The Deep Learning with PyTorch Workshop: Build deep neural networks and artificial intelligence applications with PyTorch

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Table of Contents
Preface
Chapter 1: Introduction to Deep Learning and PyTorch
	Introduction
	Why Deep Learning?
		Applications of Deep Learning
	Introduction to PyTorch
		GPUs in PyTorch
		What Are Tensors?
		Exercise 1.01: Creating Tensors of Different Ranks Using PyTorch
		Advantages of Using PyTorch
		Disadvantages of Using PyTorch
		Key Elements of PyTorch
			The PyTorch autograd Library
			The PyTorch nn Module
		Exercise 1.02: Defining a Single-Layer Architecture
			The PyTorch optim Package
		Exercise 1.03: Training a Neural Network
		Activity 1.01: Creating a Single-Layer Neural Network
	Summary
Chapter 2: Building Blocks of Neural Networks
	Introduction
	Introduction to Neural Networks
		What Are Neural Networks?
		Exercise 2.01: Performing the Calculations of a Perceptron
		Multi-Layer Perceptron
		The Learning Process of a Neural Network
			Forward Propagation
			The Calculation of Loss Functions
			Backward Propagation
			Gradient Descent
		Advantages and Disadvantages
			Advantages
			Disadvantages
		Introduction to Artificial Neural Networks
		Introduction to Convolutional Neural Networks
		Introduction to Recurrent Neural Networks
	Data Preparation
		Dealing with Messy Data
		Exercise 2.02: Dealing with Messy Data
		Data Rescaling
		Exercise 2.03: Rescaling Data
		Splitting the Data
		Exercise 2.04: Splitting a Dataset
		Disadvantages of Failing to Prepare Your Data
		Activity 2.01: Performing Data Preparation
	Building a Deep Neural Network
		Exercise 2.05: Building a Deep Neural Network Using PyTorch
		Activity 2.02: Developing a Deep Learning Solution for a Regression Problem
	Summary
Chapter 3: A Classification Problem Using DNN
	Introduction
	Problem Definition
		Deep Learning in Banking
		Exploring the Dataset
		Data Preparation
		Building the Model
		ANNs for Classification Tasks
		A Good Architecture
		PyTorch Custom Modules
		Exercise 3.01: Defining a Model's Architecture Using Custom Modules
		Defining the Loss Function and Training the Model
		Activity 3.01: Building an ANN
	Dealing with an Underfitted or Overfitted Model
		Error Analysis
		Exercise 3.02: Performing Error Analysis
		Activity 3.02: Improving a Model's Performance
	Deploying Your Model
		Saving and Loading Your Model
		PyTorch for Production in C++
		Building an API
		Exercise 3.03: Creating a Web API
		Activity 3.03: Making Use of Your Model
	Summary
Chapter 4: Convolutional Neural Networks
	Introduction
	Building a CNN
		Why Are CNNs Used for Image Processing?
		The Image as Input
		Applications of CNNs
			Classification
			Localization
			Detection
			Segmentation
		The Building Blocks of CNNs
			Convolutional Layers
		Exercise 4.01: Calculating the Output Shape of a Convolutional Layer
			Pooling Layers
		Exercise 4.02: Calculating the Output Shape of a Set of Convolutional and Pooling Layers
			Fully Connected Layers
		Side Note – Downloading Datasets from PyTorch
		Activity 4.01: Building a CNN for an Image Classification Problem
	Data Augmentation
		Data Augmentation with PyTorch
		Activity 4.02: Implementing Data Augmentation
	Batch Normalization
		Batch Normalization with PyTorch
		Activity 4.03: Implementing Batch Normalization
	Summary
Chapter 5: Style Transfer
	Introduction
	Style Transfer
		How Does It Work?
	Implementation of Style Transfer Using the VGG-19 Network Architecture
		Inputs – Loading and Displaying
		Exercise 5.01: Loading and Displaying Images
		Loading the Model
		Exercise 5.02: Loading a Pre-Trained Model in PyTorch
		Extracting the Features
		Exercise 5.03: Setting Up the Feature Extraction Process
		The Optimization Algorithm, Losses, and Parameter Update
			Content Loss
			Style Loss
			Total Loss
		Exercise 5.04: Creating the Target Image
		Activity 5.01: Performing Style Transfer
	Summary
Chapter 6: Analyzing the Sequence of Data with RNNs
	Introduction
	Recurrent Neural Networks
		Applications of RNNs
		How Do RNNs Work?
		Input and Targets for Sequenced Data
		Exercise 6.01: Creating the Input and Target Variables for a Sequenced Data Problem
		RNNs in PyTorch
		Activity 6.01: Using a Simple RNN for a Time Series Prediction
	Long Short-Term Memory Networks
		Applications of LSTM Networks
		How Do LSTM Networks Work?
	LSTM Networks in PyTorch
		Preprocessing the Input Data
			Numbered Labels
			Generating the Batches
		One-Hot Encoding
		Exercise 6.02: Preprocessing the Input Data and Creating a One-Hot Matrix
		Building the Architecture
		Training the Model
		Performing Predictions
		Activity 6.02: Text Generation with LSTM Networks
	Natural Language Processing
		Sentiment Analysis
	Sentiment Analysis in PyTorch
		Preprocessing the Input Data
		Building the Architecture
		Training the Model
		Activity 6.03: Performing NLP for Sentiment Analysis
	Summary
Appendix
Index