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• Leave a review - let other readers know what you think
• Other Books You May Enjoy
• Summary
• Q-learning using a neural network
• Q-learning in the checkerboard environment
• Q-learning

• Leave a review - let other readers know what you think 更新時間：2021-06-25 22:08:15
• Other Books You May Enjoy
• Summary
• Q-learning using a neural network
• Q-learning in the checkerboard environment
• Q-learning
• SARSA in the checkerboard environment
• SARSA algorithm
• Actor-Critic TD(0) in the checkerboard environment
• TD(λ) in a more complex Checkerboard environment
• TD(λ) algorithm
• Advanced Policy Estimation Algorithms
• Summary
• TD(0) in the checkerboard environment
• TD(0) algorithm
• Value iteration in the checkerboard environment
• Value iteration
• Policy iteration in the checkerboard environment
• Policy iteration
• Policy
• Checkerboard environment in Python
• Rewards
• Environment
• Reinforcement Learning fundamentals
• Introduction to Reinforcement Learning
• Summary
• Example of Supervised DBN with Python
• Example of unsupervised DBN in Python
• DBNs
• RBMs
• MRF
• Deep Belief Networks
• Summary
• Example of WGAN with TensorFlow
• Wasserstein GAN (WGAN)
• Example of DCGAN with TensorFlow
• Adversarial training
• Generative Adversarial Networks
• Summary
• An example of a variational autoencoder with TensorFlow
• Variational autoencoders
• Adding sparseness to the Fashion MNIST deep convolutional autoencoder
• Sparse autoencoders
• An example of a denoising autoencoder with TensorFlow
• Denoising autoencoders
• An example of a deep convolutional autoencoder with TensorFlow
• Autoencoders
• Autoencoders
• Summary
• Transfer learning
• Example of an LSTM network with Keras
• GRU
• LSTM
• Backpropagation through time (BPTT)
• Recurrent networks
• Example of a deep convolutional network with Keras and data augmentation
• Examples of deep convolutional networks with Keras
• Other useful layers
• Pooling layers
• Transpose convolution
• Separable convolution
• Atrous convolution
• Strides and padding
• Bidimensional discrete convolutions
• Convolutions
• Deep convolutional networks
• Advanced Neural Models
• Summary
• Example of batch normalization with Keras
• Batch normalization
• Example of dropout with Keras
• Dropout
• Regularization and dropout
• AdaDelta with Keras
• AdaDelta
• AdaGrad with Keras
• AdaGrad
• Adam with Keras
• Adam
• RMSProp with Keras
• RMSProp
• SGD with momentum in Keras
• Momentum and Nesterov momentum
• Gradient perturbation
• Optimization algorithms
• Example of MLP with Keras
• Weight initialization
• Stochastic gradient descent
• Back-propagation algorithm
• Softmax
• Rectifier activation functions
• Sigmoid and hyperbolic tangent
• Activation functions
• Multilayer perceptrons
• Example of a perceptron with Scikit-Learn
• Perceptron
• The basic artificial neuron
• Neural Networks for Machine Learning
• Summary
• Ensemble learning as model selection
• Example of voting classifiers with Scikit-Learn
• Ensembles of voting classifiers
• Example of gradient tree boosting with Scikit-Learn
• Gradient boosting
• Example of AdaBoost with Scikit-Learn
• AdaBoost.R2
• AdaBoost.SAMME.R
• AdaBoost.SAMME
• AdaBoost
• Example of random forest with Scikit-Learn
• Random forests
• Ensemble learning fundamentals
• Ensemble Learning
• Summary
• Example of spectral clustering with Scikit-Learn
• Spectral clustering
• Example of fuzzy C-means with Scikit-Fuzzy
• Fuzzy C-means
• Silhouette score
• Adjusted Rand Index
• Completeness score
• Homogeneity score
• Evaluation metrics
• Example of K-means with Scikit-Learn
• K-means++
• K-means
• Example of KNN with Scikit-Learn
• Ball Trees
• KD Trees
• k-Nearest Neighbors
• Clustering Algorithms
• Summary
• Example of SOM
• Self-organizing maps
• Example of Rubner-Tavan's network
• Rubner-Tavan's network
• Example of Sanger's network
• Sanger's network
• Weight vector stabilization and Oja's rule
• Example of covariance rule application
• Analysis of the covariance rule
• Hebb's rule
• Hebbian Learning and Self-Organizing Maps
• Summary
• Addendum to HMMs
• An example of FastICA with Scikit-Learn
• Independent component analysis
• An example of PCA with Scikit-Learn
• Principal Component Analysis
• An example of factor analysis with Scikit-Learn
• Factor analysis
• An example of Gaussian Mixtures using Scikit-Learn
• Gaussian mixture
• An example of parameter estimation
• EM algorithm
• MLE and MAP learning
• EM Algorithm and Applications
• Summary
• Finding the most likely hidden state sequence with hmmlearn
• Viterbi algorithm
• Example of HMM training with hmmlearn
• HMM parameter estimation
