Deep Learning: 2 Manuscripts - Deep Learning With Keras And Convolutional Neural Networks In Python

Frank Millstein

  • 出版商: W. W. Norton
  • 出版日期: 2018-03-21
  • 售價: $1,110
  • 貴賓價: 9.5$1,055
  • 語言: 英文
  • 頁數: 260
  • 裝訂: Paperback
  • ISBN: 1986718271
  • ISBN-13: 9781986718271
  • 相關分類: DeepLearningPython程式語言
  • 無法訂購

商品描述

Deep Learning - 2 BOOK BUNDLE!!

Deep Learning with Keras

This book will introduce you to various supervised and unsupervised deep learning algorithms like the multilayer perceptron, linear regression and other more advanced deep convolutional and recurrent neural networks. You will also learn about image processing, handwritten recognition, object recognition and much more.
Furthermore, you will get familiar with recurrent neural networks like LSTM and GAN as you explore processing sequence data like time series, text, and audio.
The book will definitely be your best companion on this great deep learning journey with Keras introducing you to the basics you need to know in order to take next steps and learn more advanced deep neural networks.

Here Is a Preview of What You’ll Learn Here…

  • The difference between deep learning and machine learning
  • Deep neural networks
  • Convolutional neural networks
  • Building deep learning models with Keras
  • Multi-layer perceptron network models
  • Activation functions
  • Handwritten recognition using MNIST
  • Solving multi-class classification problems
  • Recurrent neural networks and sequence classification
  • And much more...

Convolutional Neural Networks in Python

This book covers the basics behind Convolutional Neural Networks by introducing you to this complex world of deep learning and artificial neural networks in a simple and easy to understand way. It is perfect for any beginner out there looking forward to learning more about this machine learning field. This book is all about how to use convolutional neural networks for various image, object and other common classification problems in Python. Here, we also take a deeper look into various Keras layer used for building CNNs we take a look at different activation functions and much more, which will eventually lead you to creating highly accurate models able of performing great task results on various image classification, object classification and other problems. Therefore, at the end of the book, you will have a better insight into this world, thus you will be more than prepared to deal with more complex and challenging tasks on your own.

Here Is a Preview of What You’ll Learn In This Book…

  • Convolutional neural networks structure
  • How convolutional neural networks actually work
  • Convolutional neural networks applications
  • The importance of convolution operator
  • Different convolutional neural networks layers and their importance
  • Arrangement of spatial parameters
  • How and when to use stride and zero-padding
  • Method of parameter sharing
  • Matrix multiplication and its importance
  • Pooling and dense layers
  • Introducing non-linearity relu activation function
  • How to train your convolutional neural network models using backpropagation
  • How and why to apply dropout
  • CNN model training process
  • How to build a convolutional neural network
  • Generating predictions and calculating loss functions
  • How to train and evaluate your MNIST classifier
  • How to build a simple image classification CNN
  • And much, much more!

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商品描述(中文翻譯)

深度學習 - 2本書籍組合包!


使用Keras進行深度學習


本書將介紹各種監督式和非監督式的深度學習演算法,如多層感知器、線性回歸以及更高級的深度卷積和循環神經網絡。您還將學習圖像處理、手寫識別、物體識別等等。此外,您還將通過處理序列數據(如時間序列、文本和音頻)來熟悉循環神經網絡(如LSTM和GAN)。本書將成為您在深度學習旅程中的最佳伴侶,引領您了解基礎知識,以便進一步學習更高級的深度神經網絡。


以下是您將在本書中學到的內容預覽...



  • 深度學習和機器學習的區別

  • 深度神經網絡

  • 卷積神經網絡

  • 使用Keras構建深度學習模型

  • 多層感知器網絡模型

  • 激活函數

  • 使用MNIST進行手寫識別

  • 解決多類別分類問題

  • 循環神經網絡和序列分類

  • 以及更多...


使用Python的卷積神經網絡


本書以簡單易懂的方式介紹了卷積神經網絡的基礎知識,讓您了解深度學習和人工神經網絡這個複雜的世界。對於任何想要更多了解這個機器學習領域的初學者來說,這本書非常適合。本書介紹了如何在Python中使用卷積神經網絡解決各種圖像、物體和其他常見分類問題。在這裡,我們還深入研究了用於構建CNN的各種Keras層,介紹了不同的激活函數等等,最終使您能夠創建高度準確的模型,能夠在各種圖像分類、物體分類和其他問題上執行出色的任務結果。因此,在本書結束時,您將對這個領域有更深入的了解,因此您將更加準備好自己應對更複雜和具有挑戰性的任務。

以下是您將在本書中學到的內容預覽...



  • 卷積神經網絡結構

  • 卷積神經網絡的工作原理

  • 卷積神經網絡的應用

  • 卷積運算符的重要性

  • 不同的卷積神經網絡層及其重要性

  • 空間參數的排列

  • 何時以及如何使用步幅和零填充

  • 參數共享的方法

  • 矩陣乘法及其重要性

  • 池化和全連接層

  • 介紹非線性relu激活函數

  • 如何使用反向傳播訓練卷積神經網絡模型

  • 如何以及為什麼應用dropout

  • CNN模型訓練過程

  • 如何構建卷積神經網絡

  • 生成預測和計算損失函數

  • 如何訓練和評估您的MNIST分類器

  • 如何構建一個簡單的圖像分類CNN

  • 以及更多更多!


立即獲取這本書籍組合,並節省金錢!