversion of NFA to DFA involves constructing a DFA with up to 2^n states for an n-state NFA, with the same input alphabet and transition and acceptance functions based on the NFA. Examples are provided of converting epsilon NFAs to NFAs without epsilon transitions and NFAs to equivalent DFAs.

Normalization 1 nf,2nf,3nf,bcnf

Normalization 1 nf,2nf,3nf,bcnf

Hadoop Pig

Hadoop Pig

Compiler Design LR parsing SLR ,LALR CLR

Compiler Design LR parsing SLR ,LALR CLR

Dijkstra’s algorithm

Dijkstra’s algorithm

Deep reinforcement learning from scratch

Deep reinforcement learning from scratch

Operator precedence

Operator precedence

Time and Space Complexity

Time and Space Complexity

1.8. equivalence of finite automaton and regular expressions

1.8. equivalence of finite automaton and regular expressions

Multi-armed Bandits

Multi-armed Bandits

Review on Algorithmic and Non Algorithmic Software Cost Estimation Techniques

Review on Algorithmic and Non Algorithmic Software Cost Estimation Techniques

Lexical analyzer generator lex

Lexical analyzer generator lex

Non- Deterministic Algorithms

Non- Deterministic Algorithms

Divide and Conquer - Part 1

Divide and Conquer - Part 1

Query trees

Query trees

Apache Spark Core

Apache Spark Core

Parse Tree

Parse Tree

Biconnected components (13024116056)

Biconnected components (13024116056)

Transformer xl

Transformer xl

Bellman Ford's Algorithm

Bellman Ford's Algorithm

1.7. eqivalence of nfa and dfa

1.7. eqivalence of nfa and dfa

Multinomial Logistic Regression with Apache Spark

Logistic Regression can not only be used for modeling binary outcomes but also multinomial outcome with some extension. In this talk, DB will talk about basic idea of binary logistic regression step by step, and then extend to multinomial one. He will show how easy it's with Spark to parallelize this iterative algorithm by utilizing the in-memory RDD cache to scale horizontally (the numbers of training data.) However, there is mathematical limitation on scaling vertically (the numbers of training features) while many recent applications from document classification and computational linguistics are of this type. He will talk about how to address this problem by L-BFGS optimizer instead of Newton optimizer.
Bio:
DB Tsai is a machine learning engineer working at Alpine Data Labs. He is recently working with Spark MLlib team to add support of L-BFGS optimizer and multinomial logistic regression in the upstream. He also led the Apache Spark development at Alpine Data Labs. Before joining Alpine Data labs, he was working on large-scale optimization of optical quantum circuits at Stanford as a PhD student.

2015-06-15 Large-Scale Elastic-Net Regularized Generalized Linear Models at S...

Nonlinear methods are widely used to produce higher performance compared with linear methods; however, nonlinear methods are generally more expensive in model size, training time, and scoring phase. With proper feature engineering techniques like polynomial expansion, the linear methods can be as competitive as those nonlinear methods. In the process of mapping the data to higher dimensional space, the linear methods will be subject to overfitting and instability of coefficients which can be addressed by penalization methods including Lasso and Elastic-Net. Finally, we'll show how to train linear models with Elastic-Net regularization using MLlib.
Several learning algorithms such as kernel methods, decision tress, and random forests are nonlinear approaches which are widely used to have better performance compared with linear methods. However, with feature engineering techniques like polynomial expansion by mapping the data into a higher dimensional space, the performance of linear methods can be as competitive as those nonlinear methods. As a result, linear methods remain to be very useful given that the training time of linear methods is significantly faster than the nonlinear ones, and the model is just a simple small vector which makes the prediction step very efficient and easy. However, by mapping the data into higher dimensional space, those linear methods are subject to overfitting and instability of coefficients, and those issues can be successfully addressed by penalization methods including Lasso and Elastic-Net. Lasso method with L1 penalty tends to result in many coefficients shrunk exactly to zero and a few other coefficients with comparatively little shrinkage. L2 penalty trends to result in all small but non-zero coefficients. Combining L1 and L2 penalties are called Elastic-Net method which tends to give a result in between. In the first part of the talk, we'll give an overview of linear methods including commonly used formulations and optimization techniques such as L-BFGS and OWLQN. In the second part of talk, we will talk about how to train linear models with Elastic-Net using our recent contribution to Spark MLlib. We'll also talk about how linear models are practically applied with big dataset, and how polynomial expansion can be used to dramatically increase the performance.
DB Tsai is an Apache Spark committer and a Senior Research Engineer at Netflix. He is recently working with Apache Spark community to add several new algorithms including Linear Regression and Binary Logistic Regression with ElasticNet (L1/L2) regularization, Multinomial Logistic Regression, and LBFGS optimizer. Prior to joining Netflix, DB was a Lead Machine Learning Engineer at Alpine Data Labs, where he developed innovative large-scale distributed linear algorithms, and then contributed back to open source Apache Spark project.

Large scale logistic regression and linear support vector machines using spark

Large scale logistic regression and linear support vector machines using sparkMila, Université de Montréal

The document discusses distributed linear classification on Apache Spark. It describes using Spark to train logistic regression and linear support vector machine models on large datasets. Spark improves on MapReduce by conducting communications in-memory and supporting fault tolerance. The paper proposes using a trust region Newton method to optimize the objective functions for logistic regression and linear SVM. Conjugate gradient is used to approximate the Hessian matrix and solve the Newton system without explicitly storing the large Hessian.Query Engines for Hive: MR, Spark, Tez with LLAP – Considerations!

This document discusses using Spark as an execution engine for Hive queries. It begins by explaining that Hive and Spark are both commonly used in the big data space, and that Hive on Spark uses the Hive optimizer with the Spark query engine, while Spark with a Hive context uses both the Catalyst optimizer and Spark engine. The document then covers challenges in deploying Hive on Spark, such as using a custom Spark JAR without Hive dependencies. It shows how the Hive EXPLAIN command works the same on Spark, and how the execution plan and stages differ between MapReduce and Spark. Overall, the document provides a high-level overview of using Spark as a query engine for Hive.

Generalized Linear Models in Spark MLlib and SparkR by Xiangrui Meng

Generalized linear models (GLMs) are a class of models that include linear regression, logistic regression, and other forms. GLMs are implemented in both MLlib and SparkR in Spark. They support various solvers like gradient descent, L-BFGS, and iteratively re-weighted least squares. Performance is optimized through techniques like sparsity, tree aggregation, and avoiding unnecessary data copies. Future work includes better handling of categoricals, more model statistics, and model parallelism.

Forecasting stock market movement direction with support vector machine

Forecasting stock market movement direction with support vector machine.
Elaborted by Mohamed DHAOUI ( contact@mohamed-dhaoui.com )

Generalized Linear Models in Spark MLlib and SparkR

Generalized linear models (GLMs) unify various statistical models such as linear regression and logistic regression through the specification of a model family and link function. They are widely used in modeling, inference, and prediction with applications in numerous fields. In this talk, we will summarize recent community efforts in supporting GLMs in Spark MLlib and SparkR. We will review supported model families, link functions, and regularization types, as well as their use cases, e.g., logistic regression for classification and log-linear model for survival analysis. Then we discuss the choices of solvers and their pros and cons given training datasets of different sizes, and implementation details in order to match R’s model output and summary statistics. We will also demonstrate the APIs in MLlib and SparkR, including R model formula support, which make building linear models a simple task in Spark. This is a joint work with Eric Liang, Yanbo Liang, and some other Spark contributors.

