Python : Expert Machine Learning Systems and Intelligent Agents Using Python.

Cover -- Title Page -- Copyright and Credits -- About Packt -- Contributors -- Table of Contents -- Preface -- Chapter 1: 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 -- Chapter 2: 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 -- Chapter 3: 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.

Chapter 4: 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 -- Chapter 5: 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 -- Chapter 6: 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 -- Chapter 7: 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.

Chapter 8: 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 -- Chapter 9: Classical Machine Learning with TensorFlow -- Simple linear regression -- Data preparation -- Building a simple regression model -- Defining the inputs, parameters, and other variables -- Defining the model -- Defining the loss function -- Defining the optimizer function -- Training the model -- Using the trained model to predict -- Multi-regression -- Regularized regression -- Lasso regularization -- Ridge regularization -- ElasticNet regularization -- Classification using logistic regression -- Logistic regression for binary classification -- Logistic regression for multiclass classification -- Binary classification -- Multiclass classification -- Summary -- Chapter 10: Neural Networks and MLP with TensorFlow and Keras -- The perceptron -- MultiLayer Perceptron -- MLP for image classification -- TensorFlow-based MLP for MNIST classification -- Keras-based MLP for MNIST classification -- TFLearn-based MLP for MNIST classification -- Summary of MLP with TensorFlow, Keras, and TFLearn -- MLP for time series regression -- Summary -- Chapter 11: RNN with TensorFlow and Keras -- Simple Recurrent Neural Network -- RNN variants -- LSTM network -- GRU network -- TensorFlow for RNN -- TensorFlow RNN Cell Classes -- TensorFlow RNN Model Construction Classes -- TensorFlow RNN Cell Wrapper Classes -- Keras for RNN.

Application areas of RNNs -- RNN in Keras for MNIST data -- Summary -- Chapter 12: CNN with TensorFlow and Keras -- Understanding convolution -- Understanding pooling -- CNN architecture pattern - LeNet -- LeNet for MNIST data -- LeNet CNN for MNIST with TensorFlow -- LeNet CNN for MNIST with Keras -- LeNet for CIFAR10 Data -- ConvNets for CIFAR10 with TensorFlow -- ConvNets for CIFAR10 with Keras -- Summary -- Chapter 13: Autoencoder with TensorFlow and Keras -- Autoencoder types -- Stacked autoencoder in TensorFlow -- Stacked autoencoder in Keras -- Denoising autoencoder in TensorFlow -- Denoising autoencoder in Keras -- Variational autoencoder in TensorFlow -- Variational autoencoder in Keras -- Summary -- Chapter 14: TensorFlow Models in Production with TF Serving -- Saving and Restoring models in TensorFlow -- Saving and restoring all graph variables with the saver class -- Saving and restoring selected  variables with the saver class -- Saving and restoring Keras models -- TensorFlow Serving -- Installing TF Serving -- Saving models for TF Serving -- Serving models with TF Serving -- TF Serving in the Docker containers -- Installing Docker -- Building a Docker image for TF serving -- Serving the model in the Docker container -- TensorFlow Serving on Kubernetes -- Installing Kubernetes -- Uploading the Docker image to the dockerhub -- Deploying in Kubernetes -- Summary -- Chapter 15: Deep Reinforcement Learning -- OpenAI Gym 101 -- Applying simple policies to a cartpole game -- Reinforcement learning 101 -- Q function (learning to optimize when the model is not available) -- Exploration and exploitation in the RL algorithms -- V function (learning to optimize when the model is available) -- Reinforcement learning techniques -- Naive Neural Network policy for Reinforcement Learning -- Implementing Q-Learning.

Initializing and discretizing for Q-Learning -- Q-Learning with Q-Table -- Q-Learning with Q-Network  or Deep Q Network (DQN) -- Summary -- Chapter 16: Generative Adversarial Networks -- Generative Adversarial Networks 101 -- Best practices for building and training GANs -- Simple GAN with TensorFlow -- Simple GAN with Keras -- Deep Convolutional GAN with TensorFlow and Keras -- Summary -- Chapter 17: Distributed Models with TensorFlow Clusters -- Strategies for distributed execution -- TensorFlow clusters -- Defining cluster specification -- Create the server instances -- Define the parameter and operations across servers and devices -- Define and train the graph for asynchronous updates -- Define and train the graph for synchronous updates -- Summary -- Chapter 18: Debugging TensorFlow Models -- Fetching tensor values with tf.Session.run() -- Printing tensor values with tf.Print() -- Asserting on conditions with tf.Assert() -- Debugging with the TensorFlow debugger (tfdbg) -- Summary -- Chapter 19: Tensor Processing Units -- Chapter 20: Getting Started -- Understanding deep learning -- Perceptron -- Activation functions -- Sigmoid -- The hyperbolic tangent function -- The Rectified Linear Unit (ReLU) -- Artificial neural network (ANN) -- One-hot encoding -- Softmax -- Cross-entropy -- Dropout -- Batch normalization -- L1 and L2 regularization -- Training neural networks -- Backpropagation -- Gradient descent -- Stochastic gradient descent -- Playing with TensorFlow playground -- Convolutional neural network -- Kernel -- Max pooling -- Recurrent neural networks (RNN) -- Long short-term memory (LSTM) -- Deep learning for computer vision -- Classification -- Detection or localization and segmentation -- Similarity learning -- Image captioning -- Generative models -- Video analysis -- Development environment setup -- Hardware and Operating Systems - OS.

General Purpose - Graphics Processing Unit (GP-GPU).

This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.