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An explanation of fundamental concepts of features and models in machine learning, building on our geometric intuition of high dimensional spaces.

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Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...

This document discusses support vector machines (SVM) and provides an example of using SVM for classification. It begins with common applications of SVM like face detection and image classification. It then provides an overview of SVM, explaining how it finds the optimal separating hyperplane between two classes by maximizing the margin between them. An example demonstrates SVM by classifying people as male or female based on height and weight data. It also discusses how kernels can be used to handle non-linearly separable data. The document concludes by showing an implementation of SVM on a zoos dataset to classify animals as crocodiles or alligators.

Machine Learning Deep Learning AI and Data Science

What is Machine Learning
What is Deep Learning
What is Data Science
What is Artificial Intelligence

K-Nearest Neighbor Classifier

K-Nearest neighbor is one of the most commonly used classifier based in lazy learning. It is one of the most commonly used methods in recommendation systems and document similarity measures. It mainly uses Euclidean distance to find the similarity measures between two data points.

Bias and variance trade off

This manuscript addresses the fundamentals of the trade-off relation between bias and variance in machine learning.

6 games

This document discusses artificial intelligence for game playing. It introduces different types of games and optimal strategies for games like minimax and alpha-beta pruning. It also discusses challenges for games of imperfect information that include elements of chance, as well as techniques for heuristic evaluation and expected value calculations when chance is involved.

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.

Regularization

VC dimension & VC bound – Frequentist viewpoint
L1 regularization – An intuitive interpretation
Model parameter prior – Bayesian viewpoint
Early stopping – Also a regularization
Conclusion

Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...

This document provides an overview of machine learning, including:
- Machine learning allows computers to learn from data without being explicitly programmed, through processes like analyzing data, training models on past data, and making predictions.
- The main types of machine learning are supervised learning, which uses labeled training data to predict outputs, and unsupervised learning, which finds patterns in unlabeled data.
- Common supervised learning tasks include classification (like spam filtering) and regression (like weather prediction). Unsupervised learning includes clustering, like customer segmentation, and association, like market basket analysis.
- Supervised and unsupervised learning are used in many areas like risk assessment, image classification, fraud detection, customer analytics, and more

Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...

This document discusses support vector machines (SVM) and provides an example of using SVM for classification. It begins with common applications of SVM like face detection and image classification. It then provides an overview of SVM, explaining how it finds the optimal separating hyperplane between two classes by maximizing the margin between them. An example demonstrates SVM by classifying people as male or female based on height and weight data. It also discusses how kernels can be used to handle non-linearly separable data. The document concludes by showing an implementation of SVM on a zoos dataset to classify animals as crocodiles or alligators.

Machine Learning Deep Learning AI and Data Science

What is Machine Learning
What is Deep Learning
What is Data Science
What is Artificial Intelligence

K-Nearest Neighbor Classifier

K-Nearest neighbor is one of the most commonly used classifier based in lazy learning. It is one of the most commonly used methods in recommendation systems and document similarity measures. It mainly uses Euclidean distance to find the similarity measures between two data points.

Bias and variance trade off

This manuscript addresses the fundamentals of the trade-off relation between bias and variance in machine learning.

6 games

This document discusses artificial intelligence for game playing. It introduces different types of games and optimal strategies for games like minimax and alpha-beta pruning. It also discusses challenges for games of imperfect information that include elements of chance, as well as techniques for heuristic evaluation and expected value calculations when chance is involved.

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.

Regularization

VC dimension & VC bound – Frequentist viewpoint
L1 regularization – An intuitive interpretation
Model parameter prior – Bayesian viewpoint
Early stopping – Also a regularization
Conclusion

Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...

This document provides an overview of machine learning, including:
- Machine learning allows computers to learn from data without being explicitly programmed, through processes like analyzing data, training models on past data, and making predictions.
- The main types of machine learning are supervised learning, which uses labeled training data to predict outputs, and unsupervised learning, which finds patterns in unlabeled data.
- Common supervised learning tasks include classification (like spam filtering) and regression (like weather prediction). Unsupervised learning includes clustering, like customer segmentation, and association, like market basket analysis.
- Supervised and unsupervised learning are used in many areas like risk assessment, image classification, fraud detection, customer analytics, and more

GANs Deep Learning Summer School

This document discusses generative adversarial networks (GANs) and provides several summaries:
1. GANs use two neural networks, a generator and discriminator, that compete in a game theoretic framework to generate new data instances that match the training data distribution.
2. Training GANs involves training the generator to generate more realistic samples to fool the discriminator while training the discriminator to better distinguish real and generated samples.
3. Several strategies for training GANs are discussed, including varying the update rates of the generator and discriminator, using cooperative or random update strategies, and applying penalties for noisy samples.

Introduction to Statistical Machine Learning

This course provides a broad introduction to the methods and practice
of statistical machine learning, which is concerned with the development
of algorithms and techniques that learn from observed data by
constructing stochastic models that can be used for making predictions
and decisions. Topics covered include Bayesian inference and maximum
likelihood modeling; regression, classi¯cation, density estimation,
clustering, principal component analysis; parametric, semi-parametric,
and non-parametric models; basis functions, neural networks, kernel
methods, and graphical models; deterministic and stochastic
optimization; over¯tting, regularization, and validation.

Genetic Algorithm in Artificial Intelligence

Genetic algorithms are a heuristic search technique inspired by biological evolution to find optimized solutions to problems. The workflow involves initially generating a random population which is then evaluated based on a fitness function. Individuals are selected from the population based on their fitness for reproduction, with crossover and mutation occurring to create a new generation. This process is repeated until an optimal solution is found. Genetic algorithms have applications in fields like robotics, medicine, and computer gaming. They provide advantages like not requiring derivatives and being able to optimize both continuous and discrete functions, but also have limitations such as computational expense and not guaranteeing optimal solutions.

AI Lecture 3 (solving problems by searching)

The document discusses problem solving by searching. It describes problem solving agents and how they formulate goals and problems, search for solutions, and execute solutions. Tree search algorithms like breadth-first search, uniform-cost search, and depth-first search are described. Example problems discussed include the 8-puzzle, 8-queens, and route finding problems. The strategies of different uninformed search algorithms are explained.

