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This document discusses deep learning and inductive programming. It begins by defining deep learning as a stateless function that can take in high-dimensional or categorical variables as input and provide low-dimensional outputs for classification or high-dimensional outputs for generation. The document then provides an example of converting Celsius to Fahrenheit using a simple formula. It contrasts this with an inductive, data-driven approach requiring no prior knowledge of the model or algorithm. The document suggests neural networks can approximate any high-dimensional function, acting as a universal computing mechanism. It speculates that by 2020, over half of newly developed software will have inductively trained components, representing a large paradigm shift. Finally, it discusses how new engineering disciplines are needed as new

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Analysis of algo

The document discusses analyzing the efficiency of algorithms. It notes that algorithms can be correct but the best depends on efficiency. Efficiency depends on time and space complexity, where time complexity is the amount of time needed for complete execution and space complexity is the amount of memory required. Different algorithms have different efficiencies that can be analyzed based on how processing time and memory usage increase with larger input sizes.

Data Applied: Clustering

This document provides an introduction to clustering techniques and the BIRCH algorithm. It defines clustering as dividing data instances into natural groups rather than predicting classes. The BIRCH algorithm incrementally clusters multi-dimensional data to produce high quality clusters using minimal resources. It can handle large datasets by performing clustering in one data scan and allows for outliers. The algorithm builds a CF tree using clustering features to summarize cluster information during the incremental clustering process.

COLLEGE OF COMPUTING AND INFORMATICS Assignment – 4

This document provides instructions for an assignment with 4 questions. It involves using stacks, queues, sets, maps and calculating hashcodes of strings. Students need to submit their responses by April 28th, 2016 for a total of 5 marks. Late or copied submissions will receive zero marks. The questions cover identifying real life examples of stack and queue usage, basic set operations in Java, storing and manipulating course data in a map, and writing a method to calculate string hashcodes based on a given formula.

Matrix operations in MATLAB

This document provides an overview of basic MATLAB functions and commands for numerical analysis. It discusses MATLAB's command window and workspace, as well as how to perform scalar operations, define vectors and matrices, do elementary row transformations and matrix operations like addition, multiplication, and concatenation using commands like (), [], and *. It also discusses displaying output and suppressing it with semicolons, and formatting output with commands like format. The conclusion notes that matrix multiplication requires the number of columns in the first matrix to equal the number of rows in the second.

Matlab Files

This document describes a MATLAB script and function to calculate the determinant of a matrix without using the built-in det() function. It explains:
1) Converting the script into a function file that accepts the matrix and its dimension as inputs and returns the determinant.
2) The methodology uses cofactor expansion and calls the function recursively to compute the determinant.
3) The time complexity of the function is O(n!) due to the nested for loops and recursive calls.

INTRODUCTION TO DATA STRUCTURE - CS SIMPLE

This slide provides the introduction about the Data Structure. Before moving into DS, the concepts like Algorithm and Programming are discussed. In addition, concepts of Abstract Data Type ( ADT ) is also explained

Mcs 10 104 compiler design dec 2014

This document appears to be an exam for a Compiler Design course, covering four modules:
1) Compiler phases and intermediate representations
2) Data flow analysis techniques for determining variable properties
3) Control flow graph construction and dominance analysis
4) Optimization techniques including register allocation, instruction scheduling, common subexpression elimination, and interprocedural analysis
It contains eight questions testing knowledge of concepts from each module.

I. AO* SEARCH ALGORITHM

Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists

Review of basic data structures

This document discusses various data structures and their implementations in C++ using templates. It begins by reviewing basic linear data structures like lists, stacks, and queues. It then covers implementing these structures using array-based and linked representations in C++. Specifically, it describes how to implement lists, stacks, and queues using templates and both array and linked representations. It also compares the performance of array vs linked implementations.

Beginning direct3d gameprogrammingmath02_logarithm_20160324_jintaeks

This document discusses logarithms, exponentiation, and their relationship. It also covers binary search and quicksort algorithms. Logarithms are the inverse of exponentiation, where the logarithm of a number is the exponent that the base must be raised to to produce that number. Binary search and quicksort both have average time complexities of O(log n). Quicksort uses a divide and conquer approach to partition an array around a pivot element.

