最新章節

• Summary
• Further reading
• Robo soccer
• Google DeepMind's AlphaGo
• Automotive vehicle control - self-driving cars
• Applications of reinforcement learning with integration of machine learning and deep learning

• Summary 更新時間：2021-07-02 19:06:32
• Further reading
• Robo soccer
• Google DeepMind's AlphaGo
• Automotive vehicle control - self-driving cars
• Applications of reinforcement learning with integration of machine learning and deep learning
• Cliff walking example of on-policy and off-policy of TD control
• Q-learning - off-policy TD control
• SARSA on-policy TD control
• Driving office example for TD learning
• TD prediction
• Comparison between Monte Carlo methods and temporal difference learning
• Temporal difference learning
• Modeling Blackjack example of Monte Carlo methods using Python
• The suitability of Monte Carlo prediction on grid-world problems
• Monte Carlo prediction
• Key advantages of MC over DP methods
• Comparison between dynamic programming and Monte Carlo methods
• Monte Carlo methods
• Grid world example using value and policy iteration algorithms with basic Python
• Algorithms to compute optimal policy using dynamic programming
• Dynamic programming
• Markov decision processes and Bellman equations
• Fundamental categories in sequential decision making
• Category 5 - model-based
• Category 4 - model-free
• Category 3 - actor-critic
• Category 2 - policy based
• Category 1 - value based
• Reinforcement learning basics
• Characteristics of reinforcement learning
• Comparing supervised unsupervised and reinforcement learning in detail
• Introduction to reinforcement learning
• Reinforcement Learning
• Summary
• Deep auto encoders applied on handwritten digits using Keras
• Model building technique using encoder-decoder architecture
• Deep auto encoders
• SVD applied on handwritten digits using scikit-learn
• Singular value decomposition - SVD
• PCA applied on handwritten digits using scikit-learn
• PCA working methodology from first principles
• Principal component analysis - PCA
• K-means clustering with the iris data example
• The elbow method
• Optimal number of clusters and cluster evaluation
• K-means working methodology from first principles
• K-means clustering
• Unsupervised Learning
• Summary
• Grid search on collaborative filtering
• Collaborative filtering using ALS
• Movie-movie similarity matrix
• User-user similarity matrix
• Recommendation engine application on movie lens data
• Hyperparameter selection in recommendation engines using grid search
• Evaluation of recommendation engine model
• Matrix factorization using the alternating least squares algorithm for collaborative filtering
• Advantages of collaborative filtering over content-based filtering
• Collaborative filtering
• Cosine similarity
• Content-based filtering
• Recommendation Engines
• Summary
• Deep neural network classifier applied on handwritten digits using Keras
• Deep learning software
• Solving methodology
• Introduction to deep learning
• ANN classifier applied on handwritten digits using scikit-learn
• Dropout in neural networks
• Limited-memory broyden-fletcher-goldfarb-shanno - L-BFGS optimization algorithm
• Adaptive moment estimation - Adam
• RMSprop
• Adadelta
• Adagrad
• Nesterov accelerated gradient - NAG
• Momentum
• Stochastic gradient descent - SGD
• Optimization of neural networks
• Forward propagation and backpropagation
• Activation functions
• Artificial neural networks - ANN
• RBF kernel
• Polynomial kernel
• Maximum margin classifier - linear kernel
• SVM multilabel classifier with letter recognition data example
• Kernel functions
• Support vector machines
• Support vector classifier
• Maximum margin classifier
• Support vector machines working principles
• Support Vector Machines and Neural Networks
• Summary
• Naive Bayes SMS spam classification example
• Laplace estimator
• Naive Bayes classification
• Understanding Bayes theorem with conditional probability
• Joint probability
• Probability fundamentals
• Naive Bayes
• Tuning of k-value in KNN classifier
• KNN classifier with breast cancer Wisconsin data example
• Curse of dimensionality with 1D 2D and 3D example
• Curse of dimensionality
• KNN voter example
• K-nearest neighbors
• K-Nearest Neighbors and Naive Bayes
• Summary
• Ensemble of ensembles with bootstrap samples using a single type of classifier
• Ensemble of ensembles with different types of classifiers
• Ensemble of ensembles - model stacking
• Extreme gradient boosting - XGBoost classifier
• Comparison between AdaBoosting versus gradient boosting
• Gradient boosting classifier
• AdaBoost classifier
• Random forest classifier - grid search
• Random forest classifier
• Bagging classifier
• Tuning class weights in decision tree classifier
• Decision tree classifier
• HR attrition data example
• Remedial actions to push the model towards the ideal region