Types of Machine Learning

This presentation will give you the information about the types of Machine learning types and its algorithms.

Supervised learning

This presentation is here to help you understand about Machine Learning, supervised Learning, Process Flow chat of Supervised Learning and 2 steps of supervised Learning.

Unit8: Uncertainty in AI

This document presents an overview of uncertainty in artificial intelligence, specifically focusing on fuzzy logic. It defines fuzzy logic as a way to deal with imprecise and vague information using degrees of truth rather than binary logic. The key concepts discussed include fuzzy sets and their operations, fuzzy rules and inferences, and applications of fuzzy logic systems. Examples are provided to illustrate fuzzy sets for describing tall men and an air conditioning system controlled by a fuzzy logic controller.

3 problem-solving-

The document discusses problem solving agents and search algorithms. It describes problem solving as having four steps: goal formulation, problem formulation, search, and execution. It then discusses different types of problems agents may face, such as single state problems and problems with partial information. The document introduces tree search algorithms and strategies for searching a state space, such as breadth-first search. It analyzes the performance of breadth-first search and notes its exponential time and memory complexity for large problems.

Unsupervised learning

This document discusses unsupervised learning approaches including clustering, blind signal separation, and self-organizing maps (SOM). Clustering groups unlabeled data points together based on similarities. Blind signal separation separates mixed signals into their underlying source signals without information about the mixing process. SOM is an algorithm that maps higher-dimensional data onto lower-dimensional displays to visualize relationships in the data.

Beginners Guide to Non-Negative Matrix Factorization

This is a quick introduction to Non-Negative Matrix Factorization to implement supervised machine learning and the NNMF predictive model.

Adversarial search with Game Playing

Adversarial search is an algorithm used in game playing to plan ahead when other agents are planning against you. The minimax algorithm determines the optimal strategy by assuming the opponent will make the best counter-move. It searches the game tree to find the move with the highest minimum payoff. α-β pruning improves on minimax by pruning branches that cannot affect the choice of move. State-of-the-art game programs use techniques like precomputed databases, deep search trees, and pattern knowledge bases to defeat human champions at games like checkers, chess, and Othello.

Bayseian decision theory

- Bayesian decision theory provides an optimal framework for decision making when the underlying probability distributions are known.
- The Bayes rule is used to calculate the posterior probabilities of class membership given an observation's features.
- A loss function assigns costs to different types of classification mistakes and aims to minimize the total expected loss. This guides the decision rule.
- Discriminant functions are used to partition the feature space into decision regions corresponding to each class. The decision boundaries are determined by where two discriminant functions are equal.

An introduction to Machine Learning

This document provides an introduction to machine learning. It discusses how machine learning allows computers to learn from experience to improve their performance on tasks. Supervised learning is described, where the goal is to learn a function that maps inputs to outputs from a labeled dataset. Cross-validation techniques like the test set method, leave-one-out cross-validation, and k-fold cross-validation are introduced to evaluate model performance without overfitting. Applications of machine learning like medical diagnosis, recommendation systems, and autonomous driving are briefly outlined.

A brief history of machine learning

This document provides an incomplete history of machine learning from 1946 to 2016. It describes some of the major developments in the field including the first general purpose computer (ENIAC), Arthur Samuel creating the first machine learning program to play checkers in 1955, the development of the perceptron in 1958, Marvin Minsky's influential work establishing limits of perceptrons, the AI winter from 1970-1980, the rediscovery of backpropagation in the 1980s reigniting neural networks research, support vector machines gaining popularity in the 1990s, IBM's Deep Blue beating Garry Kasparov at chess in 1997, advances in image recognition with challenges like ImageNet, AlphaGo defeating top Go players in 2016, and Geoffrey Hinton's vision

Support Vector Machines for Classification

In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier.

Game Playing in Artificial Intelligence

Game Playing in Artificial Intelligence, a Computer Science fields, presented by Mwendwa Kivuva at Catholic University of Eastern Africa

Supervised learning and Unsupervised learning

This document discusses supervised and unsupervised machine learning. Supervised learning uses labeled training data to learn a function that maps inputs to outputs. Unsupervised learning is used when only input data is available, with the goal of modeling underlying structures or distributions in the data. Common supervised algorithms include decision trees and logistic regression, while common unsupervised algorithms include k-means clustering and dimensionality reduction.

Adversarial search

Adversarial search is a technique used in game playing to determine the best move when facing an opponent who is also trying to maximize their score. It involves searching through possible future game states called a game tree to evaluate the best outcome. The minimax algorithm searches the entire game tree to determine the optimal move by assuming the opponent will make the best counter-move. Alpha-beta pruning improves on minimax by pruning branches that cannot affect the choice of move. Modern game programs use techniques like precomputed databases, sophisticated evaluation functions, and extensive search to defeat human champions at games like checkers, chess, and Othello.

Training Neural Networks

Part 2 of the Deep Learning Fundamentals Series, this session discusses Tuning Training (including hyperparameters, overfitting/underfitting), Training Algorithms (including different learning rates, backpropagation), Optimization (including stochastic gradient descent, momentum, Nesterov Accelerated Gradient, RMSprop, Adaptive algorithms - Adam, Adadelta, etc.), and a primer on Convolutional Neural Networks. The demos included in these slides are running on Keras with TensorFlow backend on Databricks.

Introduction to Machine Learning Classifiers

You will learn the basic concepts of machine learning classification and will be introduced to some different algorithms that can be used. This is from a very high level and will not be getting into the nitty-gritty details.

The How and Why of Feature Engineering

Feature engineering--the underdog of machine learning. This deck provides an overview of feature generation methods for text, image, audio, feature cleaning and transformation methods, how well they work and why.

