Log In
Or create an account -> 
Imperial Library
  • Home
  • About
  • News
  • Upload
  • Forum
  • Help
  • Login/SignUp

Index
Beginning Application Development with TensorFlow and Keras
Table of Contents Beginning Application Development with TensorFlow and Keras
Why subscribe? PacktPub.com
Contributors
About the author About the reviewer
Preface
What This Book Covers What You Need for This Book Who This Book is for Conventions Reader Feedback Customer Support Downloading the Example Code Installation
Installing Visual Studio Installing Python 3 Installing TensorFlow Installing Keras
Errata Piracy Questions
1. Introduction to Neural Networks and Deep Learning
Lesson Objectives What are Neural Networks?
Successful Applications
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 Notebooks, Pandas, and NumPy Activity 1 – Verifying Software Components
Exploring a Trained Neural Network
MNIST Dataset
Training a Neural Network with TensorFlow Training a Neural Network Testing Network Performance with Unseen Data
Activity 2 – Exploring a Trained Neural Network
Summary
2. Model Architecture
Lesson Objectives Choosing the Right Model Architecture
Common Architectures
Convolutional Neural Networks Recurrent Neural Networks Generative Adversarial Networks Deep Reinforcement Learning
Data Normalization
Z-score Point-Relative Normalization Maximum and Minimum Normalization
Structuring Your Problem Activity 3 – Exploring the Bitcoin Dataset and Preparing Data for Model
Using Keras as a TensorFlow Interface
Model Components Activity 4 – Creating a TensorFlow Model Using Keras
From Data Preparation to Modeling
Training a Neural Network
Reshaping Time-Series Data
Making Predictions
Overfitting
Activity 5 – Assembling a Deep Learning System
Summary
3. Model Evaluation and Optimization
Lesson Objectives Model Evaluation
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 Activity 6 – 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) Hyperbolic Tangent (Tanh) Rectified Linear Unit Activation Functions - Implementation Regularization Strategies
L2 Regularization Dropout Regularization Strategies – Implementation Optimization Results
Activity 7 – Optimizing a Deep Learning Model
Summary
4. Productization
Lesson Objectives Handling New Data
Separating Data and Model
Data Component
Model Component Dealing with New Data
Re-Training an Old Model
Training a New Model Activity 8 – Dealing with New Data Deploying a Model as a Web Application
Application Architecture and Technologies
Deploying and Using Cryptonic
Activity 9 – Deploying a Deep Learning Application Summary
Index
  • ← Prev
  • Back
  • Next →
  • ← Prev
  • Back
  • Next →

Chief Librarian: Las Zenow <zenow@riseup.net>
Fork the source code from gitlab
.

This is a mirror of the Tor onion service:
http://kx5thpx2olielkihfyo4jgjqfb7zx7wxr3sd4xzt26ochei4m6f7tayd.onion