Scaling out logistic regression with Spark

This document discusses scaling out logistic regression with Apache Spark. It describes the need to classify a large number of websites using machine learning. Several approaches to logistic regression were tried, including a single machine Java implementation and moving to Spark for better scalability. Spark's L-BFGS algorithm was chosen for its out of the box distributed logistic regression solution. Challenges implementing logistic regression at large scale are discussed, such as overfitting and regularization. Methods used to address these challenges include L2 regularization, cross-validation to select the regularization parameter, and extensions made to Spark's LBFGS implementation.

Introduction to Machine Learning & Classification

Machine learning models are trained on past data with known outcomes to predict unknown future outcomes. The document compares several machine learning algorithms on a medical dataset to predict kidney disease:
- ZeroR classified 28.2% correctly by always predicting stage 3 disease.
- Naive Bayes classified 56.6% correctly using attribute probabilities.
- OneR classified 80.2% correctly with a single rule based on serum creatinine levels.
- J4.5 decision tree classified the highest at 88.4% correctly by recursively splitting data into subgroups based on attribute information gains.

Machine Learning with Big Data using Apache Spark

"Machine Learning with Big Data
using Apache Spark" was presented to Lansing Big Data and Hadoop User Group by Muk Agaram and Amit Singh on 3/31/2015. It goes over the basics of machine learning and demos a use case of predicting recession using Apache Spark through Logistic Regression, SVM and Random Forest Algorithm

Apache HBase + Spark: Leveraging your Non-Relational Datastore in Batch and S...

Apache HBase + Spark: Leveraging your Non-Relational Datastore in Batch and S...DataWorks Summit/Hadoop Summit

This document discusses leveraging Apache HBase as a non-relational datastore in Apache Spark batch and streaming applications. It outlines integration patterns for reading from and writing to HBase using Spark, provides examples of API usage, and discusses future work including using HBase edits as a streaming source.Building Random Forest at Scale

- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata

Elasticity & forecasting

Elasticity measures how responsive demand or supply is to changes in price. Price elasticity of demand specifically refers to the percentage change in quantity demanded given a percentage change in price. Elasticity is calculated using various methods and provides important insights for businesses in determining pricing and revenue impacts. Forecasting demand, including for new products, allows businesses to effectively plan resources and operations.

Embrace Sparsity At Web Scale: Apache Spark MLlib Algorithms Optimization For...

This document discusses optimizations made to Apache Spark MLlib algorithms to better support sparse data at large scale. It describes how KMeans, linear methods, and other ML algorithms were modified to use sparse vector representations to reduce memory usage and improve performance when working with sparse data, including optimizations made for clustering large, high-dimensional datasets. The optimizations allow these algorithms to be applied to much larger sparse datasets and high-dimensional problems than was previously possible with MLlib.

Logistic regression

This document provides an overview of logistic regression analysis. It introduces the need for logistic regression when the dependent variable is binary. Key concepts covered include the logistic regression model, interpreting the beta coefficients, assessing goodness of fit using various tests and metrics, and an example of fitting a logistic regression line to predict burger purchasing based on a customer's age. Students are instructed to use statistical software to estimate a logistic regression model and interpret the results.

Elasticity+and+its+application

Elasticity measures the responsiveness of quantity demanded or supplied to a change in its price or other factors. There are different types of elasticity: price elasticity measures the change in quantity demanded of a good due to a price change; income elasticity measures the change in quantity demanded due to a change in income; cross-price elasticity measures the change in quantity demanded of one good due to a price change in another good. Elasticity values can indicate whether demand is elastic or inelastic, and whether goods are substitutes or complements. Total revenue is impacted by whether demand is elastic or inelastic.

Feature Hashing for Scalable Machine Learning: Spark Summit East talk by Nick...

Feature hashing is a powerful technique for handling high-dimensional features in machine learning. It is fast, simple, memory-efficient, and well suited to online learning scenarios. While an approximation, it has surprisingly low accuracy tradeoffs in many machine learning problems.
Feature hashing has been made somewhat popular by libraries such as Vowpal Wabbit and scikit-learn. In Spark MLlib, it is mostly used for text features, however its use cases extend more broadly. Many Spark users are not familiar with the ways in which feature hashing might be applied to their problems.
In this talk, I will cover the basics of feature hashing, and how to use it for all feature types in machine learning. I will also introduce a more flexible and powerful feature hashing transformer for use within Spark ML pipelines. Finally, I will explore the performance and scalability tradeoffs of feature hashing on various datasets.

[DSC 2016] 系列活動：李宏毅 / 一天搞懂深度學習

深度學習 ( Deep Learning ) 是機器學習 ( Machine Learning ) 中近年來備受重視的一支，深度學習根源於類神經網路 ( Artificial Neural Network ) 模型，但今日深度學習的技術和它的前身已截然不同，目前最好的語音辨識和影像辨識系統都是以深度學習技術來完成，你可能在很多不同的場合聽過各種用深度學習做出的驚人應用 ( 例如：最近紅遍大街小巷的 AlphaGo )，聽完以後覺得心癢癢的，想要趕快使用這項強大的技術，卻不知要從何下手學習，那這門課就是你所需要的。
這門課程將由台大電機系李宏毅教授利用短短的一天議程簡介深度學習。以下是課程大綱：
什麼是深度學習
深度學習的技術表面上看起來五花八門，但其實就是三個步驟：設定好類神經網路架構、訂出學習目標、開始學習，這堂課會簡介如何使用深度學習的工具 Keras，它可以幫助你在十分鐘內完成深度學習的程式。另外，有人說深度學習很厲害、有各種吹捧，也有人說深度學習只是個噱頭，到底深度學習和其他的機器學習方法有什麼不同呢？這堂課要剖析深度學習和其它機器學習方法相比潛在的優勢。
深度學習的各種小技巧
雖然現在深度學習的工具滿街都是，想要寫一個深度學習的程式只是舉手之勞，但要得到好的成果可不簡單，訓練過程中各種枝枝節節的小技巧才是成功的關鍵。本課程中將分享深度學習的實作技巧及實戰經驗。
有記憶力的深度學習模型
機器需要記憶力才能做更多事情，這段課程要講解遞迴式類神經網路 ( Recurrent Neural Network )，告訴大家深度學習模型如何可以有記憶力。
深度學習應用與展望
深度學習可以拿來做甚麼？怎麼用深度學習做語音辨識？怎麼用深度學習做問答系統？接下來深度學習的研究者們在意的是什麼樣的問題呢？
本課程希望幫助大家不只能了解深度學習，也可以有效率地上手深度學習，用在手邊的問題上。無論是從未嘗試過深度學習的新手，還是已經有一點經驗想更深入學習，都可以在這門課中有所收穫。

Introduction to Big Data/Machine Learning

This document provides an introduction to machine learning. It begins with an agenda that lists topics such as introduction, theory, top 10 algorithms, recommendations, classification with naive Bayes, linear regression, clustering, principal component analysis, MapReduce, and conclusion. It then discusses what big data is and how data is accumulating at tremendous rates from various sources. It explains the volume, variety, and velocity aspects of big data. The document also provides examples of machine learning applications and discusses extracting insights from data using various algorithms. It discusses issues in machine learning like overfitting and underfitting data and the importance of testing algorithms. The document concludes that machine learning has vast potential but is very difficult to realize that potential as it requires strong mathematics skills.