First Partial Review

Esta es una presentación con el repaso del primer parcial de Cálculo diferencial en el pizarrón electrónico Promethean.

Basic Traversal and Search Techniques

A graph search (or traversal) technique visits every node exactly one in a systematic fashion. Two standard graph search techniques have been widely used: Depth-First Search (DFS) Breadth-First Search (BFS)

Basic Traversal and Search Techniques

Connected components are subgraphs where any two vertices are connected by paths and disconnected from other graphs. The algorithm uses disjoint sets to determine if vertices are in the same component. Depth-first search (DFS) and breadth-first search (BFS) are common graph traversal algorithms. DFS uses a stack and visits the root node first before children. BFS uses a queue and visits nodes level-by-level starting from the root. Spanning trees connect all vertices without cycles, having n-1 edges for a graph with n vertices. Biconnected components are maximal subgraphs without articulation points whose removal would disconnect the graph.

Convolution

This experiment aims to write a Matlab program to perform convolution of two signals. Convolution relates the input, output, and impulse response of a linear time-invariant system. The Matlab code takes the length and values of two signals as input, performs convolution by multiplying and summing aligned values, and plots the resulting convolution signal. The experiment helps learn about convolution and how to implement it in Matlab code.

Algorithm lecture Dynamic programming

Dynamic programming is a strategy for designing algorithms that breaks problems down into recurring subproblems. It is useful when a problem can be solved by combining the solutions of its subproblems. The longest common subsequence problem finds the longest shared subsequence between two sequences and can be solved using dynamic programming by building up the solution from overlapping subproblems. Computing the nth Fibonacci number can also be solved with dynamic programming by storing and reusing previously computed values rather than recomputing them.

Fake coin problem(analysis)

This document discusses using the decrease and conquer technique and master's theorem to analyze the number of weighings needed in the worst case for the fake-coin problem. It defines W(n) as the number of weighings needed by the algorithm in the worst case and frames the analysis using the master's theorem formula of T(n) = aT(n/b) + f(n).

Alg II 2-7 Transformations

This document provides an overview of key concepts for graphing and understanding absolute value functions in Algebra II Chapter 2. It defines absolute value functions and their key features, including that the absolute value of f(x) gives the distance from the y-axis. Students will learn to graph absolute value functions by hand and using technology. The general form of an absolute value function is given as y = a|x - h| + k and examples are provided to show transformations from the standard form. Practice problems are assigned from the textbook.

Correlation

This document describes an experiment to write Matlab programs for cross correlation and auto correlation of signals. It provides theory on correlation, defines cross correlation and auto correlation, and includes Matlab code examples to calculate both. The code demonstrates taking input signals, performing the correlations, and plotting the output correlated signals. The overall goal is to learn how to implement correlation in Matlab.

And or graph problem reduction using predicate logic

The document discusses the AO* algorithm for solving problems represented as AND/OR graphs. It begins by explaining AND/OR graphs and how they can represent achieving subgoals simultaneously or independently. It then introduces the AO* algorithm, which extends A* search to AND/OR graphs by examining multiple nodes simultaneously. The algorithm is described in pseudocode and an example is provided. Finally, the document shows an example of generating a proof tree using forward and backward chaining on a set of logical statements and translating the statements into predicate logic.

Analysis of algo

Analysis of algo

Data Applied: Clustering

Data Applied: Clustering

COLLEGE OF COMPUTING AND INFORMATICS Assignment – 4

COLLEGE OF COMPUTING AND INFORMATICS Assignment – 4

Matrix operations in MATLAB

Matrix operations in MATLAB

Matlab Files

Matlab Files

INTRODUCTION TO DATA STRUCTURE - CS SIMPLE

INTRODUCTION TO DATA STRUCTURE - CS SIMPLE

Mcs 10 104 compiler design dec 2014

Mcs 10 104 compiler design dec 2014

I. AO* SEARCH ALGORITHM

I. AO* SEARCH ALGORITHM

Review of basic data structures

Review of basic data structures

Beginning direct3d gameprogrammingmath02_logarithm_20160324_jintaeks

Beginning direct3d gameprogrammingmath02_logarithm_20160324_jintaeks

First Partial Review

First Partial Review

Basic Traversal and Search Techniques

Basic Traversal and Search Techniques

Basic Traversal and Search Techniques

Basic Traversal and Search Techniques

Convolution

Convolution

Algorithm lecture Dynamic programming

Algorithm lecture Dynamic programming

Fake coin problem(analysis)

