Neural Networks with Keras Cookbook: Over 70 recipes leveraging deep learning techniques across image,text,audio,and game bots
Authors: V Kishore Ayyadevara
ISBN-10: 1789346649
ISBN-13: 9781789346640
Released: 2019-02-28
Print Length 页数: 568 pages
Book Description
More Information
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
About
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.
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
contents
1 Building a Feedforward Neural Network
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