What Makes Great Infographics

SlideShare now has a player specifically designed for infographics. Upload your infographics now and see them take off! Need advice on creating infographics? This presentation includes tips for producing stand-out infographics. Read more about the new SlideShare infographics player here: http://wp.me/p24NNG-2ay
This infographic was designed by Column Five: http://columnfivemedia.com/

Multinomial Logistic Regression with Apache Spark

Multinomial Logistic Regression with Apache Spark

2015-06-15 Large-Scale Elastic-Net Regularized Generalized Linear Models at S...

2015-06-15 Large-Scale Elastic-Net Regularized Generalized Linear Models at S...

Large scale logistic regression and linear support vector machines using spark

Large scale logistic regression and linear support vector machines using spark

Query Engines for Hive: MR, Spark, Tez with LLAP – Considerations!

Query Engines for Hive: MR, Spark, Tez with LLAP – Considerations!

Generalized Linear Models in Spark MLlib and SparkR by Xiangrui Meng

Generalized Linear Models in Spark MLlib and SparkR by Xiangrui Meng

Forecasting stock market movement direction with support vector machine

Forecasting stock market movement direction with support vector machine

Generalized Linear Models in Spark MLlib and SparkR

Generalized Linear Models in Spark MLlib and SparkR

Scaling out logistic regression with Spark

Scaling out logistic regression with Spark

Introduction to Machine Learning & Classification

Introduction to Machine Learning & Classification

Machine Learning with Big Data using Apache Spark

Machine Learning with Big Data using Apache Spark

Apache HBase + Spark: Leveraging your Non-Relational Datastore in Batch and S...

Apache HBase + Spark: Leveraging your Non-Relational Datastore in Batch and S...

Building Random Forest at Scale

Building Random Forest at Scale

Elasticity & forecasting

Elasticity & forecasting

Embrace Sparsity At Web Scale: Apache Spark MLlib Algorithms Optimization For...

Embrace Sparsity At Web Scale: Apache Spark MLlib Algorithms Optimization For...

Logistic regression

Logistic regression

Elasticity+and+its+application

Elasticity+and+its+application

Feature Hashing for Scalable Machine Learning: Spark Summit East talk by Nick...

Feature Hashing for Scalable Machine Learning: Spark Summit East talk by Nick...

[DSC 2016] 系列活動：李宏毅 / 一天搞懂深度學習

[DSC 2016] 系列活動：李宏毅 / 一天搞懂深度學習

Introduction to Big Data/Machine Learning

Introduction to Big Data/Machine Learning

What Makes Great Infographics

What Makes Great Infographics

Alpine Spark Implementation - Technical

Alpine Data Labs presents a deep dive into our implementation of Multinomial Logistic Regression with Apache Spark. Machine Learning Engineer DB Tsai takes us through the technical implementation details step by step. First, he explains how the state of the art Machine Learning on Hadoop is not doing fulfilling the promise of Big Data. Next, he explains how Spark is a perfect match for machine learning through their in-memory cache-ing capability demonstrating 100x performance improvement. Third, he takes us through each aspect of a multinomial logistic regression and how this is developed with Spark APIs. Fourth, he demonstrates an extension of MLOR and training parameters. Finally, he benchmarks MLOR with 11M rows, 123 features, 11% non-zero elements with a 5 node Hadoop cluster. Finally, he shows Alpine's unique visual environment with Spark and verifies the performance with the job tracker. In conclusion, Alpine supports the state of the art Cloudera and Pivotal Hadoop clusters and performances at a level that far exceeds its next nearest competitor.

SPU Optimizations - Part 2

The document describes optimizing a lighting calculation for the SPU by analyzing memory requirements, partitioning data, and rearranging data for a streaming model. It then provides an example of optimizing a lighting calculation function, including vectorizing the calculation by hand to process 4 vertices simultaneously. The optimizations reduced the calculation time from 231.6 cycles per vertex per light to 208.5 cycles through compiler hints and further to an estimated higher performance by manual vectorization.

Pytorch meetup

Title: "Understanding PyTorch: PyTorch in Image Processing". Github: https://github.com/azarnyx/PyData_Meetup. The Dataset: https://goo.gl/CWmLWD.
The talk was given in PyData Meetup which took place in Munich on 06.03.2019 in Data Reply office. The talk was given by Dmitrii Azarnykh, data scientist in Data Reply.

Introduction to PyTorch

Basic concept of Deep Learning with explaining its structure and backpropagation method and understanding autograd in PyTorch. (+ Data parallism in PyTorch)

Early Results of OpenMP 4.5 Portability on NVIDIA GPUs & CPUs

This talk was presented at the DOE Centers of Excellence Performance Portability Workshop in August 2017. In this talk I explore the current status of 4 OpenMP 4.5 compilers for NVIDIA GPUs and CPUs from the perspective of performance portability between compilers and between the GPU and CPU.

04 Multi-layer Feedforward Networks

This document provides an outline for a course on neural networks and fuzzy systems. The course is divided into two parts, with the first 11 weeks covering neural networks topics like multi-layer feedforward networks, backpropagation, and gradient descent. The document explains that multi-layer networks are needed to solve nonlinear problems by dividing the problem space into smaller linear regions. It also provides notation for multi-layer networks and shows how backpropagation works to calculate weight updates for each layer.

Efficient Hill Climber for Multi-Objective Pseudo-Boolean Optimization

1) The document proposes an efficient hill climber algorithm for multi-objective pseudo-boolean optimization problems.
2) It computes scores that represent the change in fitness from moving to neighboring solutions, and updates these scores incrementally as the solution moves rather than recomputing from scratch.
3) The scores can be decomposed and updated in constant time by analyzing the variable interaction graph to identify variables that do not interact.

Learning Deep Learning

This document provides an overview of deep learning concepts including neural networks, supervised learning, perceptrons, logistic regression, feature transformation, feedforward neural networks, activation functions, loss functions, and gradient descent. It explains how neural networks can learn representations through hidden layers and how different activation functions, loss functions, and tasks relate. It also shows examples of calculating the gradient of the loss with respect to weights and biases for logistic regression.

Dafunctor

DAFunctor is a symbolic translator that converts NumPy/PyTorch ND-Array operations to equivalent C code. It works by decomposing generative NumPy functions into common properties like value expressions, index expressions, and scatter expressions. This allows DAFunctor to merge operations and generate loop-based C code without dynamic memory allocation or intermediate buffers.

