Neural Networks with Keras Cookbook: Over 70 recipes leveraging deep learning techniques across image, text, audio, and game bots

Ayyadevara, V Kishore

ISBN 10: 1789346649 / ISBN 13: 9781789346640

Verlag: Packt Publishing, 2019

Beschreibung

New. Bestandsnummer des Verkäufers D7F6-5-M-1789346649-5

Bibliografische Details

Titel: Neural Networks with Keras Cookbook: Over 70...

Verlag: Packt Publishing

Erscheinungsdatum: 2019

Einband: Paperback

Zustand: New

Art des Buches: book

Über diesen Titel

Reseña del editor:

Implement neural network architectures by building them from scratch for multiple real-world applications.

Key Features

• From scratch, build multiple neural network architectures such as CNN, RNN, LSTM in Keras
• Discover tips and tricks for designing a robust neural network to solve real-world problems
• Graduate from understanding the working details of neural networks and master the art of fine-tuning them

Book Description

This book will take you from the basics of neural networks to advanced implementations of architectures using a recipe-based approach.

We will learn about how neural networks work and the impact of various hyper parameters on a network's accuracy along with leveraging neural networks for structured and unstructured data.

Later, we will learn how to classify and detect objects in images. We will also learn to use transfer learning for multiple applications, including a self-driving car using Convolutional Neural Networks.

We will generate images while leveraging GANs and also by performing image encoding. Additionally, we will perform text analysis using word vector based techniques. Later, we will use Recurrent Neural Networks and LSTM to implement chatbot and Machine Translation systems.

Finally, you will learn about transcribing images, audio, and generating captions and also use Deep Q-learning to build an agent that plays Space Invaders game.

By the end of this book, you will have developed the skills to choose and customize multiple neural network architectures for various deep learning problems you might encounter.

What you will learn

• Build multiple advanced neural network architectures from scratch
• Explore transfer learning to perform object detection and classification
• Build self-driving car applications using instance and semantic segmentation
• Understand data encoding for image, text and recommender systems
• Implement text analysis using sequence-to-sequence learning
• Leverage a combination of CNN and RNN to perform end-to-end learning
• Build agents to play games using deep Q-learning

Who this book is for

This intermediate-level book targets beginners and intermediate-level machine learning practitioners and data scientists who have just started their journey with neural networks. This book is for those who are looking for resources to help them navigate through the various neural network architectures; you'll build multiple architectures, with concomitant case studies ordered by the complexity of the problem. A basic understanding of Python programming and a familiarity with basic machine learning are all you need to get started with this book.

Table of Contents

1. Building a Neural Network with TensorFlow and Keras
2. Building a Deep Feedforward Neural Network
3. Applications of Deep Feedforward Neural Networks
4. Building a Deep Convolutional Neural Network
5. Transfer Learning
6. Detecting and Localizing Objects in Images
7. Image Analysis Applications in Self-Driving Cars
8. Image Generation
9. Encoding Inputs
10. Text Analysis Using Word Vectors
11. Building a Recurrent Neural Network
12. Applications of a Many-to-One Architecture RNN
13. Sequence-to-Sequence Learning
14. End-to-End Learning
15. Audio Analysis
16. Reinforcement Learning