The TensorFlow Workshop

Spis treści książki

• The TensorFlow Workshop
• Preface
  • About the Book
    • About the Authors
    • Who This Book Is For
    • About the Chapters
    • Conventions
    • Code Presentation
    • Minimum Hardware Requirements
    • Downloading the Code Bundle
    • Setting Up Your Environment
      • Installing Anaconda on Your System
      • Launching Jupyter Notebook
      • Installing the tensorflow Virtual Environment
    • Get in Touch
    • Please Leave a Review
• 1. Introduction to Machine Learning with TensorFlow
  • Introduction
  • Implementing Artificial Neural Networks in TensorFlow
    • Advantages of TensorFlow
    • Disadvantages of TensorFlow
  • The TensorFlow Library in Python
    • Exercise 1.01: Verifying Your Version of TensorFlow
  • Introduction to Tensors
    • Scalars, Vectors, Matrices, and Tensors
    • Exercise 1.02: Creating Scalars, Vectors, Matrices, and Tensors in TensorFlow
  • Tensor Addition
    • Exercise 1.03: Performing Tensor Addition in TensorFlow
    • Activity 1.01: Performing Tensor Addition in TensorFlow
  • Reshaping
    • Tensor Transposition
    • Exercise 1.04: Performing Tensor Reshaping and Transposition in TensorFlow
    • Activity 1.02: Performing Tensor Reshaping and Transposition in TensorFlow
  • Tensor Multiplication
    • Exercise 1.05: Performing Tensor Multiplication in TensorFlow
  • Optimization
    • Forward Propagation
    • Backpropagation
    • Learning Optimal Parameters
    • Optimizers in TensorFlow
  • Activation functions
    • Activity 1.03: Applying Activation Functions
  • Summary
• 2. Loading and Processing Data
  • Introduction
  • Exploring Data Types
  • Data Preprocessing
  • Processing Tabular Data
    • Exercise 2.01: Loading Tabular Data and Rescaling Numerical Fields
    • Activity 2.01: Loading Tabular Data and Rescaling Numerical Fields with a MinMax Scaler
    • Exercise 2.02: Preprocessing Non-Numerical Data
  • Processing Image Data
    • Exercise 2.03: Loading Image Data for Batch Processing
  • Image Augmentation
    • Activity 2.02: Loading Image Data for Batch Processing
  • Text Processing
    • Exercise 2.04: Loading Text Data for TensorFlow Models
  • Audio Processing
    • Exercise 2.05: Loading Audio Data for TensorFlow Models
    • Activity 2.03: Loading Audio Data for Batch Processing
  • Summary
• 3. TensorFlow Development
  • Introduction
  • TensorBoard
    • Exercise 3.01: Using TensorBoard to Visualize Matrix Multiplication
    • Activity 3.01: Using TensorBoard to Visualize Tensor Transformations
    • Exercise 3.02: Using TensorBoard to Visualize Image Batches
  • TensorFlow Hub
    • Exercise 3.03: Downloading a Model from TensorFlow Hub
  • Google Colab
    • Advantages of Google Colab
    • Disadvantages of Google Colab
    • Development on Google Colab
    • Exercise 3.04: Using Google Colab to Visualize Data
    • Activity 3.02: Performing Word Embedding from a Pre-Trained Model from TensorFlow Hub
  • Summary
• 4. Regression and Classification Models
  • Introduction
  • Sequential Models
    • Keras Layers
    • Exercise 4.01: Creating an ANN with TensorFlow
  • Model Fitting
    • The Loss Function
    • Model Evaluation
    • Exercise 4.02: Creating a Linear Regression Model as an ANN with TensorFlow
    • Exercise 4.03: Creating a Multi-Layer ANN with TensorFlow
    • Activity 4.01: Creating a Multi-Layer ANN with TensorFlow
  • Classification Models
    • Exercise 4.04: Creating a Logistic Regression Model as an ANN with TensorFlow
    • Activity 4.02: Creating a Multi-Layer Classification ANN with TensorFlow
  • Summary
• 5. Classification Models
  • Introduction
  • Binary Classification
    • Logistic Regression
    • Binary Cross-Entropy
    • Binary Classification Architecture
    • Exercise 5.01: Building a Logistic Regression Model
  • Metrics for Classifiers
    • Accuracy and Null Accuracy
    • Precision, Recall, and the F1 Score
    • Confusion Matrices
    • Exercise 5.02: Classification Evaluation Metrics
  • Multi-Class Classification
    • The Softmax Function
    • Categorical Cross-Entropy
    • Multi-Class Classification Architecture
    • Exercise 5.03: Building a Multi-Class Model
    • Activity 5.01: Building a Character Recognition Model with TensorFlow
  • Multi-Label Classification
    • Activity 5.02: Building a Movie Genre Tagging a Model with TensorFlow
  • Summary
• 6. Regularization and Hyperparameter Tuning
  • Introduction
  • Regularization Techniques
    • L1 Regularization
    • L2 Regularization
    • Exercise 6.01: Predicting a Connect-4 Game Outcome Using the L2 Regularizer
    • Dropout Regularization
    • Exercise 6.02: Predicting a Connect-4 Game Outcome Using Dropout
    • Early Stopping
    • Activity 6.01: Predicting Income with L1 and L2 Regularizers
  • Hyperparameter Tuning
    • Keras Tuner
    • Random Search
    • Exercise 6.03: Predicting a Connect-4 Game Outcome Using Random Search from Keras Tuner
    • Hyperband
    • Exercise 6.04: Predicting a Connect-4 Game Outcome Using Hyperband from Keras Tuner
    • Bayesian Optimization
    • Activity 6.02: Predicting Income with Bayesian Optimization from Keras Tuner
  • Summary
• 7. Convolutional Neural Networks
  • Introduction
  • CNNs
  • Image Representation
  • The Convolutional Layer
    • Creating the Model