Feature Engineering - Getting most out of data for predictive models

How should data be preprocessed for use in machine learning algorithms? How to identify the most predictive attributes of a dataset? What features can generate to improve the accuracy of a model?
Feature Engineering is the process of extracting and selecting, from raw data, features that can be used effectively in predictive models. As the quality of the features greatly influences the quality of the results, knowing the main techniques and pitfalls will help you to succeed in the use of machine learning in your projects.
In this talk, we will present methods and techniques that allow us to extract the maximum potential of the features of a dataset, increasing flexibility, simplicity and accuracy of the models. The analysis of the distribution of features and their correlations, the transformation of numeric attributes (such as scaling, normalization, log-based transformation, binning), categorical attributes (such as one-hot encoding, feature hashing, Temporal (date / time), and free-text attributes (text vectorization, topic modeling).
Python, Python, Scikit-learn, and Spark SQL examples will be presented and how to use domain knowledge and intuition to select and generate features relevant to predictive models.

GANs Deep Learning Summer School

This document discusses generative adversarial networks (GANs) and provides several summaries:
1. GANs use two neural networks, a generator and discriminator, that compete in a game theoretic framework to generate new data instances that match the training data distribution.
2. Training GANs involves training the generator to generate more realistic samples to fool the discriminator while training the discriminator to better distinguish real and generated samples.
3. Several strategies for training GANs are discussed, including varying the update rates of the generator and discriminator, using cooperative or random update strategies, and applying penalties for noisy samples.

Introduction to Statistical Machine Learning

This course provides a broad introduction to the methods and practice
of statistical machine learning, which is concerned with the development
of algorithms and techniques that learn from observed data by
constructing stochastic models that can be used for making predictions
and decisions. Topics covered include Bayesian inference and maximum
likelihood modeling; regression, classi¯cation, density estimation,
clustering, principal component analysis; parametric, semi-parametric,
and non-parametric models; basis functions, neural networks, kernel
methods, and graphical models; deterministic and stochastic
optimization; over¯tting, regularization, and validation.

Genetic Algorithm in Artificial Intelligence

Genetic algorithms are a heuristic search technique inspired by biological evolution to find optimized solutions to problems. The workflow involves initially generating a random population which is then evaluated based on a fitness function. Individuals are selected from the population based on their fitness for reproduction, with crossover and mutation occurring to create a new generation. This process is repeated until an optimal solution is found. Genetic algorithms have applications in fields like robotics, medicine, and computer gaming. They provide advantages like not requiring derivatives and being able to optimize both continuous and discrete functions, but also have limitations such as computational expense and not guaranteeing optimal solutions.

AI Lecture 3 (solving problems by searching)

The document discusses problem solving by searching. It describes problem solving agents and how they formulate goals and problems, search for solutions, and execute solutions. Tree search algorithms like breadth-first search, uniform-cost search, and depth-first search are described. Example problems discussed include the 8-puzzle, 8-queens, and route finding problems. The strategies of different uninformed search algorithms are explained.

Types of Machine Learning

This presentation will give you the information about the types of Machine learning types and its algorithms.

Supervised learning

This presentation is here to help you understand about Machine Learning, supervised Learning, Process Flow chat of Supervised Learning and 2 steps of supervised Learning.

Unit8: Uncertainty in AI

This document presents an overview of uncertainty in artificial intelligence, specifically focusing on fuzzy logic. It defines fuzzy logic as a way to deal with imprecise and vague information using degrees of truth rather than binary logic. The key concepts discussed include fuzzy sets and their operations, fuzzy rules and inferences, and applications of fuzzy logic systems. Examples are provided to illustrate fuzzy sets for describing tall men and an air conditioning system controlled by a fuzzy logic controller.

3 problem-solving-

The document discusses problem solving agents and search algorithms. It describes problem solving as having four steps: goal formulation, problem formulation, search, and execution. It then discusses different types of problems agents may face, such as single state problems and problems with partial information. The document introduces tree search algorithms and strategies for searching a state space, such as breadth-first search. It analyzes the performance of breadth-first search and notes its exponential time and memory complexity for large problems.

Unsupervised learning

This document discusses unsupervised learning approaches including clustering, blind signal separation, and self-organizing maps (SOM). Clustering groups unlabeled data points together based on similarities. Blind signal separation separates mixed signals into their underlying source signals without information about the mixing process. SOM is an algorithm that maps higher-dimensional data onto lower-dimensional displays to visualize relationships in the data.

Beginners Guide to Non-Negative Matrix Factorization

This is a quick introduction to Non-Negative Matrix Factorization to implement supervised machine learning and the NNMF predictive model.

Adversarial search with Game Playing

Adversarial search is an algorithm used in game playing to plan ahead when other agents are planning against you. The minimax algorithm determines the optimal strategy by assuming the opponent will make the best counter-move. It searches the game tree to find the move with the highest minimum payoff. α-β pruning improves on minimax by pruning branches that cannot affect the choice of move. State-of-the-art game programs use techniques like precomputed databases, deep search trees, and pattern knowledge bases to defeat human champions at games like checkers, chess, and Othello.

Bayseian decision theory

- Bayesian decision theory provides an optimal framework for decision making when the underlying probability distributions are known.
- The Bayes rule is used to calculate the posterior probabilities of class membership given an observation's features.
- A loss function assigns costs to different types of classification mistakes and aims to minimize the total expected loss. This guides the decision rule.
- Discriminant functions are used to partition the feature space into decision regions corresponding to each class. The decision boundaries are determined by where two discriminant functions are equal.

An introduction to Machine Learning

This document provides an introduction to machine learning. It discusses how machine learning allows computers to learn from experience to improve their performance on tasks. Supervised learning is described, where the goal is to learn a function that maps inputs to outputs from a labeled dataset. Cross-validation techniques like the test set method, leave-one-out cross-validation, and k-fold cross-validation are introduced to evaluate model performance without overfitting. Applications of machine learning like medical diagnosis, recommendation systems, and autonomous driving are briefly outlined.

