The Applied TensorFlow and Keras Workshop

Spis treści książki

• The Applied TensorFlow and Keras Workshop
• Preface
  • About the Book
    • Audience
    • About the Chapters
    • Conventions
    • Code Presentation
    • Setting up Your Environment
    • Installation
      • Installing Python on Windows
      • Installing Python on Linux
      • Installing Python on macOS
      • Installing pip
    • Jupyter Notebook
    • JupyterLab
    • Installing Libraries
    • Accessing the Code Files
• 1. Introduction to Neural Networks and Deep Learning
  • Introduction
  • What are Neural Networks?
    • Successful Applications of Neural Networks
  • Why Do Neural Networks Work So Well?
    • Representation Learning
    • Function Approximation
    • Limitations of Deep Learning
    • Inherent Bias and Ethical Considerations
    • Common Components and Operations of Neural Networks
  • Configuring a Deep Learning Environment
    • Software Components for Deep Learning
      • Python 3
      • TensorFlow
      • Keras
      • TensorBoard
      • Jupyter Notebook, Pandas, and NumPy
    • Exercise 1.01: Verifying the Software Components
  • Exploring a Trained Neural Network
    • The MNIST Dataset
    • Training a Neural Network with TensorFlow
    • Exercise 1.02: Training a Neural Network Using the MNIST Dataset
    • Testing Network Performance with Unseen Data
    • Activity 1.01: Training a Neural Network with Different Hyperparameters
  • Summary
• 2. Real-World Deep Learning: Predicting the Price of Bitcoin
  • Introduction
  • Choosing the Right Model Architecture
    • Convolutional Neural Networks (CNNs)
    • Recurrent Neural Networks (RNNs)
    • Long Short-Term Memory (LSTM) Networks
    • Generative Adversarial Networks
    • Deep Reinforcement Learning (DRL)
    • Data Normalization
      • Z-Score
      • Point-Relative Normalization
      • Maximum and Minimum Normalization
  • Structuring Your Problem
    • Jupyter Notebook
    • Exercise 2.01: Exploring the Bitcoin Dataset and Preparing Data for a Model
  • Using Keras as a TensorFlow Interface
    • Model Components
    • Exercise 2.02: Creating a TensorFlow Model Using Keras
  • From Data Preparation to Modeling
    • Training a Neural Network
    • Reshaping Time Series Data
    • Making Predictions
      • Overfitting
    • Activity 2.01: Assembling a Deep Learning System
  • Summary
• 3. Real-World Deep Learning: Evaluating the Bitcoin Model
  • Introduction
    • Problem Categories
    • Loss Functions, Accuracy, and Error Rates
    • Different Loss Functions, Same Architecture
    • Using TensorBoard
    • Implementing Model Evaluation Metrics
    • Evaluating the Bitcoin Model
    • Overfitting
    • Model Predictions
    • Interpreting Predictions
    • Exercise 3.01: Creating an Active Training Environment
  • Hyperparameter Optimization
    • Layers and Nodes Adding More Layers
      • Adding More Nodes
    • Layers and Nodes Implementation
    • Epochs
      • Epochs Implementation
    • Activation Functions
    • Linear (Identity) Functions
      • Hyperbolic Tangent (Tanh) Function
      • Rectified Linear Unit Functions
    • Activation Functions Implementation
    • Regularization Strategies
      • L2 Regularization
      • Dropout
      • Regularization Strategies Implementation
    • Optimization Results
    • Activity 3.01: Optimizing a Deep Learning Model
  • Summary
• 4. Productization
  • Introduction
  • Handling New Data
    • Separating Data and Model
    • The Data Component
    • The Model Component
    • Dealing with New Data
      • Retraining an Old Model
      • Training a New Model
    • Exercise 4.01: Retraining a Model Dynamically
    • Deploying a Model as a Web Application
    • Application Architecture and Technologies
    • Exercise 4.02: Deploying and Using Cryptonic
    • Activity 4.01: Deploying a Deep Learning Application
  • Summary
• Appendix
  • 1. Introduction to Neural Networks and Deep Learning
    • Activity 1.01: Training a Neural Network with Different Hyperparameters
  • 2. Real-World Deep Learning: Predicting the Price of Bitcoin
    • Activity 2.01: Assembling a Deep Learning System
  • 3. Real-World Deep Learning: Evaluating the Bitcoin Model
    • Activity 3.01: Optimizing a Deep Learning Model
  • 4. Productization
    • Activity 4.01: Deploying a Deep Learning Application

pokaż cały spis treści

ukryj recenzje