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

Description

Take a hands-on approach to understanding deep learning and build smart applications that can recognize images and interpret text Key Features Understand how to implement deep learning with TensorFlow and Keras Learn the fundamentals of computer vision and image recognition Study the architecture of different neural networksBook Description Are you fascinated by how deep learning powers intelligent applications such as self-driving cars, virtual assistants, facial recognition devices, and chatbots to process data and solve complex problems? Whether you are familiar with machine learning or are new to this domain, The Deep Learning Workshop will make it easy for you to understand deep learning with the help of interesting examples and exercises throughout. The book starts by highlighting the relationship between deep learning, machine learning, and artificial intelligence and helps you get comfortable with the TensorFlow 2.0 programming structure using hands-on exercises. You'll understand neural networks, the structure of a perceptron, and how to use TensorFlow to create and train models. The book will then let you explore the fundamentals of computer vision by performing image recognition exercises with convolutional neural networks (CNNs) using Keras. As you advance, you'll be able to make your model more powerful by implementing text embedding and sequencing the data using popular deep learning solutions. Finally, you'll get to grips with bidirectional recurrent neural networks (RNNs) and build generative adversarial networks (GANs) for image synthesis. By the end of this deep learning book, you'll have learned the skills essential for building deep learning models with TensorFlow and Keras. What you will learn Understand how deep learning, machine learning, and artificial intelligence are different Develop multilayer deep neural networks with TensorFlow Implement deep neural networks for multiclass classification using Keras Train CNN models for image recognition Handle sequence data and use it in conjunction with RNNs Build a GAN to generate high-quality synthesized imagesWho this book is for If you are interested in machine learning and want to create and train deep learning models using TensorFlow and Keras, this workshop is for you. A solid understanding of Python and its packages, along with basic machine learning concepts, will help you to learn the topics quickly. Table of Contents Building Blocks of Deep Learning Neural Networks Image Classification with Convolutional Neural Networks (CNNs) Deep Learning for Text - Embeddings Deep Learning for Sequences LSTMs, GRUs, and Advanced RNNs Generative Adversarial Networks Cover FM Copyright 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