A brief history of machine learning

This document provides an incomplete history of machine learning from 1946 to 2016. It describes some of the major developments in the field including the first general purpose computer (ENIAC), Arthur Samuel creating the first machine learning program to play checkers in 1955, the development of the perceptron in 1958, Marvin Minsky's influential work establishing limits of perceptrons, the AI winter from 1970-1980, the rediscovery of backpropagation in the 1980s reigniting neural networks research, support vector machines gaining popularity in the 1990s, IBM's Deep Blue beating Garry Kasparov at chess in 1997, advances in image recognition with challenges like ImageNet, AlphaGo defeating top Go players in 2016, and Geoffrey Hinton's vision

Support Vector Machines for Classification

In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier.

Game Playing in Artificial Intelligence

Game Playing in Artificial Intelligence, a Computer Science fields, presented by Mwendwa Kivuva at Catholic University of Eastern Africa

Supervised learning and Unsupervised learning

This document discusses supervised and unsupervised machine learning. Supervised learning uses labeled training data to learn a function that maps inputs to outputs. Unsupervised learning is used when only input data is available, with the goal of modeling underlying structures or distributions in the data. Common supervised algorithms include decision trees and logistic regression, while common unsupervised algorithms include k-means clustering and dimensionality reduction.

Adversarial search

Adversarial search is a technique used in game playing to determine the best move when facing an opponent who is also trying to maximize their score. It involves searching through possible future game states called a game tree to evaluate the best outcome. The minimax algorithm searches the entire game tree to determine the optimal move by assuming the opponent will make the best counter-move. Alpha-beta pruning improves on minimax by pruning branches that cannot affect the choice of move. Modern game programs use techniques like precomputed databases, sophisticated evaluation functions, and extensive search to defeat human champions at games like checkers, chess, and Othello.

Training Neural Networks

Part 2 of the Deep Learning Fundamentals Series, this session discusses Tuning Training (including hyperparameters, overfitting/underfitting), Training Algorithms (including different learning rates, backpropagation), Optimization (including stochastic gradient descent, momentum, Nesterov Accelerated Gradient, RMSprop, Adaptive algorithms - Adam, Adadelta, etc.), and a primer on Convolutional Neural Networks. The demos included in these slides are running on Keras with TensorFlow backend on Databricks.

Introduction to Machine Learning Classifiers

You will learn the basic concepts of machine learning classification and will be introduced to some different algorithms that can be used. This is from a very high level and will not be getting into the nitty-gritty details.

GANs Deep Learning Summer School

GANs Deep Learning Summer School

Introduction to Statistical Machine Learning

Introduction to Statistical Machine Learning

Genetic Algorithm in Artificial Intelligence

Genetic Algorithm in Artificial Intelligence

AI Lecture 3 (solving problems by searching)

AI Lecture 3 (solving problems by searching)

Types of Machine Learning

Types of Machine Learning

Supervised learning

Supervised learning

Unit8: Uncertainty in AI

Unit8: Uncertainty in AI

3 problem-solving-

3 problem-solving-

Unsupervised learning

Unsupervised learning

Beginners Guide to Non-Negative Matrix Factorization

Beginners Guide to Non-Negative Matrix Factorization

Adversarial search with Game Playing

Adversarial search with Game Playing

Bayseian decision theory

Bayseian decision theory

An introduction to Machine Learning

An introduction to Machine Learning

A brief history of machine learning

A brief history of machine learning

Support Vector Machines for Classification

Support Vector Machines for Classification

Game Playing in Artificial Intelligence

Game Playing in Artificial Intelligence

Supervised learning and Unsupervised learning

Supervised learning and Unsupervised learning

Adversarial search

Adversarial search

Training Neural Networks

Training Neural Networks

Introduction to Machine Learning Classifiers

Introduction to Machine Learning Classifiers

The How and Why of Feature Engineering

Feature engineering--the underdog of machine learning. This deck provides an overview of feature generation methods for text, image, audio, feature cleaning and transformation methods, how well they work and why.

Feature Engineering - Getting most out of data for predictive models

How should data be preprocessed for use in machine learning algorithms? How to identify the most predictive attributes of a dataset? What features can generate to improve the accuracy of a model?
Feature Engineering is the process of extracting and selecting, from raw data, features that can be used effectively in predictive models. As the quality of the features greatly influences the quality of the results, knowing the main techniques and pitfalls will help you to succeed in the use of machine learning in your projects.
In this talk, we will present methods and techniques that allow us to extract the maximum potential of the features of a dataset, increasing flexibility, simplicity and accuracy of the models. The analysis of the distribution of features and their correlations, the transformation of numeric attributes (such as scaling, normalization, log-based transformation, binning), categorical attributes (such as one-hot encoding, feature hashing, Temporal (date / time), and free-text attributes (text vectorization, topic modeling).
Python, Python, Scikit-learn, and Spark SQL examples will be presented and how to use domain knowledge and intuition to select and generate features relevant to predictive models.

Horovod - Distributed TensorFlow Made Easy

Uber Engineering introduces Horovod, an open source framework that makes it faster and easier to train deep learning models with TensorFlow.

Transfer Learning and Fine Tuning for Cross Domain Image Classification with ...

Supporting code for my talk at Demystifying Deep Learning and AI event on November 19-20 2016 at Oakland CA.

Lessons from 2MM machine learning models

Kaggle is a community of almost 400K data scientists who have built almost 2MM machine learning models to participate in our competitions. Data scientists come to Kaggle to learn, collaborate and develop the state of the art in machine learning. This talk will cover some of the lessons we have learned from the Kaggle community.

Large-Scale Training with GPUs at Facebook

This document discusses large-scale distributed training with GPUs at Facebook using their Caffe2 framework. It describes how Facebook was able to train the ResNet-50 model on the ImageNet dataset in just 1 hour using 32 GPUs with 8 GPUs each. It explains how synchronous SGD was implemented in Caffe2 using Gloo for efficient all-reduce operations. Linear scaling of the learning rate with increased batch size was found to work best when gradually warming up the learning rate over the first few epochs. Nearly linear speedup was achieved using this approach on commodity hardware.

