Hands-On Neural Networks with Keras

Niloy Purkait

  • 出版商: Packt Publishing
  • 出版日期: 2019-03-30
  • 售價: $1,380
  • 貴賓價: 9.5$1,311
  • 語言: 英文
  • 頁數: 462
  • 裝訂: Paperback
  • ISBN: 1789536081
  • ISBN-13: 9781789536089
  • 相關分類: DeepLearning
  • 立即出貨 (庫存=1)



Key Features

  • Design and create neural network architectures on different domains using Keras
  • Integrate neural network models in your applications using this highly practical guide
  • Get ready for the future of neural networks through transfer learning and predicting multi network models

Book Description

Neural networks are used to solve a wide range of problems in different areas of AI and deep learning.

Hands-On Neural Networks with Keras will start with teaching you about the core concepts of neural networks. You will delve into combining different neural network models and work with real-world use cases, including computer vision, natural language understanding, synthetic data generation, and many more. Moving on, you will become well versed with convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, autoencoders, and generative adversarial networks (GANs) using real-world training datasets. We will examine how to use CNNs for image recognition, how to use reinforcement learning agents, and many more. We will dive into the specific architectures of various networks and then implement each of them in a hands-on manner using industry-grade frameworks.

By the end of this book, you will be highly familiar with all prominent deep learning models and frameworks, and the options you have when applying deep learning to real-world scenarios and embedding artificial intelligence as the core fabric of your organization.

What you will learn

  • Understand the fundamental nature and workflow of predictive data modeling
  • Explore how different types of visual and linguistic signals are processed by neural networks
  • Dive into the mathematical and statistical ideas behind how networks learn from data
  • Design and implement various neural networks such as CNNs, LSTMs, and GANs
  • Use different architectures to tackle cognitive tasks and embed intelligence in systems
  • Learn how to generate synthetic data and use augmentation strategies to improve your models
  • Stay on top of the latest academic and commercial developments in the field of AI

Who this book is for

This book is for machine learning practitioners, deep learning researchers and AI enthusiasts who are looking to get well versed with different neural network architecture using Keras. Working knowledge of Python programming language is mandatory.



  • 使用Keras在不同領域設計和創建神經網絡架構

  • 通過這本高度實用的指南,在應用程序中集成神經網絡模型

  • 通過轉移學習和預測多網絡模型,為神經網絡的未來做好準備






  • 了解預測數據建模的基本性質和工作流程

  • 探索神經網絡如何處理不同類型的視覺和語言信號

  • 深入研究網絡如何從數據中學習的數學和統計思想

  • 設計和實現各種神經網絡,如CNN、LSTM和GAN

  • 使用不同的架構來應對認知任務並將智能嵌入系統中

  • 學習如何生成合成數據並使用增強策略改進模型

  • 緊跟人工智能領域的最新學術和商業發展




  1. Overview of Neural Networks
  2. A Deeper Dive into Neural Networks
  3. Signal Processing - Data Analysis with Neural Networks
  4. Convolutional Neural Networks
  5. Recurrent Neural Networks
  6. Long Short-Term Memory Networks
  7. Reinforcement Learning with Deep Q-Networks
  8. Autoencoders
  9. Generative Networks
  10. Contemplating Present and Future Developments


- 神經網絡概述
- 深入探討神經網絡
- 信號處理 - 使用神經網絡進行數據分析
- 卷積神經網絡
- 遞歸神經網絡
- 長短期記憶網絡
- 使用深度 Q 網絡進行強化學習
- 自編碼器
- 生成網絡
- 現在和未來發展的思考