• Comparison of error components across various styles of models
• Comparison between logistic regression and decision trees
• Decision tree working methodology from first principles
• Terminology used in decision trees
• Introducing decision tree classifiers
• Tree-Based Machine Learning Models
• Summary
• Comparison of logistic regression with random forest
• Variable importance plot
• Grid search on random forest
• Example of random forest using German credit data
• Random forest
• Example of logistic regression using German credit data
• Applying steps in logistic regression modeling
• Terminology involved in logistic regression
• Logistic regression – introduction and advantages
• Maximum likelihood estimation
• Logistic Regression Versus Random Forest
• Summary
• Regularization parameters in linear regression and ridge/lasso regression
• Example of lasso regression machine learning model
• Example of ridge regression machine learning
• Machine learning models - ridge and lasso regression
• Backward and forward selection
• Example of multilinear regression - step-by-step methodology of model building
• Example of simple linear regression using the wine quality data
• Example of simple linear regression from first principles
• Steps applied in linear regression modeling
• Assumptions of linear regression
• Compensating factors in machine learning models
• Comparison between regression and machine learning models
• Parallelism of Statistics and Machine Learning
• Summary
• Machine learning model overview
• Grid search
• Cross-validation
• Train validation and test data
• When to stop tuning machine learning models
• Machine learning losses
• Linear regression versus gradient descent
• Machine learning terminology for model building and validation
• Train and test data
• Bias versus variance trade-off
• Statistical fundamentals and terminology for model building and validation
• Steps in machine learning model development and deployment
• Major differences between statistical modeling and machine learning
• Machine learning
• Statistical terminology for model building and validation
• Journey from Statistics to Machine Learning
• Questions
• Piracy
• Errata
• Downloading the color images of this book
• Downloading the example code
• Customer support
• Reader feedback
• Conventions
• Who this book is for
• What you need for this book
• What this book covers
• Preface
• Customer Feedback
• Why subscribe?
• www.PacktPub.com
• About the Reviewer
• About the Author
• Credits
• Title Page
• coverpage

• coverpage
• Title Page
• Credits
• About the Author
• About the Reviewer
• www.PacktPub.com
• Why subscribe?
• Customer Feedback
• Preface
• What this book covers
• What you need for this book
• Who this book is for
• Conventions
• Reader feedback
• Customer support
• Downloading the example code
• Downloading the color images of this book
• Errata
• Piracy
• Questions
• Journey from Statistics to Machine Learning
• Statistical terminology for model building and validation
• Machine learning
• Major differences between statistical modeling and machine learning
• Steps in machine learning model development and deployment
• Statistical fundamentals and terminology for model building and validation
• Bias versus variance trade-off
• Train and test data
• Machine learning terminology for model building and validation
• Linear regression versus gradient descent
• Machine learning losses
• When to stop tuning machine learning models
• Train validation and test data
• Cross-validation
• Grid search
• Machine learning model overview
• Summary
• Parallelism of Statistics and Machine Learning
• Comparison between regression and machine learning models
• Compensating factors in machine learning models
• Assumptions of linear regression
• Steps applied in linear regression modeling
• Example of simple linear regression from first principles
• Example of simple linear regression using the wine quality data
• Example of multilinear regression - step-by-step methodology of model building
• Backward and forward selection
• Machine learning models - ridge and lasso regression
• Example of ridge regression machine learning
• Example of lasso regression machine learning model
• Regularization parameters in linear regression and ridge/lasso regression
• Summary
• Logistic Regression Versus Random Forest
• Maximum likelihood estimation
• Logistic regression – introduction and advantages
• Terminology involved in logistic regression
• Applying steps in logistic regression modeling
• Example of logistic regression using German credit data
• Random forest
• Example of random forest using German credit data
• Grid search on random forest
• Variable importance plot
• Comparison of logistic regression with random forest
• Summary
• Tree-Based Machine Learning Models
• Introducing decision tree classifiers