Parameter Server Approach for Online Learning at Twitter

Parameter Server approaches for online learning at Twitter allow models to be updated continuously based on new data and improve predictions in real-time. Version 1.0 decouples training and prediction to increase efficiency. Version 2.0 scales training by distributing it across servers. Version 3.0 will scale large complex models by sharding models and features across multiple servers. These approaches enable Twitter to perform online learning on massive datasets and complex models in real-time.

2017 10-10 (netflix ml platform meetup) learning item and user representation...

1) Learning user and item representations is challenging due to sparse data and shifting preferences in recommender systems.
2) The presentation outlines research at Google to address sparsity through two approaches: focused learning, which develops specialized models for subsets of data like genres or cold-start items, and factorized deep retrieval, which jointly embeds items and their features to predict preferences for fresh items.
3) The techniques have improved overall viewership and nomination of candidates, demonstrating their effectiveness in production recommender systems.

The How and Why of Feature Engineering

The How and Why of Feature Engineering

Feature Engineering - Getting most out of data for predictive models

Feature Engineering - Getting most out of data for predictive models

Horovod - Distributed TensorFlow Made Easy

Horovod - Distributed TensorFlow Made Easy

Transfer Learning and Fine Tuning for Cross Domain Image Classification with ...

Transfer Learning and Fine Tuning for Cross Domain Image Classification with ...

Lessons from 2MM machine learning models

Lessons from 2MM machine learning models

Large-Scale Training with GPUs at Facebook

Large-Scale Training with GPUs at Facebook

Parameter Server Approach for Online Learning at Twitter

Parameter Server Approach for Online Learning at Twitter

2017 10-10 (netflix ml platform meetup) learning item and user representation...

2017 10-10 (netflix ml platform meetup) learning item and user representation...

Understanding Feature Space in Machine Learning - Data Science Pop-up Seattle

Machine learning derives mathematical models from raw data. In the model building process, raw data is first processed into "features," then the features are given to algorithms to train a model. The process of turning raw data into features is sometimes called feature engineering, and it is a crucial step in model building. Good features lead to successful models with a lot of predictive power; bad features lead to a lot of headache and nowhere.
This talk aims to help the audience understand what is a feature space and why it is so important. We will go through some common feature space representations of English text and discuss what tasks they are suited for and why. Expect lots of pictures, whiteboard drawings and handwaving. We will exercise our power of imagination to visualize high dimensional feature spaces in our mind's eye. Presented by Alice Zheng Director of Data Science at Dato.

Maths in the PYP - A Journey through the Arts

This document outlines an agenda for a mathematical journey through the arts workshop. It includes an icebreaker activity, sharing beliefs about mathematics, exploring the connections between math and art, action planning, and reflection. During the workshop, participants will read stories with mathematical concepts and use manipulatives like ladybugs and caterpillars to develop their understanding of addition and subtraction. The document emphasizes building conceptual understanding through concrete and pictorial representations before introducing symbolic notation.

Introduction to LLMs, Prompt Engineering fundamentals,

- Prompt Engineering fundamentals
- Google Foundations Models
- Advanced Prompting Techniques
- ReAct prompting
- Prompting best practices
- Open source LLM
- Google Gemma
- EU Artificial Intelligence Act

[D2 COMMUNITY] Spark User Group - 머신러닝 인공지능 기법

1) The document discusses various approaches and techniques in artificial intelligence including symbolic logic, planning, expert systems, fuzzy logic, genetic algorithms, Bayesian networks, and more.
2) It provides examples of each technique including using logic to represent arguments, planning routes for a traveling salesman, building financial expert systems, applying fuzzy logic to tipping recommendations, and using Bayesian networks for medical diagnosis.
3) The key challenges of AI discussed are computational complexity, problems with first-order logic like undecidability and uncertainty, and the difficulty of non-symbolic approaches like uncertainty in real-world problems.

CO Quadratic Inequalties.pptx

This document provides information and instructions about quadratic inequalities. It begins with objectives to identify and describe quadratic inequalities using practical situations and mathematical expressions. It then defines quadratic inequalities as inequalities containing polynomials of degree 2. The standard form of quadratic inequalities is presented. Examples of quadratic inequalities in standard and non-standard form are given and worked through. Steps for solving quadratic inequalities are demonstrated. Activities include matching terms to definitions, describing examples, and completing a table with quadratic expressions and symbols. The document aims to build understanding of quadratic inequalities.

Latent dirichlet allocation_and_topic_modeling

1. LDA represents documents as mixtures of topics and topics as mixtures of words.
2. It assumes documents are generated by first choosing a topic distribution, then choosing words from that topic.
3. The algorithm estimates topic distributions for each document and word distributions for each topic that are most likely to have generated the observed document-word matrix.

Ml3

This document provides an overview of machine learning and feature engineering. It discusses how machine learning can be used for tasks like classification, regression, similarity matching, and clustering. It explains that feature engineering involves transforming raw data into numeric representations called features that machine learning models can use. Different techniques for feature engineering text and images are presented, such as bag-of-words and convolutional neural networks. Dimensionality reduction through principal component analysis is demonstrated. Finally, information is given about upcoming machine learning tutorials and Dato's machine learning platform.

Overview of Machine Learning and Feature Engineering

Machine Learning 101 Tutorial at Strata NYC, Sep 2015
Overview of machine learning models and features. Visualization of feature space and feature engineering methods.

Infrastructures et recommandations pour les Humanités Numériques - Big Data e...