    • Exercise 7.01: Creating the First Layer to Build a CNN
  • Pooling Layer
    • Max Pooling
    • Average Pooling
    • Exercise 7.02: Creating a Pooling Layer for a CNN
    • Flattening Layer
    • Exercise 7.03: Building a CNN
  • Image Augmentation
    • Batch Normalization
    • Exercise 7.04: Building a CNN with Additional Convolutional Layers
  • Binary Image Classification
  • Object Classification
    • Exercise 7.05: Building a CNN
    • Activity 7.01: Building a CNN with More ANN Layers
  • Summary
• 8. Pre-Trained Networks
  • Introduction
  • ImageNet
  • Transfer Learning
    • Exercise 8.01: Classifying Cats and Dogs with Transfer Learning
  • Fine-Tuning
    • Activity 8.01: Fruit Classification with Fine-Tuning
  • TensorFlow Hub
  • Feature Extraction
    • Activity 8.02: Transfer Learning with TensorFlow Hub
  • Summary
• 9. Recurrent Neural Networks
  • Introduction
  • Sequential Data
    • Examples of Sequential Data
    • Exercise 9.01: Training an ANN for Sequential Data Nvidia Stock Prediction
  • Recurrent Neural Networks
    • RNN Architecture
    • Vanishing Gradient Problem
    • Long Short-Term Memory Network
    • Exercise 9.02: Building an RNN with an LSTM Layer Nvidia Stock Prediction
    • Activity 9.01: Building an RNN with Multiple LSTM Layers to Predict Power Consumption
  • Natural Language Processing
    • Data Preprocessing
      • Dataset Cleaning
      • Generating a Sequence and Tokenization
      • Padding Sequences
  • Back Propagation Through Time (BPTT)
    • Exercise 9.03: Building an RNN with an LSTM Layer for Natural Language Processing
    • Activity 9.02: Building an RNN for Predicting Tweets Sentiment
  • Summary
• 10. Custom TensorFlow Components
  • Introduction
  • TensorFlow APIs
  • Implementing Custom Loss Functions
    • Building a Custom Loss Function with the Functional API
    • Building a Custom Loss Function with the Subclassing API
    • Exercise 10.01: Building a Custom Loss Function
  • Implementing Custom Layers
    • Introduction to ResNet Blocks
    • Building Custom Layers with the Functional API
    • Building Custom Layers with Subclassing
    • Exercise 10.02: Building a Custom Layer
    • Activity 10.01: Building a Model with Custom Layers and a Custom Loss Function
  • Summary
• 11. Generative Models
  • Introduction
  • Text Generation
    • Extending NLP Sequence Models to Generate Text
    • Dataset Cleaning
    • Generating a Sequence and Tokenization
    • Generating a Sequence of n-gram Tokens
    • Padding Sequences
    • Exercise 11.01: Generating Text
  • Generative Adversarial Networks
    • The Generator Network
    • The Discriminator Network
    • The Adversarial Network
      • Combining the Generative and Discriminative Models
      • Generating Real Samples with Class Labels
      • Creating Latent Points for the Generator
      • Using the Generator to Generate Fake Samples and Class Labels
      • Evaluating the Discriminator Model
      • Training the Generator and Discriminator
      • Creating the Latent Space, Generator, Discriminator, GAN, and Training Data
    • Exercise 11.02: Generating Sequences with GANs
  • Deep Convolutional Generative Adversarial Networks (DCGANs)
    • Training a DCGAN
    • Exercise 11.03: Generating Images with DCGAN
    • Activity 11.01: Generating Images Using GANs
  • Summary
• Appendix
  • 1. Introduction to Machine Learning with TensorFlow
    • Activity 1.01: Performing Tensor Addition in TensorFlow
    • Activity 1.02: Performing Tensor Reshaping and Transposition in TensorFlow
    • Activity 1.03: Applying Activation Functions
  • 2. Loading and Processing Data
    • Activity 2.01: Loading Tabular Data and Rescaling Numerical Fields with a MinMax Scaler
    • Activity 2.02: Loading Image Data for Batch Processing
    • Activity 2.03: Loading Audio Data for Batch Processing
  • 3. TensorFlow Development
    • Activity 3.01: Using TensorBoard to Visualize Tensor Transformations
    • Activity 3.02: Performing Word Embedding from a Pre-Trained Model from TensorFlow Hub
  • 4. Regression and Classification Models
    • Activity 4.01: Creating a Multi-Layer ANN with TensorFlow
    • Activity 4.02: Creating a Multi-Layer Classification ANN with TensorFlow
  • 5. Classification Models
    • Activity 5.01: Building a Character Recognition Model with TensorFlow
    • Activity 5.02: Building a Movie Genre Tagging a Model with TensorFlow
  • 6. Regularization and Hyperparameter Tuning
    • Activity 6.01: Predicting Income with L1 and L2 Regularizers
    • Activity 6.02: Predicting Income with Bayesian Optimization from Keras Tuner
  • 7. Convolutional Neural Networks
    • Activity 7.01: Building a CNN with More ANN Layers
  • 8. Pre-Trained Networks
    • Activity 8.01: Fruit Classification with Fine-Tuning
    • Activity 8.02: Transfer Learning with TensorFlow Hub
  • 9. Recurrent Neural Networks
    • Activity 9.01: Building an RNN with Multiple LSTM Layers to Predict Power Consumption
    • Activity 9.02: Building an RNN for Predicting Tweets' Sentiment
  • 10. Custom TensorFlow Components
    • Activity 10.01: Building a Model with Custom Layers and a Custom Loss Function
  • 11. Generative Models
    • Activity 11.01: Generating Images Using GANs
    • Hey!

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