Neural Networks with Keras Cookbook

V Kishore Ayyadevara

  • 出版商: Packt Publishing
  • 出版日期: 2019-02-28
  • 售價: $1,380
  • 貴賓價: 9.5$1,311
  • 語言: 英文
  • 頁數: 568
  • 裝訂: Paperback
  • ISBN: 1789346649
  • ISBN-13: 9781789346640
  • 相關分類: DeepLearning
  • 立即出貨 (庫存=1)

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商品描述

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.

商品描述(中文翻譯)

主要特點:

- 使用Keras從頭開始構建多個神經網絡架構,如CNN、RNN、LSTM
- 探索設計強大神經網絡以解決現實世界問題的技巧和訣竅
- 理解神經網絡的工作細節,並精通微調神經網絡的技巧

書籍描述:

本書將以基於配方的方法,從神經網絡的基礎知識到高級架構的實現,帶領讀者深入了解神經網絡。

我們將學習神經網絡的工作原理,以及各種超參數對網絡準確性的影響,並利用神經網絡處理結構化和非結構化數據。

隨後,我們將學習如何對圖像進行分類和檢測。我們還將學習使用遷移學習進行多種應用,包括使用卷積神經網絡實現自駕車。

我們將利用生成對抗網絡(GAN)生成圖像,並進行圖像編碼。此外,我們還將使用基於詞向量的技術進行文本分析。隨後,我們將使用循環神經網絡(RNN)和LSTM實現聊天機器人和機器翻譯系統。

最後,您將學習如何轉錄圖像、音頻並生成字幕,並使用深度Q學習構建玩太空侵略者遊戲的代理。

通過閱讀本書,您將學習選擇和自定義多種神經網絡架構,以應對可能遇到的各種深度學習問題。

你將學到什麼:

- 從頭開始構建多個高級神經網絡架構
- 探索遷移學習以進行對象檢測和分類
- 使用實例和語義分割構建自駕車應用
- 理解圖像、文本和推薦系統的數據編碼
- 使用序列到序列學習進行文本分析
- 結合CNN和RNN進行端到端學習
- 使用深度Q學習構建遊戲代理

適合對象:

本書適合初學者和中級機器學習從業者和數據科學家,他們剛開始接觸神經網絡。本書適合那些正在尋找幫助他們在各種神經網絡架構中導航的資源;您將構建多個架構,並按照問題的複雜性進行相應的案例研究。您只需要基本的Python編程知識和對基本機器學習的熟悉即可開始閱讀本書。

目錄大綱

  1. Building a neural network with Tensorflow and Keras
  2. Building a deep neural network
  3. Applications of deep feed forward neural networks
  4. Building a deep convolutional neural networ
  5. Transfer Learning
  6. Object detection and localization
  7. Applications of image analysis in self-driving car
  8. Image generation
  9. Encoding inputs
  10. Text analysis using word vectors
  11. Building a Recurrent neural Network
  12. Applications of many to one architecture based RNN
  13. Sequence to Sequence learning
  14. End to end learning
  15. Audio analysis
  16. Reinforcement learning

目錄大綱(中文翻譯)

使用Tensorflow和Keras建立神經網絡
建立深度神經網絡
深度前饋神經網絡的應用
建立深度卷積神經網絡
遷移學習
物體檢測和定位
圖像分析在自駕車中的應用
圖像生成
編碼輸入
使用詞向量進行文本分析
建立循環神經網絡
基於多對一架構的循環神經網絡的應用
序列到序列學習
端到端學習
音頻分析
強化學習