Infrastructures et recommandations pour les Humanités Numériques - Big Data e...Patrice Bellot - Aix-Marseille Université / CNRS (LIS, INS2I)

Le développement du Web et des réseaux sociaux ou les numérisations massives de documents contribuent à un renouvellement des Sciences Humaines et Sociales, des études des patrimoines littéraires ou culturels, ou encore de la façon dont est exploitée la littérature scientifique en général.
Les humanités numériques, qui croisent diverses disciplines avec l’informatique, posent comme centrales les questions du volume des données, de leur diversité, de leur origine, de leur véracité ou de leur représentativité. Les informations sont véhiculées au sein de « documents » textuels (livres, pages Web, tweets...), audio, vidéo ou multimédia. Ils peuvent comporter des illustrations ou des graphiques.
Appréhender de telles ressources nécessite le développement d'approches informatiques robustes, capables de passer à l’échelle et adaptées à la nature fondamentalement ambiguë et variée des informations manipulées (langage naturel ou images à interpréter, points de vue multiples…).
Si les approches d’apprentissage statistique sont monnaie courante pour des tâches de classification ou d’extraction d’information, elles doivent faire face à des espaces vectoriels creux et de dimension très élevées (plusieurs millions), être en mesure d’exploiter des ressources (par exemple des lexiques ou des thesaurus) et tenir compte ou produire des annotations sémantiques qui devront pouvoir être réutilisées.
Pour faire face à ces enjeux, des infrastructures ont été créées telle HumaNum à l’échelle nationale, DARIAH ou CLARIN à l’échelle européenne et des recommandations établies à l’échelle mondiale telle que la TEI (Text Encoding Initiative). Des plateformes au service de l’information scientifique comme l’équipement d’excellence OpenEdition.org sont une autre brique essentielle pour la préservation et l’accès aux « Big Digital Humanities » mais aussi pour favoriser la reproductibilité et la compréhension des expérimentations et des résultats obtenus.Introduction to Search Systems - ScaleConf Colombia 2017

Often when a new user arrives on your website, the first place they go to find information is the search box! Whether they are searching for hotels on your travel site, products on your e-commerce site, or friends to connect with on your social media site, it is important to have fast, effective search in order to engage the user.

CSCE181 Big ideas in NLP

Introductory seminar on NLP for CS sophomores. Presented to Texas A&M's Fall 2022 CSCE181 class. Slides are a bit redundant due to compatibility issues :\

Peter Norvig - NYC Machine Learning 2013

The document discusses learning programming through MOOCs and machine learning. It provides data on a MOOC with over 160,000 students from 209 countries. It analyzes student error messages, submissions, and interactions to improve programming instructions. However, programming languages can be ambiguous and students struggle with different concepts. The document advocates for mastery learning through one-on-one tutoring and continual course improvements using data and machine learning.

syntherella feedback synthesizer

This presentation describes a mechanism for synthesizing meaningful concise descriptions for exploring virtual worlds using a screenreader.

Deep Learning Class #0 - You Can Do It

"You Can Do It" by Louis Monier (Altavista Co-Founder & CTO) & Gregory Renard (CTO & Artificial Intelligence Lead Architect at Xbrain) for Deep Learning keynote #0 at Holberton School (http://www.meetup.com/Holberton-School/events/228364522/)
If you want to assist to similar keynote for free, checkout http://www.meetup.com/Holberton-School/

DL Classe 0 - You can do it

Here are some key terms that are similar to "champagne":
- Sparkling wines
- French champagne
- Cognac
- Rosé
- White wine
- Sparkling wine
- Wine
- Burgundy
- Bordeaux
- Cava
- Prosecco
Some specific champagne brands that are similar terms include Moët, Veuve Clicquot, Dom Pérignon, Taittinger, and Bollinger. Grape varieties used in champagne production like Chardonnay and Pinot Noir could also be considered similar terms.

Word2vec ultimate beginner

word2vec beginner.
vector space, distributional semantics, word embedding, vector representation for word, word vector representation, sparse and dense representation, vector representation, Google word2vec, tensorflow

Edutalk f2013

1. The document discusses educational theory and concepts relevant to learning at hacker schools.
2. It promotes three main ideas: that learning is designable like coding, individual brains learn differently, and learning is not an isolated process but relies on community and collaboration.
3. Various learning theories are covered briefly, including cognitive apprenticeship and legitimate peripheral participation within a community of practice. Motivation, mindset, and overcoming challenges are also addressed.

Collegeteaching102

This document provides an overview of strategies for effective college teaching, including facilitating discussions, delivering lectures, assessing student comprehension through testing, and incorporating educational technologies. A variety of specific techniques are presented for each teaching method, with examples and suggestions for implementation. The goal is to help educators engage students and promote learning.

Using binary classifiers

The document provides an overview of machine learning and discusses various concepts related to applying machine learning to real-world problems. It covers topics such as feature extraction, encoding input data, classification vs regression, evaluating model performance, and challenges like overfitting and underfitting models to data. Examples are given for different types of learning problems, including text classification, sentiment analysis, and predicting stock prices.

Translation to QL Part 1

This document introduces the basics of translating statements from natural language to the formal language of Quantified Logic (QL). It explains that QL uses constants to represent singular terms, predicates represented by capital letters, and variables represented by lowercase letters. Quantifiers like "for all" and "there exists" are used to represent statements about properties of individuals or groups. To translate a statement to QL, one must identify whether quantifiers are used, what the universe of discourse is, any singular terms, and the relevant predicates to determine the proper representation using constants, predicates, variables, quantifiers and logical connectives.

Understanding Feature Space in Machine Learning - Data Science Pop-up Seattle

Understanding Feature Space in Machine Learning - Data Science Pop-up Seattle

Maths in the PYP - A Journey through the Arts

Maths in the PYP - A Journey through the Arts

Introduction to LLMs, Prompt Engineering fundamentals,

Introduction to LLMs, Prompt Engineering fundamentals,

[D2 COMMUNITY] Spark User Group - 머신러닝 인공지능 기법

[D2 COMMUNITY] Spark User Group - 머신러닝 인공지능 기법

CO Quadratic Inequalties.pptx

CO Quadratic Inequalties.pptx

Latent dirichlet allocation_and_topic_modeling

Latent dirichlet allocation_and_topic_modeling

Ml3

Ml3

Overview of Machine Learning and Feature Engineering

Overview of Machine Learning and Feature Engineering

Infrastructures et recommandations pour les Humanités Numériques - Big Data e...

Infrastructures et recommandations pour les Humanités Numériques - Big Data e...

