Keras 2.x Projects: 9 projects demonstrating faster experimentation of neural network and deep learning applications using Keras

Giuseppe Ciaburro

  • 出版商: Packt Publishing
  • 出版日期: 2018-12-31
  • 售價: $1,530
  • 貴賓價: 9.5$1,454
  • 語言: 英文
  • 頁數: 394
  • 裝訂: Paperback
  • ISBN: 1789536642
  • ISBN-13: 9781789536645
  • 相關分類: DeepLearning
  • 立即出貨 (庫存=1)

買這商品的人也買了...

商品描述

Demonstrate fundamentals of Deep Learning and neural network methodologies using Keras 2.x

Key Features

  • Experimental projects showcasing the implementation of high-performance deep learning models with Keras.
  • Use-cases across reinforcement learning, natural language processing, GANs and computer vision.
  • Build strong fundamentals of Keras in the area of deep learning and artificial intelligence.

Book Description

Keras 2.x Projects explains how to leverage the power of Keras to build and train state-of-the-art deep learning models through a series of practical projects that look at a range of real-world application areas.

To begin with, you will quickly set up a deep learning environment by installing the Keras library. Through each of the projects, you will explore and learn the advanced concepts of deep learning and will learn how to compute and run your deep learning models using the advanced offerings of Keras. You will train fully-connected multilayer networks, convolutional neural networks, recurrent neural networks, autoencoders and generative adversarial networks using real-world training datasets. The projects you will undertake are all based on real-world scenarios of all complexity levels, covering topics such as language recognition, stock volatility, energy consumption prediction, faster object classification for self-driving vehicles, and more.

By the end of this book, you will be well versed with deep learning and its implementation with Keras. You will have all the knowledge you need to train your own deep learning models to solve different kinds of problems.

What you will learn

  • Apply regression methods to your data and understand how the regression algorithm works
  • Understand the basic concepts of classification methods and how to implement them in the Keras environment
  • Import and organize data for neural network classification analysis
  • Learn about the role of rectified linear units in the Keras network architecture
  • Implement a recurrent neural network to classify the sentiment of sentences from movie reviews
  • Set the embedding layer and the tensor sizes of a network

Who this book is for

If you are a data scientist, machine learning engineer, deep learning practitioner or an AI engineer who wants to build speedy intelligent applications with minimal lines of codes, then this book is the best fit for you. Sound knowledge of machine learning and basic familiarity with Keras library would be useful.

Table of Contents

  1. Getting Started With Keras
  2. Modeling Real Estate Market Using Regression Analysis
  3. Heart Disease Classification With A Neural Network
  4. Concrete Quality Prediction Using Deep Neural Network
  5. Fashion Articles Recognition By A Convolutional Neural Network
  6. Movie Reviews Sentiment Analysis Using Recurrent Neural Network
  7. Stock Volatility Forecasting Using Long Short-Term Memory
  8. Reconstruction Of Handwritten Digit Images Using Autoencoder
  9. Robot control system using Deep Reinforcement Learning
  10. Reuters newswire topics classifier in Keras
  11. What is next?

商品描述(中文翻譯)

展示使用Keras 2.x的深度學習和神經網絡方法的基礎。

主要特點:
- 通過使用Keras實現高性能深度學習模型的實驗項目。
- 涵蓋強化學習、自然語言處理、GAN和計算機視覺等領域的應用案例。
- 在深度學習和人工智能領域建立堅實的Keras基礎。

書籍描述:
《Keras 2.x Projects》解釋了如何利用Keras建立和訓練最先進的深度學習模型,通過一系列實際項目來探索各種實際應用領域。

首先,您將通過安裝Keras庫快速建立深度學習環境。通過每個項目,您將探索和學習深度學習的高級概念,並學習如何使用Keras的高級功能計算和運行深度學習模型。您將使用真實世界的訓練數據集訓練全連接多層網絡、卷積神經網絡、循環神經網絡、自編碼器和生成對抗網絡。這些項目都基於各種複雜程度的真實場景,涵蓋語言識別、股票波動性、能源消耗預測、自駕車的更快物體分類等主題。

通過閱讀本書,您將熟悉深度學習及其在Keras中的實現。您將擁有訓練自己的深度學習模型解決不同問題的所有知識。

學到的內容:
- 將回歸方法應用於數據並了解回歸算法的工作原理。
- 了解分類方法的基本概念以及如何在Keras環境中實現它們。
- 導入和組織用於神經網絡分類分析的數據。
- 了解Keras網絡架構中修正的線性單元的作用。
- 實現循環神經網絡以分類電影評論的情感。
- 設置網絡的嵌入層和張量大小。

本書適合對於數據科學家、機器學習工程師、深度學習從業者或人工智能工程師,希望用最少的代碼構建高效智能應用的讀者。對機器學習有扎實的知識和對Keras庫有基本的熟悉將會有所幫助。

目錄:
1. 開始使用Keras
2. 使用回歸分析建模房地產市場
3. 使用神經網絡進行心臟病分類
4. 使用深度神經網絡預測混凝土質量
5. 使用卷積神經網絡識別時尚文章
6. 使用循環神經網絡進行電影評論情感分析
7. 使用長短期記憶預測股票波動性
8. 使用自編碼器重建手寫數字圖像
9. 使用深度強化學習控制機器人系統
10. 在Keras中進行路透社新聞主題分類
11. 下一步是什麼?