Time complexity.ppt

This document discusses algorithm analysis and complexity. It defines key terms like algorithm, asymptotic complexity, Big-O notation, and time complexity. It provides examples of analyzing simple algorithms like summing array elements. The running time is expressed as a function of input size n. Common complexities like constant, linear, quadratic, and exponential time are introduced. Nested loops and sequences of statements are analyzed. The goal of analysis is to classify algorithms into complexity classes to understand how input size affects runtime.

how to calclute time complexity of algortihm

This document discusses algorithm analysis and complexity. It defines key terms like asymptotic complexity, Big-O notation, and time complexity. It provides examples of analyzing simple algorithms like a sum function to determine their time complexity. Common analyses include looking at loops, nested loops, and sequences of statements. The goal is to classify algorithms according to their complexity, which is important for large inputs and machine-independent. Algorithms are classified based on worst, average, and best case analyses.

DFA minimization algorithms in map reduce

This document summarizes the key aspects of the author's master's thesis which proposes algorithms for minimizing deterministic finite automata (DFAs) in the Map-Reduce framework. It introduces DFA minimization and existing sequential algorithms. It then presents related work on parallel DFA minimization and proposes new Map-Reduce algorithms based on Moore's and Hopcroft's sequential algorithms. Finally, it analyzes the communication and computational costs of the proposed parallel algorithms.

lecture01_lecture01_lecture0001_ceva.pdf

The document provides an overview and outline of the course "Optimization for Machine Learning". Key points:
- The course covers topics like convexity, gradient methods, constrained optimization, proximal algorithms, stochastic gradient descent, and more.
- Mathematical modeling and computational optimization for machine learning are discussed. Optimization algorithms like gradient descent and stochastic gradient descent are important for learning model parameters.
- Convex optimization problems have desirable properties like every local minimum being a global minimum. Gradient descent and related algorithms are guaranteed to converge for convex problems.
- Convex sets and functions are introduced, including characterizations using epigraphs and subgradients. Convex functions have useful properties like continuity and satisfying Jensen's inequality.

Using CNTK's Python Interface for Deep LearningDave DeBarr -

The document provides an overview of using CNTK (Cognitive Toolkit) for deep learning in Python. It discusses topics like machine learning, deep learning, neural networks, gradient descent, and examples using logistic regression and multi-layer perceptrons. It also covers installing CNTK and related tools on Azure virtual machines to access GPUs for faster computation. Key steps outlined are downloading required software, configuring the Nvidia driver, running examples notebooks, and the basic principles of backpropagation for training neural networks.

Algorithm Design and Complexity - Course 11

This document discusses algorithms for solving the all-pairs shortest paths (APSP) problem. It begins by defining the problem and describing the input as a weighted graph. It then discusses several approaches to solving APSP, including using single-source shortest path algorithms repeatedly, and specialized dynamic programming algorithms like Floyd-Warshall and Johnson's algorithm. Floyd-Warshall finds shortest paths by considering intermediate vertices between pairs of vertices. It works in O(n3) time and space for a graph with n vertices. Johnson's algorithm improves the running time for sparse graphs with negative weights.

20181212 - PGconfASIA - LT - English

PL/CUDA allows writing user-defined functions in CUDA C that can run on a GPU. This provides benefits for analytics workloads that can utilize thousands of GPU cores and wide memory bandwidth. A sample logistic regression implementation in PL/CUDA showed a 350x speedup compared to a CPU-based implementation in MADLib. Logistic regression performs binary classification by estimating weights for explanatory variables and intercept through iterative updates. This is well-suited to parallelization on a GPU.

parallel

This document presents an implementation and analysis of parallelizing the training of a multilayer perceptron neural network. It describes distributing the calculations across processor nodes by assigning each processor responsibility for a fraction of nodes in each layer. Theoretical speedups of 1-16x are estimated and experimental speedups of 2-10x are observed for networks with over 60,000 nodes trained on up to 16 processors. Node parallelization provides near linear speedup for training multilayer perceptrons.

Scala as a Declarative Language

The document discusses the benefits of declarative programming using Scala. It provides examples of implementing algorithms and data structures declaratively in Scala. It also discusses the history and future of Scala, as well as how Scala encourages thinking about programs as transformations rather than changes to memory.

Design and Implementation of Parallel and Randomized Approximation Algorithms

This document summarizes the design and implementation of parallel and randomized approximation algorithms for solving matrix games, linear programs, and semi-definite programs. It presents solvers for these problems that provide approximate solutions in sublinear or near-linear time. It analyzes the performance and precision-time tradeoffs of the solvers compared to other algorithms. It also provides examples of applying the SDP solver to approximate the Lovasz theta function.

Spark 4th Meetup Londond - Building a Product with Spark

This document discusses common technical problems encountered when building products with Spark and provides solutions. It covers Spark exceptions like out of memory errors and shuffle file problems. It recommends increasing partitions and memory configurations. The document also discusses optimizing Spark code using functional programming principles like strong and weak pipelining, and leveraging monoid structures to reduce shuffling. Overall it provides tips to debug issues, optimize performance, and productize Spark applications.

Alpine Spark Implementation - Technical

Alpine Spark Implementation - Technical

SPU Optimizations - Part 2

SPU Optimizations - Part 2

Pytorch meetup

Pytorch meetup

Introduction to PyTorch

Introduction to PyTorch

Early Results of OpenMP 4.5 Portability on NVIDIA GPUs & CPUs

Early Results of OpenMP 4.5 Portability on NVIDIA GPUs & CPUs

04 Multi-layer Feedforward Networks

04 Multi-layer Feedforward Networks

Efficient Hill Climber for Multi-Objective Pseudo-Boolean Optimization

Efficient Hill Climber for Multi-Objective Pseudo-Boolean Optimization

Learning Deep Learning

Learning Deep Learning

Dafunctor

Dafunctor

Time complexity.ppt

Time complexity.ppt

how to calclute time complexity of algortihm

how to calclute time complexity of algortihm

DFA minimization algorithms in map reduce

DFA minimization algorithms in map reduce

lecture01_lecture01_lecture0001_ceva.pdf

lecture01_lecture01_lecture0001_ceva.pdf

Using CNTK's Python Interface for Deep LearningDave DeBarr -

Using CNTK's Python Interface for Deep LearningDave DeBarr -

Algorithm Design and Complexity - Course 11

Algorithm Design and Complexity - Course 11

20181212 - PGconfASIA - LT - English

20181212 - PGconfASIA - LT - English

parallel

parallel

Scala as a Declarative Language

Scala as a Declarative Language

Design and Implementation of Parallel and Randomized Approximation Algorithms

Design and Implementation of Parallel and Randomized Approximation Algorithms

Spark 4th Meetup Londond - Building a Product with Spark

Spark 4th Meetup Londond - Building a Product with Spark

2017 Netflix's Recommendation ML Pipeline Using Apache Spark: Spark Summit Ea...

1) Netflix uses machine learning algorithms driven by Apache Spark to power over 80% of recommendations to its members.
2) Netflix runs A/B tests on recommendation models by first evaluating them offline on historical data and then deploying the best-performing models in live experiments.
3) Netflix's recommendation pipeline involves feature engineering on a standardized data format, training models both locally and in distributed mode, and scoring and ranking items to produce recommendations.