Introduction to Search Systems - ScaleConf Colombia 2017

Introduction to Search Systems - ScaleConf Colombia 2017

CSCE181 Big ideas in NLP

CSCE181 Big ideas in NLP

Peter Norvig - NYC Machine Learning 2013

Peter Norvig - NYC Machine Learning 2013

syntherella feedback synthesizer

syntherella feedback synthesizer

Deep Learning Class #0 - You Can Do It

Deep Learning Class #0 - You Can Do It

DL Classe 0 - You can do it

DL Classe 0 - You can do it

Word2vec ultimate beginner

Word2vec ultimate beginner

Edutalk f2013

Edutalk f2013

Collegeteaching102

Collegeteaching102

Using binary classifiers

Using binary classifiers

Translation to QL Part 1

Translation to QL Part 1

Randomised Optimisation Algorithms in DAPHNE

Slides from talk:
Aleš Zamuda: Randomised Optimisation Algorithms in DAPHNE .
Austrian-Slovenian HPC Meeting 2024 – ASHPC24, Seeblickhotel Grundlsee in Austria, 10–13 June 2024
https://ashpc.eu/

waterlessdyeingtechnolgyusing carbon dioxide chemicalspdf

The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.

The debris of the ‘last major merger’ is dynamically young

The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.

11.1 Role of physical biological in deterioration of grains.pdf

Storagedeteriorationisanyformoflossinquantityandqualityofbio-materials.
Themajorcausesofdeteriorationinstorage
•Physical
•Biological
•Mechanical
•Chemical
Storageonlypreservesquality.Itneverimprovesquality.
Itisadvisabletostartstoragewithqualityfoodproduct.Productwithinitialpoorqualityquicklydepreciates

AJAY KUMAR NIET GreNo Guava Project File.pdf

AJAY KUMAR NIET GreNo Guava Project PDF File

Applied Science: Thermodynamics, Laws & Methodology.pdf

When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.

Authoring a personal GPT for your research and practice: How we created the Q...

Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.

Describing and Interpreting an Immersive Learning Case with the Immersion Cub...

Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.

8.Isolation of pure cultures and preservation of cultures.pdf

Isolation of pure culture, its various method.

在线办理(salfor毕业证书)索尔福德大学毕业证毕业完成信一模一样

学校原件一模一样【微信：741003700 】《(salfor毕业证书)索尔福德大学毕业证》【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

Direct Seeded Rice - Climate Smart Agriculture

Direct Seeded Rice - Climate Smart AgricultureInternational Food Policy Research Institute- South Asia Office

PPT on Direct Seeded Rice presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
GBSN - Biochemistry (Unit 6) Chemistry of Proteins

Chemistry of Proteins

The binding of cosmological structures by massless topological defects

Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.

Compexometric titration/Chelatorphy titration/chelating titration

Classification
Metal ion ion indicators
Masking and demasking reagents
Estimation of Magnisium sulphate
Calcium gluconate
Complexometric Titration/ chelatometry titration/chelating titration, introduction, Types-
1.Direct Titration
2.Back Titration
3.Replacement Titration
4.Indirect Titration
Masking agent, Demasking agents
formation of complex
comparition between masking and demasking agents,
Indicators/Metal ion indicators/ Metallochromic indicators/pM indicators,
Visual Technique,PM indicators (metallochromic), Indicators of pH, Redox Indicators
Instrumental Techniques-Photometry
Potentiometry
Miscellaneous methods.
Complex titration with EDTA.

Travis Hills of MN is Making Clean Water Accessible to All Through High Flux ...

By harnessing the power of High Flux Vacuum Membrane Distillation, Travis Hills from MN envisions a future where clean and safe drinking water is accessible to all, regardless of geographical location or economic status.

快速办理(UAM毕业证书)马德里自治大学毕业证学位证一模一样

学校原件一模一样【微信：741003700 】《(UAM毕业证书)马德里自治大学毕业证学位证》【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

Micronuclei test.M.sc.zoology.fisheries.

Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.

molar-distalization in orthodontics-seminar.pptx

orthodontic topic

Basics of crystallography, crystal systems, classes and different forms

Basics of crystallography

Randomised Optimisation Algorithms in DAPHNE

Randomised Optimisation Algorithms in DAPHNE

waterlessdyeingtechnolgyusing carbon dioxide chemicalspdf

waterlessdyeingtechnolgyusing carbon dioxide chemicalspdf

The debris of the ‘last major merger’ is dynamically young

The debris of the ‘last major merger’ is dynamically young

11.1 Role of physical biological in deterioration of grains.pdf

11.1 Role of physical biological in deterioration of grains.pdf

AJAY KUMAR NIET GreNo Guava Project File.pdf

AJAY KUMAR NIET GreNo Guava Project File.pdf

Applied Science: Thermodynamics, Laws & Methodology.pdf

Applied Science: Thermodynamics, Laws & Methodology.pdf

Authoring a personal GPT for your research and practice: How we created the Q...

Authoring a personal GPT for your research and practice: How we created the Q...

Describing and Interpreting an Immersive Learning Case with the Immersion Cub...

Describing and Interpreting an Immersive Learning Case with the Immersion Cub...

Juaristi, Jon. - El canon espanol. El legado de la cultura española a la civi...

Juaristi, Jon. - El canon espanol. El legado de la cultura española a la civi...

8.Isolation of pure cultures and preservation of cultures.pdf

8.Isolation of pure cultures and preservation of cultures.pdf

在线办理(salfor毕业证书)索尔福德大学毕业证毕业完成信一模一样

在线办理(salfor毕业证书)索尔福德大学毕业证毕业完成信一模一样

Direct Seeded Rice - Climate Smart Agriculture

Direct Seeded Rice - Climate Smart Agriculture

GBSN - Biochemistry (Unit 6) Chemistry of Proteins

GBSN - Biochemistry (Unit 6) Chemistry of Proteins

The binding of cosmological structures by massless topological defects

The binding of cosmological structures by massless topological defects

Compexometric titration/Chelatorphy titration/chelating titration

Compexometric titration/Chelatorphy titration/chelating titration

Travis Hills of MN is Making Clean Water Accessible to All Through High Flux ...

