Deep Learning Quick Reference: Over 10 secret hacks for training and optimizing deep neural networks with TensorFlow and Keras

Mike Bernico

  • 出版商: Packt Publishing
  • 出版日期: 2018-03-13
  • 售價: $1,770
  • 貴賓價: 9.5$1,682
  • 語言: 英文
  • 頁數: 272
  • 裝訂: Paperback
  • ISBN: 1788837991
  • ISBN-13: 9781788837996
  • 相關分類: DeepLearningTensorFlow
  • 海外代購書籍(需單獨結帳)

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商品描述

Dive deeper into neural networks and get your models trained, optimized with this quick reference guide

Key Features

  • A quick reference to all important deep learning concepts and their implementations
  • Essential tips, tricks, and hacks to train a variety of deep learning models such as CNNs, RNNs, LSTMs, and more
  • Supplemented with essential mathematics and theory, every chapter provides best practices and safe choices for training and fine-tuning your models in Keras and Tensorflow.

Book Description

Deep learning has become an essential necessity to enter the world of artificial intelligence. With this book deep learning techniques will become more accessible, practical, and relevant to practicing data scientists. It moves deep learning from academia to the real world through practical examples.

You will learn how Tensor Board is used to monitor the training of deep neural networks and solve binary classification problems using deep learning. Readers will then learn to optimize hyperparameters in their deep learning models. The book then takes the readers through the practical implementation of training CNN's, RNN's, and LSTM's with word embeddings and seq2seq models from scratch. Later the book explores advanced topics such as Deep Q Network to solve an autonomous agent problem and how to use two adversarial networks to generate artificial images that appear real. For implementation purposes, we look at popular Python-based deep learning frameworks such as Keras and Tensorflow, Each chapter provides best practices and safe choices to help readers make the right decision while training deep neural networks.

By the end of this book, you will be able to solve real-world problems quickly with deep neural networks.

What you will learn

  • Solve regression and classification challenges with TensorFlow and Keras
  • Learn to use Tensor Board for monitoring neural networks and its training
  • Optimize hyperparameters and safe choices/best practices
  • Build CNN's, RNN's, and LSTM's and using word embedding from scratch
  • Build and train seq2seq models for machine translation and chat applications.
  • Understanding Deep Q networks and how to use one to solve an autonomous agent problem.
  • Explore Deep Q Network and address autonomous agent challenges.

Who This Book Is For

If you are a Data Scientist or a Machine Learning expert, then this book is a very useful read in training your advanced machine learning and deep learning models. You can also refer this book if you are stuck in-between the neural network modeling and need immediate assistance in getting accomplishing the task smoothly. Some prior knowledge of Python and tight hold on the basics of machine learning is required.

Table of Contents

  1. The Building Blocks of Deep Learning
  2. Using Deep Learning To Solve Regression Problems
  3. Monitoring Network Training Using Tensor Board
  4. Using Deep Learning To Solve Binary Classification Problems
  5. Using Keras To Solve MultiClass Classification Problems
  6. HyperParameter Optimization
  7. Training a CNN From Scratch
  8. Transfer Learning with Pretrained CNNs
  9. Training an RNN from scratch
  10. Training LSTMs with Word Embeddings From Scratch
  11. Training Seq2Seq Models
  12. Using Deep Reinforcement Learning
  13. Deep Convolutional Generative Adversarial Networks

商品描述(中文翻譯)

深入探索神經網絡,並使用這本快速參考指南來訓練和優化您的模型。

主要特點:

- 所有重要的深度學習概念及其實現的快速參考
- 訓練各種深度學習模型(如CNN、RNN、LSTM等)的基本技巧和技巧
- 每章補充了必要的數學和理論,提供了在Keras和Tensorflow中訓練和微調模型的最佳實踐和安全選擇。

書籍描述:

深度學習已成為進入人工智能世界的必需品。通過這本書,深度學習技術將變得更加易於理解、實用和與實踐的數據科學家相關。它通過實際示例將深度學習從學術界帶入現實世界。

您將學習如何使用Tensor Board監控深度神經網絡的訓練,並使用深度學習解決二元分類問題。然後,讀者將學習優化深度學習模型的超參數。本書還從頭開始介紹了使用詞嵌入和seq2seq模型訓練CNN、RNN和LSTM的實際實現。隨後,本書探討了高級主題,如使用Deep Q Network解決自主代理問題,以及如何使用兩個對抗性網絡生成看起來真實的人工圖像。為了實現目的,我們將研究流行的基於Python的深度學習框架,如Keras和Tensorflow。每章都提供了最佳實踐和安全選擇,以幫助讀者在訓練深度神經網絡時做出正確的決策。

通過閱讀本書,您將能夠快速解決現實世界的問題,並使用深度神經網絡。

您將學到什麼:

- 使用TensorFlow和Keras解決回歸和分類問題
- 學習使用Tensor Board監控神經網絡及其訓練
- 優化超參數和安全選擇/最佳實踐
- 從頭開始構建CNN、RNN和LSTM,並使用詞嵌入
- 構建和訓練seq2seq模型,用於機器翻譯和聊天應用
- 了解Deep Q網絡以及如何使用它解決自主代理問題
- 探索Deep Q網絡並解決自主代理問題

本書適合對象:

如果您是數據科學家或機器學習專家,那麼本書對於訓練高級機器學習和深度學習模型非常有用。如果您在神經網絡建模過程中遇到困難,並需要即時協助順利完成任務,您也可以參考本書。需要具備Python的基礎知識和機器學習基礎。

目錄:

1. 深度學習的基本構建塊
2. 使用深度學習解決回歸問題
3. 使用Tensor Board監控網絡訓練
4. 使用深度學習解決二元分類問題
5. 使用Keras解決多類分類問題
6. 超參數優化
7. 從頭開始訓練CNN
8. 使用預訓練的CNN進行轉移學習
9. 從頭開始訓練RNN
10. 使用詞嵌入從頭開始訓練LSTM
11. 訓練Seq2Seq模型
12. 使用深度強化學習
13. 深度卷積生成對抗網絡