2015 01-17 Lambda Architecture with Apache Spark, NextML Conference

Lambda architecture is a data-processing architecture designed to handle massive quantities of data by taking advantage of both batch- and stream-processing methods. In Lambda architecture, the system involves three layers: batch processing, speed (or real-time) processing, and a serving layer for responding to queries, and each comes with its own set of requirements.
In batch layer, it aims at perfect accuracy by being able to process the all available big dataset which is an immutable, append-only set of raw data using distributed processing system. Output will be typically stored in a read-only database with result completely replacing existing precomputed views. Apache Hadoop, Pig, and HIVE are
the de facto batch-processing system.
In speed layer, the data is processed in streaming fashion, and the real-time views are provided by the most recent data. As a result, the speed layer is responsible for filling the "gap" caused by the batch layer's lag in providing views based on the most recent data. This layer's views may not be as accurate as the views provided by batch layer's views created with full dataset, so they will be eventually replaced by the batch layer's views. Traditionally, Apache Storm is
used in this layer.
In serving layer, the result from batch layer and speed layer will be stored here, and it responds to queries in a low-latency and ad-hoc way.
One of the lambda architecture examples in machine learning context is building the fraud detection system. In speed layer, the incoming streaming data can be used for online learning to update the model learnt in batch layer to incorporate the recent events. After a while, the model can be rebuilt using the full dataset.
Why Spark for lambda architecture? Traditionally, different
technologies are used in batch layer and speed layer. If your batch system is implemented with Apache Pig, and your speed layer is implemented with Apache Storm, you have to write and maintain the same logics in SQL and in Java/Scala. This will very quickly becomes a maintenance nightmare. With Spark, we have an unified development framework for batch and speed layer at scale. In this talk, an end-to-end example implemented in Spark will be shown, and we will
discuss about the development, testing, maintenance, and deployment of lambda architecture system with Apache Spark.

2014-10-20 Large-Scale Machine Learning with Apache Spark at Internet of Thin...

This document discusses machine learning techniques for large-scale datasets using Apache Spark. It provides an overview of Spark's machine learning library (MLlib), describing algorithms like logistic regression, linear regression, collaborative filtering, and clustering. It also compares Spark to traditional Hadoop MapReduce, highlighting how Spark leverages caching and iterative algorithms to enable faster machine learning model training.

2014-08-14 Alpine Innovation to Spark

Spark is rapidly catching fire with the machine learning and data science community for a number of reasons. Predominantly, it is making it possible to extend and enhance machine learning algorithms to a level we’ve never seen before. In this talk, we’ll give examples of two areas Alpine Data Labs has contributed to the Spark project:
Bio:
DB Tsai is a Machine Learning Engineer working at Alpine Data Labs. His current focus is on Big Data, Data Mining, and Machine Learning. He uses Hadoop, Spark, Mahout, and several Machine Learning algorithms to build powerful, scalable, and robust cloud-driven applications. His favorite programming languages are Java, Scala, and Python. DB is a Ph.D. candidate in Applied Physics at Stanford University (currently taking leave of absence). He holds a Master’s degree in Electrical Engineering from Stanford University, as well as a Master's degree in Physics from National Taiwan University.

Large-Scale Machine Learning with Apache Spark

Spark is a new cluster computing engine that is rapidly gaining popularity — with over 150 contributors in the past year, it is one of the most active open source projects in big data, surpassing even Hadoop MapReduce. Spark was designed to both make traditional MapReduce programming easier and to support new types of applications, with one of the earliest focus areas being machine learning. In this talk, we’ll introduce Spark and show how to use it to build fast, end-to-end machine learning workflows. Using Spark’s high-level API, we can process raw data with familiar libraries in Java, Scala or Python (e.g. NumPy) to extract the features for machine learning. Then, using MLlib, its built-in machine learning library, we can run scalable versions of popular algorithms. We’ll also cover upcoming development work including new built-in algorithms and R bindings.
Bio:
Xiangrui Meng is a software engineer at Databricks. He has been actively involved in the development of Spark MLlib since he joined. Before Databricks, he worked as an applied research engineer at LinkedIn, where he was the main developer of an offline machine learning framework in Hadoop MapReduce. His thesis work at Stanford is on randomized algorithms for large-scale linear regression.

Unsupervised Learning with Apache Spark

Unsupervised learning refers to a branch of algorithms that try to find structure in unlabeled data. Clustering algorithms, for example, try to partition elements of a dataset into related groups. Dimensionality reduction algorithms search for a simpler representation of a dataset. Spark's MLLib module contains implementations of several unsupervised learning algorithms that scale to huge datasets. In this talk, we'll dive into uses and implementations of Spark's K-means clustering and Singular Value Decomposition (SVD).
Bio:
Sandy Ryza is an engineer on the data science team at Cloudera. He is a committer on Apache Hadoop and recently led Cloudera's Apache Spark development.

2017 Netflix's Recommendation ML Pipeline Using Apache Spark: Spark Summit Ea...

2017 Netflix's Recommendation ML Pipeline Using Apache Spark: Spark Summit Ea...

2015 01-17 Lambda Architecture with Apache Spark, NextML Conference

2015 01-17 Lambda Architecture with Apache Spark, NextML Conference

2014-10-20 Large-Scale Machine Learning with Apache Spark at Internet of Thin...

2014-10-20 Large-Scale Machine Learning with Apache Spark at Internet of Thin...

2014-08-14 Alpine Innovation to Spark

2014-08-14 Alpine Innovation to Spark

Large-Scale Machine Learning with Apache Spark

Large-Scale Machine Learning with Apache Spark

Unsupervised Learning with Apache Spark

Unsupervised Learning with Apache Spark

一比一原版(CalArts毕业证)加利福尼亚艺术学院毕业证如何办理

CalArts毕业证学历书【微信95270640】CalArts毕业证’圣力嘉学院毕业证《Q微信95270640》办理CalArts毕业证√文凭学历制作{CalArts文凭}购买学历学位证书本科硕士,CalArts毕业证学历学位证【实体公司】办毕业证、成绩单、学历认证、学位证、文凭认证、办留信网认证、（网上可查，实体公司，专业可靠）
(诚招代理)办理国外高校毕业证成绩单文凭学位证,真实使馆公证（留学回国人员证明）真实留信网认证国外学历学位认证雅思代考国外学校代申请名校保录开请假条改GPA改成绩ID卡
1.高仿业务:【本科硕士】毕业证,成绩单（GPA修改）,学历认证（教育部认证）,大学Offer,,ID,留信认证,使馆认证,雅思,语言证书等高仿类证书；
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1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
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6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
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-全套服务：毕业证成绩单真实使馆公证真实教育部认证。让您回国发展信心十足！
（详情请加一下 文凭顾问+微信:95270640）欢迎咨询！子小伍玩小伍比山娃小一岁虎头虎脑的很霸气父亲让山娃跟小伍去夏令营听课山娃很高兴夏令营就设在附近一所小学山娃发现那所小学比自己的学校更大更美操场上还铺有塑胶跑道呢里面很多小朋友一班一班的快快乐乐原来城里娃都藏这儿来了怪不得平时见不到他们山娃恍然大悟起来吹拉弹唱琴棋书画山娃都不懂却什么都想学山娃怨自己太笨什么都不会斟酌再三山娃终于选定了学美术当听说每月要交元时父亲犹豫了山娃也说爸算了吧咱学校一学期才转

Call For Paper -3rd International Conference on Artificial Intelligence Advan...

