Hands-On Convolutional Neural Networks with TensorFlow: Solve computer vision problems with modeling in TensorFlow and Python.

Iffat Zafar, Giounona Tzanidou, Richard Burton, Nimesh Patel, Leonardo Araujo

買這商品的人也買了...

相關主題

商品描述

Learn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems with this practical guide on training CNNs for image classification, image recognition, object detection and many computer vision challenges.

Key Features

  • Learn the fundamentals of Convolutional Neural Networks
  • Harness Python and Tensorflow to train CNNs
  • Build scalable deep learning models that can process millions of items

Book Description

Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time!

We start with an overview of popular machine learning and deep learning models, and then get you set up with a TensorFlow development environment. This environment is the basis for implementing and training deep learning models in later chapters. Then, you will use Convolutional Neural Networks to work on problems such as image classification, object detection, and semantic segmentation.

After that, you will use transfer learning to see how these models can solve other deep learning problems. You will also get a taste of implementing generative models such as autoencoders and generative adversarial networks.

Later on, you will see useful tips on machine learning best practices and troubleshooting. Finally, you will learn how to apply your models on large datasets of millions of images.

What you will learn

  • Train machine learning models with TensorFlow
  • Create systems that can evolve and scale during their life cycle
  • Use CNNs in image recognition and classification
  • Use TensorFlow for building deep learning models
  • Train popular deep learning models
  • Fine-tune a neural network to improve the quality of results with transfer learning
  • Build TensorFlow models that can scale to large datasets and systems

Who this book is for

This book is for Software Engineers, Data Scientists, or Machine Learning practitioners who want to use CNNs for solving real-world problems. Knowledge of basic machine learning concepts, linear algebra and Python will help.

Table of Contents

  1. Setup and introduction to TensorFlow
  2. Deep Learning and Convolutional Neural Networks
  3. Image Classification in Tensorflow
  4. Object Detection and Segmentation
  5. VGG, Inception Modules, Residuals, and MobileNets
  6. Autoencoders, Variational Autoencoders, and Generative Adversarial Networks
  7. Transfer Learning
  8. Machine Learning Best Practices and Troubleshooting
  9. Training at Scale

商品描述(中文翻譯)

學習如何應用TensorFlow解決各種深度學習和機器學習問題,本實用指南將教你如何訓練卷積神經網絡(CNN)進行圖像分類、圖像識別、物體檢測以及其他許多計算機視覺挑戰。

主要特點:
- 學習卷積神經網絡的基礎知識
- 利用Python和TensorFlow訓練卷積神經網絡
- 構建可處理數百萬項目的可擴展深度學習模型

書籍描述:
卷積神經網絡(CNN)是計算機視覺應用中最受歡迎的架構之一。本書通過解決深度學習中的實際問題,介紹了CNN,同時教你如何在流行的Python庫TensorFlow中實現它們。通過本書,你將能夠迅速訓練CNN。

我們首先概述了流行的機器學習和深度學習模型,然後幫助你建立TensorFlow開發環境。這個環境是後續章節中實現和訓練深度學習模型的基礎。接下來,你將使用卷積神經網絡解決圖像分類、物體檢測和語義分割等問題。

之後,你將使用遷移學習來解決其他深度學習問題。你還將了解到如何實現自編碼器和生成對抗網絡等生成模型。

隨後,你將獲得有關機器學習最佳實踐和故障排除的實用技巧。最後,你將學習如何在數百萬張圖像的大型數據集上應用你的模型。

你將學到:
- 使用TensorFlow訓練機器學習模型
- 創建在生命周期中可以演進和擴展的系統
- 在圖像識別和分類中使用卷積神經網絡
- 使用TensorFlow構建深度學習模型
- 訓練流行的深度學習模型
- 通過遷移學習微調神經網絡以提高結果質量
- 構建可擴展到大型數據集和系統的TensorFlow模型

本書適合軟體工程師、數據科學家或機器學習從業者,他們希望使用CNN解決實際問題。具備基本的機器學習概念、線性代數和Python知識將對閱讀有所幫助。

目錄:
1. 設置和介紹TensorFlow
2. 深度學習和卷積神經網絡
3. 在TensorFlow中進行圖像分類
4. 物體檢測和分割
5. VGG、Inception模塊、殘差和MobileNets
6. 自編碼器、變分自編碼器和生成對抗網絡
7. 遷移學習
8. 機器學習最佳實踐和故障排除
9. 大規模訓練