The Deep Learning Workshop: Learn the skills you need to develop your own next-generation deep learning models with TensorFlow and Keras: Take a ... that can recognize images and interpret text

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Table of Contents
Preface
Chapter 1: Building Blocks of Deep Learning
	Introduction
		AI, Machine Learning, and Deep Learning
		Machine Learning
		Deep Learning
		Using Deep Learning to Classify an Image
			Pre-Trained Models
			The Google Text-to-Speech API
			Prerequisite Packages for the Demo
		Exercise 1.01: Image and Speech Recognition Demo
		Deep Learning Models
			The Multi-Layer Perceptron
			Convolutional Neural Networks
			Recurrent Neural Networks
		Generative Adversarial Networks
	Introduction to TensorFlow
		Constants
		Variables
			Defining Functions in TensorFlow
		Exercise 1.02: Implementing a Mathematical Equation
		Linear Algebra with TensorFlow
		Exercise 1.03: Matrix Multiplication Using TensorFlow
		The reshape Function
		Exercise 1.04: Reshaping Matrices Using the reshape() Function in TensorFlow
		The argmax Function
		Exercise 1.05: Implementing the argmax() Function
		Optimizers
		Exercise 1.06: Using an Optimizer for a Simple Linear Regression
		Activity 1.01: Solving a Quadratic Equation Using an Optimizer
	Summary
Chapter 2: Neural Networks
	Introduction
	Neural Networks and the Structure of Perceptrons
		Input Layer
			Weights
			Bias
			Net Input Function
			Activation Function (G)
			Perceptrons in TensorFlow
		Exercise 2.01: Perceptron Implementation
	Training a Perceptron
		Perceptron Training Process in TensorFlow
		Exercise 2.02: Perceptron as a Binary Classifier
		Multiclass Classifier
			The Softmax Activation Function
		Exercise 2.03: Multiclass Classification Using a Perceptron
		MNIST Case Study
		Exercise 2.04: Classifying Handwritten Digits
	Keras as a High-Level API
		Exercise 2.05: Binary Classification Using Keras
		Multilayer Neural Network or Deep Neural Network
		ReLU Activation Function
		Exercise 2.06: Multilayer Binary Classifier
		Exercise 2.07: Deep Neural Network on MNIST Using Keras
	Exploring the Optimizers and Hyperparameters of Neural Networks
		Gradient Descent Optimizers
		The Vanishing Gradient Problem
		Hyperparameter Tuning
		Overfitting and Dropout
	Activity 2.01: Build a Multilayer Neural Network to Classify Sonar Signals
	Summary
Chapter 3: Image Classification with Convolutional Neural Networks (CNNs)
	Introduction
	Digital Images
	Image Processing
		Convolution Operations
		Exercise 3.01: Implementing a Convolution Operation
		Stride
		Padding
	Convolutional Neural Networks
	Pooling Layers
		CNNs with TensorFlow and Keras
		Exercise 3.02: Recognizing Handwritten Digits (MNIST) with CNN Using KERAS
		Data Generator
		Exercise 3.03: Classifying Cats versus Dogs with Data Generators
	Data Augmentation
		Horizontal Flipping
		Vertical Flipping
		Zooming
		Horizontal Shifting
		Vertical Shifting
		Rotating
		Shearing
		Exercise 3.04: Image Classification (CIFAR-10) with Data Augmentation
		Activity 3.01: Building a Multiclass Classifier Based on the Fashion MNIST Dataset
	Saving and Restoring Models
		Saving the Entire Model
			Saving the Architecture Only
			Saving the Weights Only
	Transfer Learning
	Fine-Tuning
		Activity 3.02: Fruit Classification with Transfer Learning
	Summary
Chapter 4: Deep Learning for Text – Embeddings
	Introduction
	Deep Learning for Natural Language Processing
		Getting Started with Text Data Handling
		Text Preprocessing
			Tokenization
			Normalizing Case
			Removing Punctuation
			Removing Stop Words
		Exercise 4.01: Tokenizing, Case Normalization, Punctuation, and Stop Word Removal
		Stemming and Lemmatization
		Exercise 4.02: Stemming Our Data
			Beyond Stemming and Lemmatization
			Downloading Text Corpora Using NLTK
		Activity 4.01: Text Preprocessing of the 'Alice in Wonderland' Text
		Text Representation Considerations
	Classical Approaches to Text Representation