Fake coin problem(analysis)

Alg II 2-7 Transformations

Alg II 2-7 Transformations

Correlation

Correlation

And or graph problem reduction using predicate logic

And or graph problem reduction using predicate logic

Learning to Reconstruct

1) Machine learning techniques can be used to learn priors for solving inverse problems like image reconstruction from limited data.
2) Fully learned reconstruction is infeasible due to the large number of parameters needed. Learned post-processing and learned iterative reconstruction methods provide better results.
3) Learned iterative reconstruction formulates the problem as learning updating operators in an iterative optimization scheme, but is computationally challenging due to the need to differentiate through the whole solver. Future work includes methods to address this issue.

02. Data Types and variables

In this chapter we will get familiar with primitive types and variables in Java – what they are and how to work with them. First we will consider the data types – integer types, real types with floating-point, Boolean, character, string and object type. We will continue with the variables, with their characteristics, how to declare them, how they are assigned a value and what is variable initialization.

Dti2143 dam31303 lab sheet 5

The document outlines 5 exercises for a computer programming lab, including writing programs to determine the larger of two numbers, calculate moving distance based on force and angle, compute the 12th Fibonacci number using a formula, convert a time period in seconds to hours, minutes and seconds, and display age categories based on a user's age input. It provides examples of the expected output for each program.

Visula C# Programming Lecture 6

The document discusses classes, methods, and objects in C#. It explains that classes define methods and properties, and methods perform actions and can take parameters and return values. It provides examples of commonly used methods in classes like Console, Math, and Random. It also discusses how to define classes with data members and methods, and how to create objects from classes which allows calling instance methods on those objects. Classes serve both as program modules containing static methods and data, and as blueprints for generating objects with their own state and behavior.

Jörg Stelzer

The document discusses machine learning techniques for multivariate data analysis using the TMVA toolkit. It describes several common classification problems in high energy physics (HEP) and summarizes several machine learning algorithms implemented in TMVA for supervised learning, including rectangular cut optimization, likelihood methods, neural networks, boosted decision trees, support vector machines and rule ensembles. It also discusses challenges like nonlinear correlations between input variables and techniques for data preprocessing and decorrelation.

Lecture 2 Basic Concepts in Machine Learning for Language Technology

Definition of Machine Learning
Type of Machine Learning:
Classification
Regression
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Supervised Learning:
Supervised Classification
Training set
Hypothesis class
Empirical error
Margin
Noise
Inductive bias
Generalization
Model assessment
Cross-Validation
Classification in NLP
Types of Classification

20BCE1734.pdf

This document describes two programs that implement synchronization between two threads accessing a critical section. The first program uses Peterson's solution with a global count variable that is incremented by one thread and decremented by the other. The second program uses semaphores for synchronization, with one thread incrementing and the other decrementing a shared variable. Both aim to safely update the variable value through the critical section five times each.

Apclass (2)

This document provides an overview of programming in C++ using Turbo C++. It covers the structure of C++ programs, the Turbo C++ integrated development environment, control structures, functions, classes and object-oriented programming. It also provides examples of problem solving approaches and pseudocode for several problems involving calculations for shapes like circles and trapezoids. Modular programming using functions is discussed along with passing parameters by value and reference.

Chapter 2 ds

This document provides an introduction and overview of algorithms and analysis of algorithms. It begins with definitions of algorithms and what constitutes an algorithm. It then discusses analyzing algorithms to determine their efficiency, including analyzing worst-case, average-case, and best-case scenarios. It introduces asymptotic notation used to describe algorithm running times at different scales, including big-O notation. It also discusses computational models and analyzing algorithms in terms of counting the basic operations they perform. The document is presented as a lecture on algorithms with definitions, examples, and outlines of key topics to cover.

Lecture2a algorithm

This document provides an overview of algorithms and recursion from a lecture. It discusses performance analysis using Big O notation. Common time complexities like O(1), O(n), O(n^2) are introduced. The document defines an algorithm as a set of well-defined steps to solve a problem and categorizes algorithms as recursive vs iterative, logical, serial/parallel/distributed, deterministic/non-deterministic, exact/approximate, and quantum. Examples of recursive algorithms like factorials, greatest common divisor, and the Fibonacci sequence are presented along with their recursive definitions and code implementations.