• Backward phase
• Forward phase
• Forward-backward algorithm
• Hidden Markov Models (HMMs)
• Sampling example using PyMC3
• Example of Metropolis-Hastings sampling
• Metropolis-Hastings sampling
• Gibbs sampling
• A gentle introduction to Markov chains
• Example of direct sampling
• Direct sampling
• Sampling from a Bayesian network
• Bayesian networks
• Conditional probabilities and Bayes' theorem
• Bayesian Networks and Hidden Markov Models
• Summary
• Example of t-distributed stochastic neighbor embedding
• t-SNE
• Example of Laplacian Spectral Embedding
• Laplacian Spectral Embedding
• Example of locally linear embedding
• Locally linear embedding
• Example of Isomap
• Isomap
• Manifold learning
• Example of label propagation based on Markov random walks
• Label propagation based on Markov random walks
• Example of label spreading
• Label spreading
• Label propagation in Scikit-Learn
• Example of label propagation
• Label propagation
• Graph-Based Semi-Supervised Learning
• Summary
• Example of TSVM
• Transductive Support Vector Machines (TSVM)
• Example of S3VM
• Semi-supervised Support Vector Machines (S3VM)
• Example of contrastive pessimistic likelihood estimation
• Contrastive pessimistic likelihood estimation
• Weighted log-likelihood
• Example of a generative Gaussian mixture
• Generative Gaussian mixtures
• Manifold assumption
• Cluster assumption
• Smoothness assumption
• Semi-supervised assumptions
• Inductive learning
• Transductive learning
• Semi-supervised scenario
• Introduction to Semi-Supervised Learning
• Summary
• Early stopping
• ElasticNet
• Lasso
• Ridge
• Regularization
• Categorical cross-entropy
• Hinge cost function
• Huber cost function
• Mean squared error
• Examples of cost functions
• Loss and cost functions
• The Cramér-Rao bound
• Overfitting
• Variance of an estimator
• Underfitting
• Bias of an estimator
• Vapnik-Chervonenkis capacity
• Capacity of a model
• Features of a machine learning model
• Cross-validation
• Training and validation sets
• Zero-centering and whitening
• Models and data
• Machine Learning Model Fundamentals
• Reviews
• Get in touch
• Conventions used
• Download the color images
• Download the example code files
• To get the most out of this book
• What this book covers
• Who this book is for
• Preface
• Packt is searching for authors like you
• About the reviewer
• About the author
• Contributors
• PacktPub.com
• Why subscribe?
• Packt Upsell
• Dedication
• 版權信息
• 封面

• 封面
• 版權信息
• Dedication
• Packt Upsell
• Why subscribe?
• PacktPub.com
• Contributors
• About the author
• About the reviewer
• Packt is searching for authors like you
• Preface
• Who this book is for
• What this book covers
• To get the most out of this book
• Download the example code files
• Download the color images
• Conventions used
• Get in touch
• Reviews
• Machine Learning Model Fundamentals
• Models and data
• Zero-centering and whitening
• Training and validation sets
• Cross-validation
• Features of a machine learning model
• Capacity of a model
• Vapnik-Chervonenkis capacity
• Bias of an estimator
• Underfitting
• Variance of an estimator
• Overfitting
• The Cramér-Rao bound
• Loss and cost functions
• Examples of cost functions
• Mean squared error
• Huber cost function
• Hinge cost function
• Categorical cross-entropy
• Regularization
• Ridge
• Lasso
• ElasticNet
• Early stopping
• Summary
• Introduction to Semi-Supervised Learning
• Semi-supervised scenario
• Transductive learning
• Inductive learning
• Semi-supervised assumptions
• Smoothness assumption
• Cluster assumption
• Manifold assumption
• Generative Gaussian mixtures
• Example of a generative Gaussian mixture
• Weighted log-likelihood
• Contrastive pessimistic likelihood estimation
• Example of contrastive pessimistic likelihood estimation
• Semi-supervised Support Vector Machines (S3VM)
• Example of S3VM
• Transductive Support Vector Machines (TSVM)
• Example of TSVM
• Summary
• Graph-Based Semi-Supervised Learning
• Label propagation
• Example of label propagation
• Label propagation in Scikit-Learn
• Label spreading
• Example of label spreading
• Label propagation based on Markov random walks
• Example of label propagation based on Markov random walks
• Manifold learning
• Isomap
• Example of Isomap
• Locally linear embedding
• Example of locally linear embedding
• Laplacian Spectral Embedding
• Example of Laplacian Spectral Embedding
• t-SNE
• Example of t-distributed stochastic neighbor embedding
• Summary
• Bayesian Networks and Hidden Markov Models
• Conditional probabilities and Bayes' theorem
• Bayesian networks
• Sampling from a Bayesian network
• Direct sampling
• Example of direct sampling
