The Deep Learning Workshop

Spis treści książki

• The Deep Learning Workshop
• Preface
  • About the Book
    • Audience
    • About the Chapters
    • Conventions
    • Code Presentation
    • Setting up Your Environment
    • Hardware Requirements
    • Installing Anaconda on your system
    • Launching Jupyter Notebook
    • Installing Libraries
    • Installing TensorFlow 2.0
    • Installing Keras
    • Accessing the Code Files
• 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
• 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
• 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
• 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
• 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
• 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
• 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
  • 1. Building Blocks of Deep Learning
    • Activity 1.01: Solving a Quadratic Equation Using an Optimizer
    • Solution
  • 2. Neural Networks
    • Activity 2.01: Build a Multilayer Neural Network to Classify Sonar Signals
    • Solution
  • 3. Image Classification with Convolutional Neural Networks (CNNs)
    • Activity 3.01: Building a Multiclass Classifier Based on the Fashion MNIST Dataset
    • Solution
    • Activity 3.02: Fruit Classification with Transfer Learning
    • Solution
  • 4. Deep Learning for Text Embeddings
    • Activity 4.01: Text Preprocessing of the 'Alice in Wonderland' Text
    • Solution
    • Activity 4.02: Text Representation for Alice in Wonderland
    • Solution
  • 5. Deep Learning for Sequences
    • Activity 5.01: Using a Plain RNN Model to Predict IBM Stock Prices
    • Solution
  • 6. LSTMs, GRUs, and Advanced RNNs
    • Activity 6.01: Sentiment Analysis of Amazon Product Reviews
    • Solution
  • 7. Generative Adversarial Networks
    • Activity 7.01: Implementing a DCGAN for the MNIST Fashion Dataset
    • Solution

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