Learn Keras for deep neural networks: a fast-track approach to modern deep learning with Python

Intro
 Table of Contents
 About the Author
 About the Technical Reviewer
 Acknowledgments
 Introduction
 Chapter 1: An Introduction to Deep Learning and Keras
 Introduction to DL
 Demystifying the Buzzwords
 What Are Some Classic Problems Solved by DL in Today\'s Market?
 Decomposing a DL Model
 Exploring the Popular DL Frameworks
 Low-Level DL Frameworks
 Theano
 Torch
 PyTorch
 MxNet
 TensorFlow
 High-Level DL Frameworks
 A Sneak Peek into the Keras Framework
 Getting the Data Ready
 Defining the Model Structure
 Training the Model and Making Predictions
 Summary
 Chapter 2: Keras in Action Setting Up the EnvironmentSelecting the Python Version
 Installing Python for Windows, Linux, or macOS
 Installing Keras and TensorFlow Back End
 Getting Started with DL in Keras
 Input Data
 Neuron
 Activation Function
 Sigmoid Activation Function
 ReLU Activation Function
 Model
 Layers
 Core Layers
 Dense Layer
 Dropout Layer
 Other Important Layers
 The Loss Function
 Optimizers
 Stochastic Gradient Descent (SGD)
 Adam
 Other Important Optimizers
 Metrics
 Model Configuration
 Model Training
 Model Evaluation
 Putting All the Building Blocks Together
 Summary Chapter 3: Deep Neural Networks for Supervised Learning: RegressionGetting Started
 Problem Statement
 Why Is Representing a Problem Statement with a Design Principle Important?
 Designing an SCQ
 Designing the Solution
 Exploring the Data
 Looking at the Data Dictionary
 Finding Data Types
 Working with Time
 Predicting Sales
 Exploring Numeric Columns
 Understanding the Categorical Features
 Data Engineering
 Defining Model Baseline Performance
 Designing the DNN
 Testing the Model Performance
 Improving the Model
 Increasing the Number of Neurons
 Plotting the Loss Metric Across Epochs Testing the Model ManuallySummary
 Chapter 4: Deep Neural Networks for Supervised Learning: Classification
 Getting Started
 Problem Statement
 Designing the SCQ
 Designing the Solution
 How Can We Identify a Potential Customer?
 Exploring the Data
 Data Engineering
 Defining Model Baseline Accuracy
 Designing the DNN for Classification
 Revisiting the Data
 Standardize, Normalize, or Scale the Data
 Transforming the Input Data
 DNNs for Classification with Improved Data
 Summary
 Chapter 5: Tuning and Deploying Deep Neural Networks
 The Problem of Overfitting
 So, What Is Regularization? L1 RegularizationL2 Regularization
 Dropout Regularization
 Hyperparameter Tuning
 Hyperparameters in DL
 Number of Neurons in a Layer
 Number of Layers
 Number of Epochs
 Weight Initialization
 Batch Size
 Learning Rate
 Activation Function
 Optimization
 Approaches for Hyperparameter Tuning
 Manual Search
 Grid Search
 Random Search
 Further Reading
 Model Deployment
 Tailoring the Test Data
 Saving Models to Memory
 Retraining the Models with New Data
 Online Models
 Delivering Your Model As an API
 Putting All the Pieces of the Puzzle Together
 Summary
 Chapter 6: The Path Ahead