Python Deep Learning Projects: Investigate and Implement Deep Learning Architectures for building intelligent systems

Matthew Lamons, Rahul Kumar, Abhishek Nagaraja

  • 出版商: Packt Publishing
  • 出版日期: 2018-10-31
  • 售價: $1,430
  • 貴賓價: 9.5$1,359
  • 語言: 英文
  • 頁數: 472
  • 裝訂: Paperback
  • ISBN: 1788997093
  • ISBN-13: 9781788997096
  • 相關分類: PythonDeepLearning 深度學習
  • 下單後立即進貨 (約1~2週)

相關主題

商品描述

Insightful practical projects to master deep learning and neural network architectures using Python, Keras and MXNet

Key Features

  • Rich projects on computer vision, NLP, and image processing
  • Build your own neural network and explore innumerable possibilities with deep learning 
  • Explore the power of Python for deep learning in various scenarios using insightful projects

Book Description

Deep Learning has quietly revolutionized every field of Artificial Intelligence, enabling the development of applications that a few years ago were considered almost impossible.

This book will provide all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision. Each new project will build upon the experience and knowledge accumulated in the previous ones, allowing the reader to progressively master the subject.

You will learn neural network models implementing a text classifier system using Recurrent Neural network model (RNN) and optimize it to understand the shortcomings you might come across while implementing a simple deep learning system. If you are looking to implement intelligent systems like Automatic Machine Translation, Handwriting Generation, Character Text Generation, Object Classification in Photographs, Colorization of Images, Image Caption Generation, Character Text Generation or Automatic Game Playing into your application then this book is for you.

By the end of this book, you will come across various Recurrent and Convolutional Neural network implementations with practical hands-on to modeling concepts like regularization, Gradient clipping, and gradient normalization, LSTM, Bidirectional RNN's through a series engaging projects.

What you will learn

  • Set up a Deep Learning development environment on AWS, using GPU-powered instances and the Deep Learning AMI
  • Implement Sequence to Sequence Networks for modeling natural language processing
  • Develop an end-to-end speech recognition system
  • Build a system for pixel-wise semantic labeling of an image
  • Develop a system that generates images and their regions

Who This Book Is For

This book is for developers, data scientists, or enthusiasts, who have sound knowledge of python programming, basics of machine learning, and want to break into deep learning, either for opening a new career opportunity or for realizing their own AI projects.