* Registration is currently open *
Call for Research Papers!!!
Free – Extended Paper will be published as free of cost.
3rd International Conference on Artificial Intelligence Advances (AIAD 2024)
July 13 ~ 14, 2024, Virtual Conference
Webpage URL: https://aiad2024.org/index
Submission Deadline: June 22, 2024
Submission System URL:
https://aiad2024.org/submission/index.php
Contact Us:
Here's where you can reach us : aiad@aiad2024.org (or) aiadconference@yahoo.com
WikiCFP URL: http://wikicfp.com/cfp/servlet/event.showcfp?eventid=180509©ownerid=171656
#artificialintelligence #softcomputing #machinelearning #technology #datascience #python #deeplearning #tech #robotics #innovation #bigdata #coding #iot #computerscience #data #dataanalytics #engineering #robot #datascientist #software #automation #analytics #ml #pythonprogramming #programmer #digitaltransformation #developer #promptengineering #generativeai #genai #chatgpt #artificial #intelligence #datamining #networkscommunications #robotics #callforsubmission #submissionsopen #deadline #opencall #virtual #conference

smart pill dispenser is designed to improve medication adherence and safety f...

Smart Pill Dispenser that boosts medication adherence, empowers patients, enables remote monitoring, enhances safety, reduces healthcare costs, and contributes to data-driven healthcare improvements

Supermarket Management System Project Report.pdf

Supermarket management is a stand-alone J2EE using Eclipse Juno program.
This project contains all the necessary required information about maintaining
the supermarket billing system.
The core idea of this project to minimize the paper work and centralize the
data. Here all the communication is taken in secure manner. That is, in this
application the information will be stored in client itself. For further security the
data base is stored in the back-end oracle and so no intruders can access it.

Introduction to verilog basic modeling .ppt

Digital CKT

Computational Engineering IITH Presentation

This Presentation will give you a brief idea about what Computational Engineering at IIT Hyderabad has to offer.

Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024

Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.

An Introduction to the Compiler Designss

compiler material

IEEE Aerospace and Electronic Systems Society as a Graduate Student Member

IEEE Aerospace and Electronic Systems Society as a Graduate Student Member

ITSM Integration with MuleSoft.pptx

ITSM Integration with mulesoft

AI + Data Community Tour - Build the Next Generation of Apps with the Einstei...

AI + Data Community Tour - Build the Next Generation of Apps with the Einstei...Paris Salesforce Developer Group

Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.Data Driven Maintenance | UReason Webinar

Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.

原版制作(Humboldt毕业证书)柏林大学毕业证学位证一模一样

原件一模一样【微信：bwp0011】《(Humboldt毕业证书)柏林大学毕业证学位证》【微信：bwp0011】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问微bwp0011
【主营项目】
一.毕业证【微bwp0011】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【微bwp0011】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才

Design and optimization of ion propulsion drone

Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.

Software Engineering and Project Management - Software Testing + Agile Method...

Software Testing: A Strategic Approach to Software Testing, Strategic Issues, Test Strategies for Conventional Software, Test Strategies for Object -Oriented Software, Validation Testing, System Testing, The Art of Debugging.
Agile Methodology: Before Agile – Waterfall, Agile Development.

一比一原版(uofo毕业证书)美国俄勒冈大学毕业证如何办理

原版一模一样【微信：741003700 】【(uofo毕业证书)美国俄勒冈大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。

Object Oriented Analysis and Design - OOAD

This ppt gives detailed description of Object Oriented Analysis and design.

SCALING OF MOS CIRCUITS m .pptx

this ppt explains about scaling parameters of the mosfet it is basically vlsi subject

DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL

As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.

一比一原版(CalArts毕业证)加利福尼亚艺术学院毕业证如何办理

一比一原版(CalArts毕业证)加利福尼亚艺术学院毕业证如何办理

Call For Paper -3rd International Conference on Artificial Intelligence Advan...

Call For Paper -3rd International Conference on Artificial Intelligence Advan...

smart pill dispenser is designed to improve medication adherence and safety f...

smart pill dispenser is designed to improve medication adherence and safety f...

Supermarket Management System Project Report.pdf

Supermarket Management System Project Report.pdf

1FIDIC-CONSTRUCTION-CONTRACT-2ND-ED-2017-RED-BOOK.pdf

1FIDIC-CONSTRUCTION-CONTRACT-2ND-ED-2017-RED-BOOK.pdf

Introduction to verilog basic modeling .ppt

Introduction to verilog basic modeling .ppt

Computational Engineering IITH Presentation

Computational Engineering IITH Presentation

Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024

Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024

An Introduction to the Compiler Designss

An Introduction to the Compiler Designss

IEEE Aerospace and Electronic Systems Society as a Graduate Student Member

IEEE Aerospace and Electronic Systems Society as a Graduate Student Member

ITSM Integration with MuleSoft.pptx

ITSM Integration with MuleSoft.pptx

AI + Data Community Tour - Build the Next Generation of Apps with the Einstei...

AI + Data Community Tour - Build the Next Generation of Apps with the Einstei...

Data Driven Maintenance | UReason Webinar

Data Driven Maintenance | UReason Webinar

原版制作(Humboldt毕业证书)柏林大学毕业证学位证一模一样

原版制作(Humboldt毕业证书)柏林大学毕业证学位证一模一样

Design and optimization of ion propulsion drone

Design and optimization of ion propulsion drone

Software Engineering and Project Management - Software Testing + Agile Method...

Software Engineering and Project Management - Software Testing + Agile Method...