• A gentle introduction to Markov chains
• Gibbs sampling
• Metropolis-Hastings sampling
• Example of Metropolis-Hastings sampling
• Sampling example using PyMC3
• Hidden Markov Models (HMMs)
• Forward-backward algorithm
• Forward phase
• Backward phase
• HMM parameter estimation
• Example of HMM training with hmmlearn
• Viterbi algorithm
• Finding the most likely hidden state sequence with hmmlearn
• Summary
• EM Algorithm and Applications
• MLE and MAP learning
• EM algorithm
• An example of parameter estimation
• Gaussian mixture
• An example of Gaussian Mixtures using Scikit-Learn
• Factor analysis
• An example of factor analysis with Scikit-Learn
• Principal Component Analysis
• An example of PCA with Scikit-Learn
• Independent component analysis
• An example of FastICA with Scikit-Learn
• Addendum to HMMs
• Summary
• Hebbian Learning and Self-Organizing Maps
• Hebb's rule
• Analysis of the covariance rule
• Example of covariance rule application
• Weight vector stabilization and Oja's rule
• Sanger's network
• Example of Sanger's network
• Rubner-Tavan's network
• Example of Rubner-Tavan's network
• Self-organizing maps
• Example of SOM
• Summary
• Clustering Algorithms
• k-Nearest Neighbors
• KD Trees
• Ball Trees
• Example of KNN with Scikit-Learn
• K-means
• K-means++
• Example of K-means with Scikit-Learn
• Evaluation metrics
• Homogeneity score
• Completeness score
• Adjusted Rand Index
• Silhouette score
• Fuzzy C-means
• Example of fuzzy C-means with Scikit-Fuzzy
• Spectral clustering
• Example of spectral clustering with Scikit-Learn
• Summary
• Ensemble Learning
• Ensemble learning fundamentals
• Random forests
• Example of random forest with Scikit-Learn
• AdaBoost
• AdaBoost.SAMME
• AdaBoost.SAMME.R
• AdaBoost.R2
• Example of AdaBoost with Scikit-Learn
• Gradient boosting
• Example of gradient tree boosting with Scikit-Learn
• Ensembles of voting classifiers
• Example of voting classifiers with Scikit-Learn
• Ensemble learning as model selection
• Summary
• Neural Networks for Machine Learning
• The basic artificial neuron
• Perceptron
• Example of a perceptron with Scikit-Learn
• Multilayer perceptrons
• Activation functions
• Sigmoid and hyperbolic tangent
• Rectifier activation functions
• Softmax
• Back-propagation algorithm
• Stochastic gradient descent
• Weight initialization
• Example of MLP with Keras
• Optimization algorithms
• Gradient perturbation
• Momentum and Nesterov momentum
• SGD with momentum in Keras
• RMSProp
• RMSProp with Keras
• Adam
• Adam with Keras
• AdaGrad
• AdaGrad with Keras
• AdaDelta
• AdaDelta with Keras
• Regularization and dropout
• Dropout
• Example of dropout with Keras
• Batch normalization
• Example of batch normalization with Keras
• Summary
• Advanced Neural Models
• Deep convolutional networks
• Convolutions
• Bidimensional discrete convolutions
• Strides and padding
• Atrous convolution
• Separable convolution
• Transpose convolution
• Pooling layers
• Other useful layers
• Examples of deep convolutional networks with Keras
• Example of a deep convolutional network with Keras and data augmentation
• Recurrent networks
• Backpropagation through time (BPTT)
• LSTM
• GRU
• Example of an LSTM network with Keras
• Transfer learning
• Summary
• Autoencoders
• Autoencoders
• An example of a deep convolutional autoencoder with TensorFlow
• Denoising autoencoders
• An example of a denoising autoencoder with TensorFlow
• Sparse autoencoders
• Adding sparseness to the Fashion MNIST deep convolutional autoencoder
• Variational autoencoders
• An example of a variational autoencoder with TensorFlow
• Summary
• Generative Adversarial Networks
• Adversarial training
• Example of DCGAN with TensorFlow
• Wasserstein GAN (WGAN)
• Example of WGAN with TensorFlow
• Summary
• Deep Belief Networks
• MRF
• RBMs
• DBNs
• Example of unsupervised DBN in Python
• Example of Supervised DBN with Python
• Summary
• Introduction to Reinforcement Learning
• Reinforcement Learning fundamentals
• Environment
• Rewards
• Checkerboard environment in Python
• Policy
• Policy iteration
• Policy iteration in the checkerboard environment
• Value iteration
• Value iteration in the checkerboard environment
• TD(0) algorithm
• TD(0) in the checkerboard environment
• Summary
• Advanced Policy Estimation Algorithms
• TD(λ) algorithm
• TD(λ) in a more complex Checkerboard environment
• Actor-Critic TD(0) in the checkerboard environment
• SARSA algorithm
• SARSA in the checkerboard environment
• Q-learning
• Q-learning in the checkerboard environment
• Q-learning using a neural network
• Summary
• Other Books You May Enjoy
• Leave a review - let other readers know what you think 更新時間：2021-06-25 22:08:15