• Terminology used in decision trees
• Decision tree working methodology from first principles
• Comparison between logistic regression and decision trees
• Comparison of error components across various styles of models
• Remedial actions to push the model towards the ideal region
• HR attrition data example
• Decision tree classifier
• Tuning class weights in decision tree classifier
• Bagging classifier
• Random forest classifier
• Random forest classifier - grid search
• AdaBoost classifier
• Gradient boosting classifier
• Comparison between AdaBoosting versus gradient boosting
• Extreme gradient boosting - XGBoost classifier
• Ensemble of ensembles - model stacking
• Ensemble of ensembles with different types of classifiers
• Ensemble of ensembles with bootstrap samples using a single type of classifier
• Summary
• K-Nearest Neighbors and Naive Bayes
• K-nearest neighbors
• KNN voter example
• Curse of dimensionality
• Curse of dimensionality with 1D 2D and 3D example
• KNN classifier with breast cancer Wisconsin data example
• Tuning of k-value in KNN classifier
• Naive Bayes
• Probability fundamentals
• Joint probability
• Understanding Bayes theorem with conditional probability
• Naive Bayes classification
• Laplace estimator
• Naive Bayes SMS spam classification example
• Summary
• Support Vector Machines and Neural Networks
• Support vector machines working principles
• Maximum margin classifier
• Support vector classifier
• Support vector machines
• Kernel functions
• SVM multilabel classifier with letter recognition data example
• Maximum margin classifier - linear kernel
• Polynomial kernel
• RBF kernel
• Artificial neural networks - ANN
• Activation functions
• Forward propagation and backpropagation
• Optimization of neural networks
• Stochastic gradient descent - SGD
• Momentum
• Nesterov accelerated gradient - NAG
• Adagrad
• Adadelta
• RMSprop
• Adaptive moment estimation - Adam
• Limited-memory broyden-fletcher-goldfarb-shanno - L-BFGS optimization algorithm
• Dropout in neural networks
• ANN classifier applied on handwritten digits using scikit-learn
• Introduction to deep learning
• Solving methodology
• Deep learning software
• Deep neural network classifier applied on handwritten digits using Keras
• Summary
• Recommendation Engines
• Content-based filtering
• Cosine similarity
• Collaborative filtering
• Advantages of collaborative filtering over content-based filtering
• Matrix factorization using the alternating least squares algorithm for collaborative filtering
• Evaluation of recommendation engine model
• Hyperparameter selection in recommendation engines using grid search
• Recommendation engine application on movie lens data
• User-user similarity matrix
• Movie-movie similarity matrix
• Collaborative filtering using ALS
• Grid search on collaborative filtering
• Summary
• Unsupervised Learning
• K-means clustering
• K-means working methodology from first principles
• Optimal number of clusters and cluster evaluation
• The elbow method
• K-means clustering with the iris data example
• Principal component analysis - PCA
• PCA working methodology from first principles
• PCA applied on handwritten digits using scikit-learn
• Singular value decomposition - SVD
• SVD applied on handwritten digits using scikit-learn
• Deep auto encoders
• Model building technique using encoder-decoder architecture
• Deep auto encoders applied on handwritten digits using Keras
• Summary
• Reinforcement Learning
• Introduction to reinforcement learning
• Comparing supervised unsupervised and reinforcement learning in detail
• Characteristics of reinforcement learning
• Reinforcement learning basics
• Category 1 - value based
• Category 2 - policy based
• Category 3 - actor-critic
• Category 4 - model-free
• Category 5 - model-based
• Fundamental categories in sequential decision making
• Markov decision processes and Bellman equations
• Dynamic programming
• Algorithms to compute optimal policy using dynamic programming
• Grid world example using value and policy iteration algorithms with basic Python
• Monte Carlo methods
• Comparison between dynamic programming and Monte Carlo methods
• Key advantages of MC over DP methods
• Monte Carlo prediction
• The suitability of Monte Carlo prediction on grid-world problems
• Modeling Blackjack example of Monte Carlo methods using Python
• Temporal difference learning
• Comparison between Monte Carlo methods and temporal difference learning
• TD prediction
• Driving office example for TD learning
• SARSA on-policy TD control
• Q-learning - off-policy TD control
• Cliff walking example of on-policy and off-policy of TD control
• Applications of reinforcement learning with integration of machine learning and deep learning
• Automotive vehicle control - self-driving cars
• Google DeepMind's AlphaGo
• Robo soccer
• Further reading
• Summary 更新時間：2021-07-02 19:06:32