一比一原版(uofo毕业证书)美国俄勒冈大学毕业证如何办理

一比一原版(uofo毕业证书)美国俄勒冈大学毕业证如何办理

Object Oriented Analysis and Design - OOAD

Object Oriented Analysis and Design - OOAD

SCALING OF MOS CIRCUITS m .pptx

SCALING OF MOS CIRCUITS m .pptx

DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL

DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL

- 1. Multinomial Logistic Regression with Apache Spark DB Tsai dbtsai@alpinenow.com Machine Learning Engineer June 20th , 2014 Silicon Valley Machine Learning Meetup
- 2. What is Alpine Data Labs doing? ● Collaborative, Code-Free, Advanced Analytics Solution for Big Data
- 3. ● Technology we're using: Scala/Java, Akka, Spray, Hadoop, Spark/SparkSQL, Pig, Sqoop ● Our platform runs against different flavors of Hadoop distributions such as Cloudera, Pivotal Hadoop, MapR, HortonWorks and Apache. ● Actively involved in the open source community: almost of all our newly developed algorithms in Spark will be contributed back to MLLib. ● Already committed a L-BFGS optimizer to Spark, and helped fix couple bugs. Working on multinational logistic regression, GLM, Decision Tree and a Random Forest ● In addition we are the maintainer of several open source projects including Chorus, SBT plugin for JUnit test Listener and several other projects. We're open source friendly!
- 4. We're hiring! ● Machine Learning Engineer ● Data Scientist ● UI/UX Engineer ● Front-end Engineer ● Infrastructure Engineer ● Back-end Engineer ● Automation Test Engineer Shoot me an email at dbtsai@alpinenow.com
- 5. Machine Learning with Big Data ● Hadoop MapReduce solutions ● MapReduce is scaling well for batch processing ● Lots of machine learning algorithms are iterative by nature. ● There are lots of tricks people do, like training with subsamples of data, and then average the models. Why big data with approximation? + =
- 6. Lightning-fast cluster computing ● Empower users to iterate through the data by utilizing the in-memory cache. ● Logistic regression runs up to 100x faster than Hadoop M/R in memory. ● We're able to train exact model without doing any approximation.
- 7. Binary Logistic Regression d = ax1+bx2+cx0 √a2 +b2 where x0=1 P( y=1∣⃗x , ⃗w)= exp(d ) 1+exp(d ) = exp(⃗x ⃗w) 1+exp(⃗x ⃗w) P ( y=0∣⃗x , ⃗w)= 1 1+exp(⃗x ⃗w) log P ( y=1∣⃗x , ⃗w) P( y=0∣⃗x , ⃗w) =⃗x ⃗w w0= c √a2 +b2 w1= a √a2 +b2 w2= b √a2 +b2 where w0 iscalled as intercept
- 8. Training Binary Logistic Regression ● Maximum Likelihood estimation From a training data and labels ● We want to find that maximizes the likelihood of data defined by ● We can take log of the equation, and minimize it instead. The Log-Likelihood becomes the loss function. X =( ⃗x1 , ⃗x2 , ⃗x3 ,...) Y =( y1, y2, y3, ...) ⃗w L( ⃗w , ⃗x1, ... , ⃗xN )=P ( y1∣⃗x1 , ⃗w)P ( y2∣⃗x2 , ⃗w)... P( yN∣ ⃗xN , ⃗w) l( ⃗w ,⃗x)=log P(y1∣⃗x1 , ⃗w)+log P( y2∣⃗x2 , ⃗w)...+log P( yN∣ ⃗xN , ⃗w)
- 9. Optimization ● First Order Minimizer Require loss, gradient of loss function – Gradient Decent is step size – Limited-memory BFGS (L-BFGS) – Orthant-Wise Limited-memory Quasi-Newton (OWLQN) – Coordinate Descent (CD) – Trust Region Newton Method (TRON) ● Second Order Minimizer Require loss, gradient and hessian of loss function – Newton-Raphson, quadratic convergence. Fast! ● Ref: Journal of Machine Learning Research 11 (2010) 3183- 3234, Chih-Jen Lin et al. ⃗wn+1=⃗wn−γ ⃗G , γ ⃗wn+1=⃗wn−H−1 ⃗G
- 10. Problem of Second Order Minimizer ● Scale horizontally (the numbers of training data) by leveraging Spark to parallelize this iterative optimization process. ● Don't scale vertically (the numbers of training features). Dimension of Hessian is ● Recent applications from document classification and computational linguistics are of this type. dim(H )=[(k−1)(n+1)] 2 where k is numof class ,nis num of features
- 11. L-BFGS ● It's a quasi-Newton method. ● Hessian matrix of second derivatives doesn't need to be evaluated directly. ● Hessian matrix is approximated using gradient evaluations. ● It converges a way faster than the default optimizer in Spark, Gradient Decent. ● We love open source! Alpine Data Labs contributed our L-BFGS to Spark, and it's already merged in Spark-1157.
- 12. Training Binary Logistic Regression l( ⃗w ,⃗x) = ∑ k=1 N log P( yk∣⃗xk , ⃗w) = ∑k=1 N yk log P ( yk=1∣⃗xk , ⃗w)+(1−yk )log P ( yk =0∣⃗xk , ⃗w) = ∑k=1 N yk log exp( ⃗xk ⃗w) 1+exp( ⃗xk ⃗w) +(1−yk )log 1 1+exp( ⃗xk ⃗w) = ∑k=1 N yk ⃗xk ⃗w−log(1+exp( ⃗xk ⃗w)) Gradient : Gi (⃗w ,⃗x)= ∂l( ⃗w ,⃗x) ∂ wi =∑k=1 N yk xki− exp( ⃗xk ⃗w) 1+exp( ⃗xk ⃗w) xki Hessian: H ij (⃗w ,⃗x)= ∂∂l( ⃗w ,⃗x) ∂wi ∂w j =−∑k=1 N exp( ⃗xk ⃗w) (1+exp( ⃗xk ⃗w))2 xki xkj
- 13. Overfitting P ( y=1∣⃗x , ⃗w)= exp(zd ) 1+exp(zd ) = exp(⃗x ⃗w) 1+exp(⃗x ⃗w)
- 14. Regularization ● The loss function becomes ● The loss function of regularizer doesn't depend on data. Common regularizers are – L2 Regularization: – L1 Regularization: ● L1 norm is not differentiable at zero! ltotal (⃗w ,⃗x)=lmodel (⃗w ,⃗x)+lreg ( ⃗w) lreg (⃗w)=λ∑i=1 N wi 2 lreg (⃗w)=λ∑i=1 N ∣wi∣ ⃗G (⃗w ,⃗x)total=⃗G(⃗w ,⃗x)model+⃗G( ⃗w)reg ̄H ( ⃗w ,⃗x)total= ̄H (⃗w ,⃗x)model+ ̄H (⃗w)reg
- 15. Mini School of Spark APIs ● map(func) : Return a new distributed dataset formed by passing each element of the source through a function func. ● reduce(func) : Aggregate the elements of the dataset using a function func (which takes two arguments and returns one). The function should be commutative and associative so that it can be computed correctly in parallel. This is O(n) operation where n is number of partition. In SPARK-2174, treeReduce will be merged for O(log(n)) operation.
- 16. Example – compute the mean of numbers. 3.0 2.0 1.0 5.0 Executor 1 Executor 2 map (3.0, 1) (2.0, 1) reduce (5.0, 2) reduce (11.0, 4) Shuffle reduce (6.0, 2) (1.0, 1) (5.0, 1) map Executor 3
- 17. Mini School of Spark APIs ● mapPartitions(func) : Similar to map, but runs separately on each partition (block) of the RDD, so func must be of type Iterator[T] => Iterator[U] when running on an RDD of type T. ● This API allows us to have global variables on entire partition to aggregate the result locally and efficiently.