		One-Hot Encoding
		Exercise 4.03: Creating One-Hot Encoding for Our Data
		Term Frequencies
		The TF-IDF Method
		Exercise 4.04: Document-Term Matrix with TF-IDF
		Summarizing the Classical Approaches
	Distributed Representation for Text
		Word Embeddings and Word Vectors
			word2vec
		Training Our Own Word Embeddings
		Semantic Regularities in Word Embeddings
		Exercise 4.05: Vectors for Phrases
			Effect of Parameters  – "size" of the Vector
			Effect of Parameters – "window size"
		Skip-gram versus CBOW
			Effect of Training Data
		Exercise 4.06: Training Word Vectors on Different Datasets
		Using Pre-Trained Word Vectors
		Bias in Embeddings – A Word of Caution
		Other Notable Approaches to Word Embeddings
		Activity 4.02: Text Representation for Alice in Wonderland
	Summary
Chapter 5: Deep Learning for Sequences
	Introduction
	Working with Sequences
		Time Series Data – Stock Price Prediction
		Exercise 5.01: Visualizing Our Time-Series Data
	Recurrent Neural Networks
		Loops – An Integral Part of RNNs
		Exercise 5.02: Implementing the Forward Pass of a Simple RNN Using TensorFlow
		The Flexibility and Versatility of RNNs
		Preparing the Data for Stock Price Prediction
		Parameters in an RNN
		Training RNNs
		Exercise 5.03: Building Our First Plain RNN Model
		Model Training and Performance Evaluation
		1D Convolutions for Sequence Processing
		Exercise 5.04: Building a 1D Convolution-Based Model
		Performance of 1D Convnets
		Using 1D Convnets with RNNs
		Exercise 5.05: Building a Hybrid (1D Convolution and RNN) Model
		Activity 5.01: Using a Plain RNN Model to Predict IBM Stock Prices
	Summary
Chapter 6: LSTMs, GRUs, and Advanced RNNs
	Introduction
	Long-Range Dependence/Influence
	The Vanishing Gradient Problem
	Sequence Models for Text Classification
		Loading Data
		Staging and Preprocessing Our Data
	The Embedding Layer
	Building the Plain RNN Model
		Exercise 6.01: Building and Training an RNN Model for Sentiment Classification
	Making Predictions on Unseen Data
	LSTMs, GRUs, and Other Variants
		LSTMs
	Parameters in an LSTM
		Exercise 6.02: LSTM-Based Sentiment Classification Model
	LSTM versus Plain RNNs
	Gated Recurrence Units
		Exercise 6.03: GRU-Based Sentiment Classification Model
		LSTM versus GRU
	Bidirectional RNNs
		Exercise 6.04: Bidirectional LSTM-Based Sentiment Classification Model
	Stacked RNNs
		Exercise 6.05: Stacked LSTM-Based Sentiment Classification Model
	Summarizing All the Models
	Attention Models
	More Variants of RNNs
		Activity 6.01: Sentiment Analysis of Amazon Product Reviews
	Summary
Chapter 7: Generative Adversarial Networks
	Introduction
		Key Components of Generative Adversarial Networks
		Problem Statement – Generating a Distribution Similar to a Given Mathematical Function
		Process 1 – Generating Real Data from the Known Function
		Exercise 7.01: Generating a Data Distribution from a Known Function
		Process 2 – Creating a Basic Generative Network
		Building the Generative Network
		Sequential()
			Kernel Initializers
			Dense Layers
			Activation Functions
		Exercise 7.02: Building a Generative Network
		Setting the Stage for the Discriminator Network
		Process 3 – Discriminator Network
			Implementing the Discriminator Network
			Function to Generate Real Samples
			Functions to Generate Fake Samples
			Building the Discriminator Network
			Training the Discriminator Network
		Exercise 7.03: Implementing the Discriminator Network
		Process 4 – Implementing the GAN
			Integrating All the Building Blocks
		Process for Building the GAN
		The Training Process
		Exercise 7.04: Implementing the GAN
	Deep Convolutional GANs
		Building Blocks of DCGANs
		Generating Handwritten Images Using DCGANs
			The Training Process
		Exercise 7.05: Implementing the DCGAN
		Analysis of Sample Plots
		Common Problems with GANs
			Mode Collapse
			Convergence Failure
		Activity 7.01: Implementing a DCGAN for the MNIST Fashion Dataset
	Summary
Appendix
Index