Question đúng cnu

The document contains 55 multiple choice questions related to computer programming concepts such as control structures, variables, functions, errors, languages, and coding conventions. The questions cover topics like which control structure is used to determine program execution order, proper naming of variables, different types of errors, machine language components, necessary elements for good programs, and order of coding activities. The document tests knowledge of programming fundamentals and best practices.

Unsupervised Learning: Clustering

What am I going to get from this course?
Provides a basic conceptual understanding of how clustering works
Provides intuitive understanding of the mathematics behind various clustering algorithms
Walk through Python code examples on how to use various cluster algorithms
Show how clustering is applied in various industry applications
Check it on Experfy: https://www.experfy.com/training/courses/unsupervised-learning-clustering

Machine Learning: Foundations Course Number 0368403401

This machine learning foundations course will consist of 4 homework assignments, both theoretical and programming problems in Matlab. There will be a final exam. Students will work in groups of 2-3 to take notes during classes in LaTeX format. These class notes will contribute 30% to the overall grade. The course will cover basic machine learning concepts like storage and retrieval, learning rules, estimating flexible models, and applications in areas like control, medical diagnosis, and document retrieval.

Design and Analysis of Algorithm Brute Force 1.ppt

The document discusses the brute force algorithm design technique. It provides examples of problems that can be solved using brute force, including swapping variables, computing powers and factorials, sorting, searching, and matrix multiplication. Brute force involves systematically enumerating all possible candidates for solutions and checking if each candidate satisfies the problem's statement. The document outlines brute force algorithms for several problems and discusses the strengths and weaknesses of the brute force approach.

Data Structures and Algorithm Analysis

Definition of Data Structure,
What is Data Structure?
Types of data structure
Algorithm Analysis
Asymptotic Notation

Data structures using C

The document discusses key concepts related to data structures and algorithms in C including:
1. Data structures allow for efficient storage and retrieval of data through logical organization and mathematical modeling.
2. Algorithms must be correct, finite, and efficient to solve problems by taking input and producing output through a defined sequence of steps.
3. Common data structures covered include arrays, stacks, queues, linked lists, trees, and graphs. Abstract data types allow separation of implementation from interface.

Ds12 140715025807-phpapp02

The document discusses key concepts related to data structures and algorithms in C including:
1. Data structures allow for efficient storage and retrieval of data through logical organization and mathematical modeling.
2. Algorithms must be correct, finite, and efficient to solve problems by taking input and producing output through a defined sequence of steps.
3. Common data structures covered include arrays, stacks, queues, linked lists, trees, and graphs. Abstract data types allow separation of implementation from interface.

Machine Learning, Financial Engineering and Quantitative Investing

This document discusses machine learning applications in financial engineering and quantitative investing. It covers machine learning techniques for curve construction, model calibration, instrument valuation, and risk measurement in quantitative finance. Specifically, it discusses using machine learning methods for yield curve construction, volatility surface calibration, discount curve calibration, and model parameter estimation from historical data. The goal is to apply machine learning to automate quantitative finance tasks and improve the accuracy of pricing and risk models.

Verilog VHDL code Multiplexer and De Multiplexer

The document describes Experiment 3 which aims to implement multiplexers and demultiplexers using Verilog code and gate-level modeling. It includes the theory of multiplexers and demultiplexers, truth tables for 4:1 and 2:1 multiplexers, and Verilog code examples to simulate a 4:1 multiplexer, 2:1 demultiplexer, and 4:1 decoder along with their corresponding RTL simulations and output waveforms.

e.ppt

This document provides an introduction to algorithms through examples and analysis of running time. It demonstrates algorithms for computing the greatest common divisor and square roots. The greatest common divisor algorithm (Euclid's algorithm) is analyzed for correctness through loop invariants and termination. The square root algorithm uses a binary search approach. Analysis of algorithm running times focuses on counting basic operations and establishing asymptotic behavior using Big-O notation.

