Mastering TensorFlow 1.x: Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras

Armando Fandango

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商品描述

Build, scale, and deploy deep neural network models using the star libraries in Python

Key Features

  • Delve into advanced machine learning and deep learning use cases using Tensorflow and Keras
  • Build, deploy, and scale end-to-end deep neural network models in a production environment
  • Learn to deploy TensorFlow on mobile, and distributed TensorFlow on GPU, Clusters, and Kubernetes

Book Description

TensorFlow is the most popular numerical computation library built from the ground up for distributed, cloud, and mobile environments. TensorFlow represents the data as tensors and the computation as graphs.

This book is a comprehensive guide that lets you explore the advanced features of TensorFlow 1.x. Gain insight into TensorFlow Core, Keras, TF Estimators, TFLearn, TF Slim, Pretty Tensor, and Sonnet. Leverage the power of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Throughout the book, you will obtain hands-on experience with varied datasets, such as MNIST, CIFAR-10, PTB, text8, and COCO-Images.

You will learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF Clusters, deploy production models with TensorFlow Serving, and build and deploy TensorFlow models for mobile and embedded devices on Android and iOS platforms. You will see how to call TensorFlow and Keras API within the R statistical software, and learn the required techniques for debugging when the TensorFlow API-based code does not work as expected.

The book helps you obtain in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems. By the end of this guide, you will have mastered the offerings of TensorFlow and Keras, and gained the skills you need to build smarter, faster, and efficient machine learning and deep learning systems.

What you will learn

  • Master advanced concepts of deep learning such as transfer learning, reinforcement learning, generative models and more, using TensorFlow and Keras
  • Perform supervised (classification and regression) and unsupervised (clustering) learning to solve machine learning tasks
  • Build end-to-end deep learning (CNN, RNN, and Autoencoders) models with TensorFlow
  • Scale and deploy production models with distributed and high-performance computing on GPU and clusters
  • Build TensorFlow models to work with multilayer perceptrons using Keras, TFLearn, and R
  • Learn the functionalities of smart apps by building and deploying TensorFlow models on iOS and Android devices
  • Supercharge TensorFlow with distributed training and deployment on Kubernetes and TensorFlow Clusters

Table of Contents

  1. Tensorflow 101
  2. High Level Libraries For TensorFlow
  3. Keras 101
  4. Classical Machine Learning with TensorFlow
  5. Neural Networks and MLP with TensorFlow and Keras
  6. RNN with TensorFlow and Keras
  7. RNN for Time Series Data with TensorFlow and Keras
  8. NLP for Text Data with TensorFlow and Keras
  9. CNN with TensorFlow and Keras
  10. Autoencoder with TensorFlow and Keras
  11. TensorFlow Models in Production with TF Serving
  12. Transfer Learning and Pre-Trained Models
  13. Deep Reinforcement Learning
  14. Generative Adversarial Networks
  15. Distributed Models with TensorFlow Clusters
  16. TensorFlow on Mobile and Embedded Platforms
  17. TensorFlow and Keras in R
  18. Debugging TensorFlow Models
  19. Appendix A: TPU

商品描述(中文翻譯)

使用Python中的頂尖程式庫建立、擴展和部署深度神經網路模型

主要特點:
- 使用TensorFlow和Keras深入研究進階機器學習和深度學習案例
- 在生產環境中建立、部署和擴展端到端的深度神經網路模型
- 學習在移動設備上部署TensorFlow,以及在GPU、集群和Kubernetes上部署分散式TensorFlow

書籍描述:
TensorFlow是一個針對分散式、雲端和移動環境從頭開始建立的最受歡迎的數值計算程式庫。TensorFlow將數據表示為張量,計算表示為圖形。

本書是一本全面的指南,讓您探索TensorFlow 1.x的高級功能。深入瞭解TensorFlow Core、Keras、TF Estimators、TFLearn、TF Slim、Pretty Tensor和Sonnet。利用TensorFlow和Keras的強大功能,建立深度學習模型,使用轉移學習、生成對抗網絡和深度強化學習等概念。在整本書中,您將通過各種數據集(如MNIST、CIFAR-10、PTB、text8和COCO-Images)獲得實踐經驗。

您將學習TensorFlow 1.x的高級功能,例如使用TF Clusters進行分散式TensorFlow,使用TensorFlow Serving部署生產模型,以及在Android和iOS平台上建立和部署TensorFlow模型以供移動和嵌入式設備使用。您將了解如何在R統計軟件中調用TensorFlow和Keras API,並學習當TensorFlow API的代碼不按預期工作時所需的調試技術。

本書幫助您深入瞭解TensorFlow,使您成為解決人工智能問題的專家。通過本指南,您將掌握TensorFlow和Keras的功能,並獲得構建更智能、更快速、更高效的機器學習和深度學習系統所需的技能。

您將學到的內容:
- 使用TensorFlow和Keras掌握深度學習的高級概念,如轉移學習、強化學習、生成模型等
- 使用TensorFlow解決監督(分類和回歸)和非監督(聚類)學習的機器學習任務
- 使用TensorFlow構建端到端的深度學習模型(CNN、RNN和自編碼器)
- 在GPU和集群上進行分散式和高性能計算,構建和部署生產模型
- 使用Keras、TFLearn和R構建與多層感知器一起工作的TensorFlow模型
- 通過在iOS和Android設備上建立和部署TensorFlow模型,學習智能應用程序的功能
- 在Kubernetes和TensorFlow集群上進行分散式訓練和部署,提升TensorFlow的性能
- 在R中使用TensorFlow和Keras API,解決人工智能問題
- 調試TensorFlow模型的技巧

目錄:
1. TensorFlow 101
2. TensorFlow的高級程式庫
3. Keras 101
4. 使用TensorFlow進行經典機器學習
5. 使用TensorFlow和Keras進行神經網絡和多層感知器
6. 使用TensorFlow和Keras進行RNN
7. 使用TensorFlow和Keras進行時間序列數據的RNN
8. 使用TensorFlow和Keras進行文本數據的NLP
9. 使用TensorFlow和Keras進行CNN
10. 使用TensorFlow和Keras進行自編碼器
11. 使用TF Serving在生產環境中部署TensorFlow模型
12. 轉移學習和預訓練模型
13. 深度強化學習
14. 生成對抗網絡
15. 使用TensorFlow集群進行分散式模型
16. 在移動和嵌入式平台上的TensorFlow
17. 在R中使用TensorFlow和Keras
18. 調試TensorFlow模型
附錄A: TPU