- 18. Better Mean of Numbers Implementation 3.0 2.0 Executor 1 reduce (11.0, 4) Shuffle var counts = 0 var sum = 0.0 loop var counts = 2 var sum = 5.0 3.0 2.0 (5.0, 2) 1.0 5.0 Executor 2 var counts = 0 var sum = 0.0 loop var counts = 2 var sum = 6.0 1.0 5.0 (6.0, 2) mapPartitions
- 19. More Idiomatic Scala Implementation ● aggregate(zeroValue: U)(seqOp: (U, T) => U, combOp: (U, U) => U): U zeroValue is a neutral "zero value" with type U for initialization. This is analogous to in previous example. var counts = 0 var sum = 0.0 seqOp is function taking (U, T) => U, where U is aggregator initialized as zeroValue. T is each line of values in RDD. This is analogous to mapPartition in previous example. combOp is function taking (U, U) => U. This is essentially combing the results between different executors. The functionality is the same as reduce(func) in previous example.
- 20. Approach of Parallelization in Spark Spark Driver JVM Time Spark Executor 1 JVM Spark Executor 2 JVM 1) Find available resources in cluster, and launch executor JVMs. Also initialize the weights. 2) Ask executors to load the data into executors' JVMs 2) Trying to load the data into memory. If the data is bigger than memory, it will be partial cached. (The data locality from source will be taken care by Spark) 3) Ask executors to compute loss, and gradient of each training sample (each row) given the current weights. Get the aggregated results after the Reduce Phase in executors. 5) If the regularization is enabled, compute the loss and gradient of regularizer in driver since it doesn't depend on training data but only depends on weights. Add them into the results from executors. 3) Map Phase: Compute the loss and gradient of each row of training data locally given the weights obtained from the driver. Can either emit each result or sum them up in local aggregators. 4) Reduce Phase: Sum up the losses and gradients emitted from the Map Phase Taking a rest! 6) Plug the loss and gradient from model and regularizer into optimizer to get the new weights. If the differences of weights and losses are larger than criteria, GO BACK TO 3) Taking a rest! 7) Finish the model training! Taking a rest!
- 21. Step 3) and 4) ● This is the implementation of step 3) and 4) in MLlib before Spark 1.0 ● gradient can have implementations of Logistic Regression, Linear Regression, SVM, or any customized cost function. ● Each training data will create new “grad” object after gradient.compute.
- 22. Step 3) and 4) with mapPartitions
- 23. Step 3) and 4) with aggregate ● This is the implementation of step 3) and 4) in MLlib in coming Spark 1.0 ● No unnecessary object creation! It's helpful when we're dealing with large features training data. GC will not kick in the executor JVMs.
- 24. Extension to Multinomial Logistic Regression ● In Binary Logistic Regression ● For K classes multinomial problem where labels ranged from [0, K-1], we can generalize it via ● The model, weights becomes (K-1)(N+1) matrix, where N is number of features. log P( y=1∣⃗x , ̄w) P ( y=0∣⃗x , ̄w) =⃗x ⃗w1 log P ( y=2∣⃗x , ̄w) P ( y=0∣⃗x , ̄w) =⃗x ⃗w2 ... log P ( y=K −1∣⃗x , ̄w) P ( y=0∣⃗x , ̄w) =⃗x ⃗wK −1 log P( y=1∣⃗x , ⃗w) P ( y=0∣⃗x , ⃗w) =⃗x ⃗w ̄w=(⃗w1, ⃗w2, ... , ⃗wK −1)T P( y=0∣⃗x , ̄w)= 1 1+∑ i=1 K −1 exp(⃗x ⃗wi ) P( y=1∣⃗x , ̄w)= exp(⃗x ⃗w2) 1+∑ i=1 K −1 exp(⃗x ⃗wi) ... P( y=K −1∣⃗x , ̄w)= exp(⃗x ⃗wK −1) 1+∑i=1 K −1 exp(⃗x ⃗wi )
- 25. Training Multinomial Logistic Regression l( ̄w ,⃗x) = ∑ k=1 N log P( yk∣⃗xk , ̄w) = ∑k=1 N α( yk)log P( y=0∣⃗xk , ̄w)+(1−α( yk ))log P ( yk∣⃗xk , ̄w) = ∑k=1 N α( yk)log 1 1+∑ i=1 K −1 exp(⃗x ⃗wi) +(1−α( yk ))log exp(⃗x ⃗w yk ) 1+∑ i=1 K−1 exp(⃗x ⃗wi) = ∑ k=1 N (1−α( yk ))⃗x ⃗wyk −log(1+∑ i=1 K−1 exp(⃗x ⃗wi)) Gradient : Gij ( ̄w ,⃗x)= ∂l( ̄w ,⃗x) ∂wij =∑k=1 N (1−α( yk )) xkj δi , yk − exp( ⃗xk ⃗w) 1+exp( ⃗xk ⃗w) xkj α( yk )=1 if yk=0 α( yk )=0 if yk≠0 Define: Note that the first index “i” is for classes, and the second index “j” is for features. Hessian: H ij ,lm( ̄w ,⃗x)= ∂∂l ( ̄w ,⃗x) ∂wij ∂wlm
- 26. 0 5 10 15 20 25 30 35 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 Logistic Regression with a9a Dataset (11M rows, 123 features, 11% non-zero elements) 16 executors in INTEL Xeon E3-1230v3 32GB Memory * 5 nodes Hadoop 2.0.5 alpha cluster L-BFGS Dense Features L-BFGS Sparse Features GD Sparse Features GD Dense Features Seconds Log-Likelihood/NumberofSamples a9a Dataset Benchmark
- 27. a9a Dataset Benchmark -1 1 3 5 7 9 11 13 15 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 Logistic Regression with a9a Dataset (11M rows, 123 features, 11% non-zero elements) 16 executors in INTEL Xeon E3-1230v3 32GB Memory * 5 nodes Hadoop 2.0.5 alpha cluster L-BFGS GD Iterations Log-Likelihood/NumberofSamples
- 28. 0 5 10 15 20 25 30 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Logistic Regression with rcv1 Dataset (6.8M rows, 677,399 features, 0.15% non-zero elements) 16 executors in INTEL Xeon E3-1230v3 32GB Memory * 5 nodes Hadoop 2.0.5 alpha cluster LBFGS Sparse Vector GD Sparse Vector Second Log-Likelihood/NumberofSamples rcv1 Dataset Benchmark
- 29. news20 Dataset Benchmark 0 10 20 30 40 50 60 70 80 0 0.2 0.4 0.6 0.8 1 1.2 Logistic Regression with news20 Dataset (0.14M rows, 1,355,191 features, 0.034% non-zero elements) 16 executors in INTEL Xeon E3-1230v3 32GB Memory * 5 nodes Hadoop 2.0.5 alpha cluster LBFGS Sparse Vector GD Sparse Vector Second Log-Likelihood/NumberofSamples
- 30. Alpine Demo
- 31. Alpine Demo
- 32. Alpine Demo
- 33. Conclusion ● We're hiring! ● Spark runs programs 100x faster than Hadoop ● Spark turns iterative big data machine learning problems into single machine problems ● MLlib provides lots of state of art machine learning implementations, e.g. K-means, linear regression, logistic regression, ALS, collaborative filtering, and Naive Bayes, etc. ● Spark provides ease of use APIs in Java, Scala, or Python, and interactive Scala and Python shell ● Spark is Apache project, and it's the most active big data open source project nowadays.