Learning to Reconstruct

Learning to Reconstruct

02. Data Types and variables

02. Data Types and variables

Dti2143 dam31303 lab sheet 5

Dti2143 dam31303 lab sheet 5

Visula C# Programming Lecture 6

Visula C# Programming Lecture 6

Jörg Stelzer

Jörg Stelzer

Lecture 2 Basic Concepts in Machine Learning for Language Technology

Lecture 2 Basic Concepts in Machine Learning for Language Technology

20BCE1734.pdf

20BCE1734.pdf

Apclass (2)

Apclass (2)

Chapter 2 ds

Chapter 2 ds

Lecture2a algorithm

Lecture2a algorithm

Question đúng cnu

Question đúng cnu

Unsupervised Learning: Clustering

Unsupervised Learning: Clustering

Machine Learning: Foundations Course Number 0368403401

Machine Learning: Foundations Course Number 0368403401

Design and Analysis of Algorithm Brute Force 1.ppt

Design and Analysis of Algorithm Brute Force 1.ppt

Data Structures and Algorithm Analysis

Data Structures and Algorithm Analysis

Data structures using C

Data structures using C

Ds12 140715025807-phpapp02

Ds12 140715025807-phpapp02

Machine Learning, Financial Engineering and Quantitative Investing

Machine Learning, Financial Engineering and Quantitative Investing

Verilog VHDL code Multiplexer and De Multiplexer

Verilog VHDL code Multiplexer and De Multiplexer

e.ppt

e.ppt

20230925プレジデント社60周年.pdf

プレジデント社60周年フォーラムで行った講演「人工知能技術の発展と社会へのインパクト」の資料です。

20230912JSSST大会基調講演_丸山.pdf

Where does Computer Science go?
(in Japanese)

20230712Kuramae-Seminar.pdf

蔵前立志セミナーでの講演資料です。

202212APSEC.pptx.pdf

Machine Learning Systems Engineering (MLSE) is a collective effort started 5 years ago in Japan to address challenges in developing and deploying machine learning systems. Key activities included panel discussions at conferences to raise awareness among software engineers, workshops identifying gaps between ML and software engineering practices, and forming a special interest group to organize further discussions. Working groups studied challenges such as fairness, infrastructure, and development processes. International collaborations helped spread ideas to other countries. Research projects explored techniques for requirements engineering, testing, debugging and assuring quality in machine learning systems to develop the new field of machine learning systems engineering. Guidelines and books were also created to establish best practices.

20210731知財学会研究会

本講演の内容は、2021年9月発売の一橋ビジネスレビュー誌2021年度 Vol.69-No.2に掲載予定の記事「アカデミアと社会 ～2項対立を超えて～」に基づきま

2021 06-17 ism-symposium

統計数理研究所 オープンハウス連携イベント
データサイエンスが描き出す「モノづくり」の未来シナリオ
～産学連携シンポジウム～
ものづくりとソフトウェア
-- DevOpsとSoftware 2.0

Jsai

人工知能学会2019年大会の招待講演です。
This is the slide deck that I presented at Japan AI Society Annual Conference in 2019. English speaker notes are attached.

20181212 ibm aot

This is my presentation on Evolution of Computing at IBM's Academy of Technology Regional Meeting on Dec. 12, 2018.

20181205 sakigake

さきがけトークイベントでの講演資料です。

20181204i mlse 1

1) Deep neural networks can output any point in space but this is problematic when outputs must remain within a defined feasible region.
2) The presentation proposes transforming the output space to guarantee outputs fall within the feasible region. This is done by bounding the space to a hypercube around a pivot point, then shrinking/extending points toward the origin while keeping the pivot interior.
3) With this transformation, the output is guaranteed to remain feasible for any model parameters or inputs, allowing training to continue while enforcing constraints.