Travis Hills of MN is Making Clean Water Accessible to All Through High Flux ...

快速办理(UAM毕业证书)马德里自治大学毕业证学位证一模一样

快速办理(UAM毕业证书)马德里自治大学毕业证学位证一模一样

Micronuclei test.M.sc.zoology.fisheries.

Micronuclei test.M.sc.zoology.fisheries.

molar-distalization in orthodontics-seminar.pptx

molar-distalization in orthodontics-seminar.pptx

Basics of crystallography, crystal systems, classes and different forms

Basics of crystallography, crystal systems, classes and different forms

- 1. Understanding Feature Space in Machine Learning Alice Zheng, Dato September 9, 2015 1
- 2. 2 My journey so far Applied machine learning (Data science) Build ML tools Shortage of experts and good tools.
- 3. 3 Why machine learning? Model data. Make predictions. Build intelligent applications.
- 4. 4 The machine learning pipeline I fell in love the instant I laid my eyes on that puppy. His big eyes and playful tail, his soft furry paws, … Raw data Features Models Predictions Deploy in production
- 5. Feature = numeric representation of raw data
- 6. 6 Representing natural text It is a puppy and it is extremely cute. What’s important? Phrases? Specific words? Ordering? Subject, object, verb? Classify: puppy or not? Raw Text {“it”:2, “is”:2, “a”:1, “puppy”:1, “and”:1, “extremely”:1, “cute”:1 } Bag of Words
- 7. 7 Representing natural text It is a puppy and it is extremely cute. Classify: puppy or not? Raw Text Bag of Words it 2 they 0 I 1 am 0 how 0 puppy 1 and 1 cat 0 aardvark 0 cute 1 extremely 1 … … Sparse vector representation
- 8. 8 Representing images Image source: “Recognizing and learning object categories,” Li Fei-Fei, Rob Fergus, Anthony Torralba, ICCV 2005—2009. Raw image: millions of RGB triplets, one for each pixel Classify: person or animal? Raw Image Bag of Visual Words
- 9. 9 Representing images Classify: person or animal? Raw Image Deep learning features 3.29 -15 -5.24 48.3 1.36 47.1 - 1.92 36.5 2.83 95.4 -19 -89 5.09 37.8 Dense vector representation
- 10. 10 Feature space in machine learning • Raw data high dimensional vectors • Collection of data points point cloud in feature space • Model = geometric summary of point cloud • Feature engineering = creating features of the appropriate granularity for the task
- 11. Crudely speaking, mathematicians fall into two categories: the algebraists, who find it easiest to reduce all problems to sets of numbers and variables, and the geometers, who understand the world through shapes. -- Masha Gessen, “Perfect Rigor”
- 12. 12 Algebra vs. Geometry a b c a2 + b2 = c2 Algebra Geometry Pythagorean Theorem (Euclidean space)
- 13. 13 Visualizing a sphere in 2D x2 + y2 = 1 a b c Pythagorean theorem: a2 + b2 = c2 x y 1 1
- 14. 14 Visualizing a sphere in 3D x2 + y2 + z2 = 1 x y z 1 1 1
- 15. 15 Visualizing a sphere in 4D x2 + y2 + z2 + t2 = 1 x y z 1 1 1
- 16. 16 Why are we looking at spheres? = = = = Poincaré Conjecture: All physical objects without holes is “equivalent” to a sphere.
- 17. 17 The power of higher dimensions • A sphere in 4D can model the birth and death process of physical objects • Point clouds = approximate geometric shapes • High dimensional features can model many things
- 19. 19 The challenge of high dimension geometry • Feature space can have hundreds to millions of dimensions • In high dimensions, our geometric imagination is limited - Algebra comes to our aid
- 20. 20 Visualizing bag-of-words puppy cute 1 1 I have a puppy and it is extremely cute I have a puppy and it is extremely cute it 1 they 0 I 1 am 0 how 0 puppy 1 and 1 cat 0 aardvark 0 zebra 0 cute 1 extremely 1 … …
- 21. 21 Visualizing bag-of-words puppy cute 1 1 1 extremely I have a puppy and it is extremely cute I have an extremely cute cat I have a cute puppy
- 22. 22 Document point cloud word 1 word 2
- 23. 23 What is a model? • Model = mathematical “summary” of data • What’s a summary? - A geometric shape
- 24. 24 Classification model Feature 2 Feature 1 Decide between two classes
- 25. 25 Clustering model Feature 2 Feature 1 Group data points tightly
- 26. 26 Regression model Target Feature Fit the target values
- 28. 28 When does bag-of-words fail? puppy cat 2 1 1 have I have a puppy I have a cat I have a kitten Task: find a surface that separates documents about dogs vs. cats Problem: the word “have” adds fluff instead of information I have a dog and I have a pen 1
- 29. 29 Improving on bag-of-words • Idea: “normalize” word counts so that popular words are discounted • Term frequency (tf) = Number of times a terms appears in a document • Inverse document frequency of word (idf) = • N = total number of documents • Tf-idf count = tf x idf
- 30. 30 From BOW to tf-idf puppy cat 2 1 1 have I have a puppy I have a cat I have a kitten idf(puppy) = log 4 idf(cat) = log 4 idf(have) = log 1 = 0 I have a dog and I have a pen 1
- 31. 31 From BOW to tf-idf puppy cat1 have tfidf(puppy) = log 4 tfidf(cat) = log 4 tfidf(have) = 0 I have a dog and I have a pen, I have a kitten 1 log 4 log 4 I have a cat I have a puppy Decision surface Tf-idf flattens uninformative dimensions in the BOW point cloud
- 32. 32 Entry points of feature engineering • Start from data and task - What’s the best text representation for classification? • Start from modeling method - What kind of features does k-means assume? - What does linear regression assume about the data?
- 33. 33 That’s not all, folks! • There’s a lot more to feature engineering: - Feature normalization - Feature transformations - “Regularizing” models - Learning the right features • Dato is hiring! jobs@dato.com alicez@dato.com @RainyData

- Features sit between raw data and model. They can make or break an application.