Deep Learning with TensorFlow: Explore neural networks and build intelligent systems with Python, 2nd Edition

Cover
Copyright
Packt Upsell
Contributors
Table of Contents
Preface
Chapter 1: Getting Started with Deep Learning
	A soft introduction to machine learning
		Supervised learning
		Unbalanced data
		Unsupervised learning
		Reinforcement learning
		What is deep learning?
	Artificial neural networks
		The biological neurons
		The artificial neuron
	How does an ANN learn?
		ANNs and the backpropagation algorithm
		Weight optimization
		Stochastic gradient descent
	Neural network architectures
		Deep Neural Networks (DNNs)
			Multilayer perceptron
			Deep Belief Networks (DBNs)
		Convolutional Neural Networks (CNNs)
		AutoEncoders
		Recurrent Neural Networks (RNNs)
		Emergent architectures
	Deep learning frameworks
	Summary
Chapter 2: A First Look at TensorFlow
	A general overview of TensorFlow
	What's new in TensorFlow v1.6?
		Nvidia GPU support optimized
		Introducing TensorFlow Lite
		Eager execution
		Optimized Accelerated Linear Algebra (XLA)
	Installing and configuring TensorFlow
	TensorFlow computational graph
	TensorFlow code structure
		Eager execution with TensorFlow
	Data model in TensorFlow
		Tensor
		Rank and shape
		Data type
		Variables
		Fetches
		Feeds and placeholders
	Visualizing computations through TensorBoard
		How does TensorBoard work?
	Linear regression and beyond
		Linear regression revisited for a real dataset
	Summary
Chapter 3: Feed-Forward Neural Networks with TensorFlow
	Feed-forward neural networks (FFNNs)
		Feed-forward and backpropagation
		Weights and biases
		Activation functions
			Using sigmoid
			Using tanh
			Using ReLU
			Using softmax
	Implementing a feed-forward neural network
		Exploring the MNIST dataset
			Softmax classifier
	Implementing a multilayer perceptron (MLP)
		Training an MLP
		Using MLPs
			Dataset description
			Preprocessing
			A TensorFlow implementation of MLP for client-subscription assessment
		Deep Belief Networks (DBNs)
			Restricted Boltzmann Machines (RBMs)
			Construction of a simple DBN
			Unsupervised pre-training
			Supervised fine-tuning
		Implementing a DBN with TensorFlow for client-subscription assessment
	Tuning hyperparameters and advanced FFNNs
		Tuning FFNN hyperparameters
			Number of hidden layers
			Number of neurons per hidden layer
			Weight and biases initialization
			Selecting the most suitable optimizer
			GridSearch and randomized search for hyperparameters tuning
		Regularization
		Dropout optimization
	Summary
Chapter 4: Convolutional Neural Networks
	Main concepts of CNNs
	CNNs in action
	LeNet5
	Implementing a LeNet-5 step by step
		AlexNet
		Transfer learning
		Pretrained AlexNet
	Dataset preparation
	Fine-tuning implementation
		VGG
		Artistic style learning with VGG-19
		Input images
		Content extractor and loss
		Style extractor and loss
		Merger and total loss
		Training
	Inception-v3
		Exploring Inception with TensorFlow
	Emotion recognition with CNNs
		Testing the model on your own image
		Source code
	Summary
Chapter 5: Optimizing TensorFlow Autoencoders
	How does an autoencoder work?
	Implementing autoencoders with TensorFlow
	Improving autoencoder robustness
		Implementing a denoising autoencoder
		Implementing a convolutional autoencoder
			Encoder
			Decoder
	Fraud analytics with autoencoders
		Description of the dataset
		Problem description
		Exploratory data analysis
		Training, validation, and testing set preparation
		Normalization
		Autoencoder as an unsupervised feature learning algorithm
		Evaluating the model
	Summary
Chapter 6: Recurrent Neural Networks
	Working principles of RNNs
		Implementing basic RNNs in TensorFlow
		RNN and the long-term dependency problem
			Bi-directional RNNs
	RNN and the gradient vanishing-exploding problem
		LSTM networks
			GRU cell
	Implementing an RNN for spam prediction
		Data description and preprocessing
	Developing a predictive model for time series data
		Description of the dataset
		Pre-processing and exploratory analysis
		LSTM predictive model
		Model evaluation
	An LSTM predictive model for sentiment analysis
		Network design
		LSTM model training
		Visualizing through TensorBoard
		LSTM model evaluation
	Human activity recognition using LSTM model
		Dataset description
		Workflow of the LSTM model for HAR
		Implementing an LSTM model for HAR
	Summary
Chapter 7: Heterogeneous and Distributed Computing
	GPGPU computing
		The GPGPU history
		The CUDA architecture
		The GPU programming model
	The TensorFlow GPU setup
		Update TensorFlow
		GPU representation
		Using a GPU
		GPU memory management
		Assigning a single GPU on a multi-GPU system
		The source code for GPU with soft placement
		Using multiple GPUs
	Distributed computing
		Model parallelism
		Data parallelism
	The distributed TensorFlow setup
	Summary
Chapter 8: Advanced TensorFlow Programming
	tf.estimator
		Estimators
		Graph actions
		Parsing resources
		Flower predictions
	TFLearn
		Installation
		Titanic survival predictor
	PrettyTensor
		Chaining layers
		Normal mode
		Sequential mode
		Branch and join
		Digit classifier
	Keras
		Keras programming models
			Sequential model
			Functional API
	Summary
Chapter 9: Recommendation Systems Using Factorization Machines
	Recommendation systems
		Collaborative filtering approaches
		Content-based filtering approaches
		Hybrid recommender systems
		Model-based collaborative filtering
	Movie recommendation using collaborative filtering
		The utility matrix
		Description of the dataset
			Ratings data
			Movies data
			Users data
		Exploratory analysis of the MovieLens dataset
		Implementing a movie RE
			Training the model with the available ratings
			Inferencing the saved model
			Generating the user-item table
			Clustering similar movies
			Movie rating prediction by users
			Finding top k movies
			Predicting top k similar movies
			Computing user-user similarity
		Evaluating the recommender system
	Factorization machines for recommendation systems
		Factorization machines
			Cold-start problem and collaborative-filtering approaches
		Problem definition and formulation
		Dataset description
			Workflow of the implementation
		Preprocessing
			Training the FM model
	Improved factorization machines
		Neural factorization machines
			Dataset description
			Using NFM for the movie recommendation
	Summary
Chapter 10: Reinforcement Learning
	The RL problem
	OpenAI Gym
		OpenAI environments
		The env class
		Installing and running OpenAI Gym
	The Q-Learning algorithm
		The FrozenLake environment
	Deep Q-learning
		Deep Q neural networks
		The Cart-Pole problem
			Deep Q-Network for the Cart-Pole problem
			The Experience Replay method
			Exploitation and exploration
			The Deep Q-Learning training algorithm
	Summary
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