20181120 ldp ai

自民党AI本部でプレゼンした内容です。

20181030 fun

10/30/2018に函館未来大学で行った、機械学習工学に関するプレゼンです。

20180719 cocn dist

COCNフォーラムにおけるプレゼンテーションです。

20180601 ai discussions

内閣府の人間中心のＡＩ社会原則検討会議第2回(6/1)の議論のための資料です。

構造改革徹底推進会合におけるプレゼン

4/4に内閣官房日本経済再生本部での「AI人材」に関する議論で話す内容です。

深層学習よもやま話

Deep Learning Labでお話したよもやま話です。

20230925プレジデント社60周年.pdf

20230925プレジデント社60周年.pdf

20230912JSSST大会基調講演_丸山.pdf

20230912JSSST大会基調講演_丸山.pdf

20230712Kuramae-Seminar.pdf

20230712Kuramae-Seminar.pdf

202212APSEC.pptx.pdf

202212APSEC.pptx.pdf

20210731知財学会研究会

20210731知財学会研究会

2021 06-17 ism-symposium

2021 06-17 ism-symposium

Jsai

Jsai

20181212 ibm aot

20181212 ibm aot

20181205 sakigake

20181205 sakigake

20181204i mlse 1

20181204i mlse 1

20181120 ldp ai

20181120 ldp ai

20181030 fun

20181030 fun

20180719 cocn dist

20180719 cocn dist

20180601 ai discussions

20180601 ai discussions

構造改革徹底推進会合におけるプレゼン

構造改革徹底推進会合におけるプレゼン

深層学習よもやま話

深層学習よもやま話

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Comprehensive Vulnerability Assessments Process _ Aardwolf Security.docx

A Comprehensive vulnerability assessment process involves defining, identifying, classifying, and reporting cyber vulnerabilities across endpoints, workloads, and systems. Consult Aardwolf Security for the best services. For more details visit our website.
https://aardwolfsecurity.com/security-testing/vulnerability-assessment-services/

Cisco Live Announcements: New ThousandEyes Release Highlights - July 2024

Cisco Live Announcements: New ThousandEyes Release Highlights - July 2024

welcome to presentation on Google Apps

about Google Apps

Wired_2.0_Create_AmsterdamJUG_09072024.pptx

In this talk, we will explore strategies to optimize the success rate of storing and retaining new information. We will discuss scientifically proven ideal learning intervals and content structures. Additionally, we will examine how to create an environment that improves our focus while you remain in the “flow”. Lastly we will also address the influence of AI on learning capabilities.
In the dynamic field of software development, this knowledge will empower you to accelerate your learning curve and support others in their learning journeys.

Artificial intelligence in customer services or chatbots

artificial intelligence in customer services

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The Ultimate Guide to Phone Spy Apps: Everything You Need to Know

Unlock the ultimate guide to phone spy apps with our comprehensive overview! Discover everything you need to know about monitoring smartphone activities discreetly and effectively. From parental control to employee management and personal security, learn how these apps, including the renowned ONEMONITAR, can safeguard your loved ones and protect your data. Dive into essential features, choosing the right app, and ethical usage tips. Stay informed and empowered in the digital age with our in-depth guide!

Mobile App Development Company in Noida - Drona Infotech.

Mobile App Development Company in Noida - Drona Infotech.Mobile App Development Company in Noida - Drona Infotech

Drona Infotech is one of the Best Mobile App Development Company in Noida. Transform your business with our custom app development services, designed to meet your specific needs and goals.
Visit Us For: https://www.dronainfotech.com/mobile-application-development/Odoo E-commerce website development guides

Odoo is a powerful platform that offers a comprehensive suite of business applications, including an e-commerce website builder. With Odoo, businesses can create custom websites tailored to their specific needs and preferences. One real-world example of Odoo's e-commerce capabilities is seen in the success story of a small online retailer that used Odoo to streamline their online sales process. By leveraging Odoo's website development tools, the retailer was able to create a user-friendly and visually appealing online store that significantly boosted their sales.
Odoo's e-commerce website development features a drag-and-drop interface, making it easy for users to design and customize their online stores without the need for extensive coding knowledge. This user-friendly approach allows businesses to quickly launch their e-commerce websites and make updates on the fly. Additionally, Odoo offers a wide range of templates and themes to choose from, enabling businesses to create a unique and professional online presence.
One of the key benefits of using Odoo for e-commerce website development is its seamless integration with other Odoo applications, such as inventory management and CRM. This integration ensures that businesses have a holistic view of their operations and can easily manage orders, track inventory, and engage with customers all within the same platform. By centralizing these functions, businesses can streamline operations and improve efficiency.

ERP Software Solutions Provider in Coimbatore

In the bustling tech hub of Coimbatore, businesses are rapidly adopting Enterprise Resource Planning (ERP) software to stay competitive. As the textile capital of South India evolves into a major IT center, the demand for robust ERP solutions has skyrocketed.

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Software development... for all? (keynote at ICSOFT'2024)

Our world runs on software. It governs all major aspects of our life. It is an enabler for research and innovation, and is critical for business competitivity. Traditional software engineering techniques have achieved high effectiveness, but still may fall short on delivering software at the accelerated pace and with the increasing quality that future scenarios will require.
To attack this issue, some software paradigms raise the automation of software development via higher levels of abstraction through domain-specific languages (e.g., in model-driven engineering) and empowering non-professional developers with the possibility to build their own software (e.g., in low-code development approaches). In a software-demanding world, this is an attractive possibility, and perhaps -- paraphrasing Andy Warhol -- "in the future, everyone will be a developer for 15 minutes". However, to make this possible, methods are required to tweak languages to their context of use (crucial given the diversity of backgrounds and purposes), and the assistance to developers throughout the development process (especially critical for non-professionals).
In this keynote talk at ICSOFT'2024 I presented enabling techniques for this vision, supporting the creation of families of domain-specific languages, their adaptation to the usage context; and the augmentation of low-code environments with assistants and recommender systems to guide developers (professional or not) in the development process.

How To Fill Timesheet in TaskSprint: Quick Guide 2024

Overview: How To Fill Timesheet In TaskSprint?
Ever feel like time is running fast and slipping through your fingers? Yes, we have all experienced it. You put your nose to the grindstone for a project and deal with tasks and deadlines as if they were easy hurdles. But when it is time to complete a timesheet, you find yourself at sea about the amount of time each project consumes. But fear not, fellow soldier, in the battle against time! TaskSprint, your reliable sidekick in project management, offers an in-built timesheet feature to make tracking your hours seem like a walk in the park.
This is a detailed guide that will lead you in such a way that you will become familiar with how to fill the timesheet. We'll show you how to navigate the interface, easily add entries, and ensure your project manager understands your valuable work hours.
So, ditch the guesswork and embrace precise time tracking. Get ready to transform your timesheet woes into a streamlined, efficient process. Let's dive in and learn how to fill timesheets.

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GT degree offer diploma Transcript

学历定制【微信号:95270640】《(GT毕业证书)乔治城大学毕业证》【微信号:95270640】《毕业证、成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，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）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，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【关于学历材料质量】
我们承诺采用的是学校原版纸张（原版纸质、底色、纹路）我们工厂拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有成品以及工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号95270640】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号95270640】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
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
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

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- 1. iMLSE Discussions Hiroshi Maruyama PFN Fellow
- 2. 2 What is Deep Learning? – A (Stateless) Function Y = f(X)X Y Very high- dimensional, any combination of continuous and categorial variables Low-dimensional for classification, very high-dimensional for generation
- 3. 3 Example: Converting Celsius to Fahrenheit Hiroshi Maruyama double c2f(double c) { return 1.8*c + 32.0; } Input: C Output: F Where F is Fahrenheit equivalent of C in Celsius Requirements Algorithm F = 1.8 * C + 32Model A Priori Knowledge Model must be know in advance, and Algorithm must be constructible
- 4. Training Data Set Observation Training（search for parameter θ） No knowledge on model or algorithm is required! Alternative Approach – Data-Driven, Inductive Programming (aka Statistical Modeling)
- 5. 5 Deep Neural Net as a Universal Computing Mechanism ⚫ Very large number of parameters ⚫ Can approximate ANY high- dimensional function* ➔ Pseudo Turing Complete! Output Input * G. Cybenko. Approximations by superpositions of sigmoidal functions. Mathematics of Control, Signals, and Systems, 2(4):303–314, 1989.
- 6. Maruyama’s Conjecture: In 2020, more than half of newly developed software have inductively-trained components This is the largest paradigm shift since the inventin of digital computer!
- 7. https://medium.com/@mijordan3/artificial-intelligence-the-revolution-hasnt-happened-yet-5e1d5812e1e7 The need for new engineering descipline
- 8. What is engineering? Theories（e.g., structure） ＊ Safety Factor New technology is accepted by the society only after it becomes engineering descipline Civil Engineering Handbook, p999 Why do we trust bridges? Because of the accumulated knowledge called Civil Engineering
- 9. 9 What are the SE concepts that can be brought into the ML world? ⚫ Testing — Covarage — Regression ⚫ Invariance ⚫ Reuse ⚫ :