Machine Learning with Tensorflow, 2/e (Paperback)

Chris, Mattmann A.

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商品描述

Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library.

Summary
Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Written by NASA JPL Deputy CTO and Principal Data Scientist Chris Mattmann, all examples are accompanied by downloadable Jupyter Notebooks for a hands-on experience coding TensorFlow with Python. New and revised content expands coverage of core machine learning algorithms, and advancements in neural networks such as VGG-Face facial identification classifiers and deep speech classifiers.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology
Supercharge your data analysis with machine learning ML algorithms automatically improve as they process data, so results get better over time. You don't have to be a mathematician to use ML: Tools like Google's TensorFlow library help with complex calculations so you can focus on getting the answers you need.

About the book
Machine Learning with TensorFlow, Second Edition is a fully revised guide to building machine learning models using Python and TensorFlow. You'll apply core ML concepts to real-world challenges, such as sentiment analysis, text classification, and image recognition. Hands-on examples illustrate neural network techniques for deep speech processing, facial identification, and auto-encoding with CIFAR-10.

What's inside

Machine Learning with TensorFlow
Choosing the best ML approaches
Visualizing algorithms with TensorBoard
Sharing results with collaborators
Running models in Docker

About the reader
Requires intermediate Python skills and knowledge of general algebraic concepts like vectors and matrices. Examples use the super-stable 1.15.x branch of TensorFlow and TensorFlow 2.x.

About the author
Chris Mattmann is the Division Manager of the Artificial Intelligence, Analytics, and Innovation Organization at NASA Jet Propulsion Lab. The first edition of this book was written by Nishant Shukla with Kenneth Fricklas.

Table of Contents

PART 1 - YOUR MACHINE-LEARNING RIG

1 A machine-learning odyssey

2 TensorFlow essentials

PART 2 - CORE LEARNING ALGORITHMS

3 Linear regression and beyond

4 Using regression for call-center volume prediction

5 A gentle introduction to classification

6 Sentiment classification: Large movie-review dataset

7 Automatically clustering data

8 Inferring user activity from Android accelerometer data

9 Hidden Markov models

10 Part-of-speech tagging and word-sense disambiguation

PART 3 - THE NEURAL NETWORK PARADIGM

11 A peek into autoencoders

12 Applying autoencoders: The CIFAR-10 image dataset

13 Reinforcement learning

14 Convolutional neural networks

15 Building a real-world CNN: VGG-Face ad VGG-Face Lite

16 Recurrent neural networks

17 LSTMs and automatic speech recognition

18 Sequence-to-sequence models for chatbots

19 Utility landscape

商品描述(中文翻譯)

《使用 TensorFlow 進行機器學習,第二版》已更新新的程式碼、新的專案和新的章節,為讀者提供了機器學習概念和 TensorFlow 函式庫的堅實基礎。

摘要
《使用 TensorFlow 進行機器學習,第二版》已更新新的程式碼、新的專案和新的章節,為讀者提供了機器學習概念和 TensorFlow 函式庫的堅實基礎。本書由 NASA JPL 副首席技術官兼首席資料科學家 Chris Mattmann 撰寫,所有範例均附有可下載的 Jupyter Notebook,以便進行使用 Python 編寫 TensorFlow 的實踐編碼。新的和修訂的內容擴展了核心機器學習演算法的涵蓋範圍,以及神經網絡中的進展,例如 VGG-Face 臉部識別分類器和深度語音分類器。

購買印刷版書籍可獲得 Manning Publications 提供的 PDF、Kindle 和 ePub 格式的免費電子書。

關於技術
使用機器學習的數據分析能力,機器學習算法在處理數據時會自動改進,因此結果會隨著時間的推移變得更好。您不需要成為數學家才能使用機器學習:像 Google 的 TensorFlow 函式庫這樣的工具可以幫助進行複雜的計算,讓您專注於獲得所需的答案。

關於本書
《使用 TensorFlow 進行機器學習,第二版》是一本使用 Python 和 TensorFlow 構建機器學習模型的全面修訂指南。您將應用核心機器學習概念解決現實世界的挑戰,例如情感分析、文本分類和圖像識別。實例演示了用於深度語音處理、臉部識別和 CIFAR-10 的神經網絡技術。

內容簡介
使用 TensorFlow 進行機器學習
選擇最佳的機器學習方法
使用 TensorBoard 可視化算法
與合作者分享結果
在 Docker 中運行模型

讀者對象
需要具備中級 Python 技能和一般代數概念(如向量和矩陣)的知識。示例使用超穩定的 TensorFlow 1.15.x 分支和 TensorFlow 2.x。

作者簡介
Chris Mattmann 是 NASA Jet Propulsion Lab 的人工智能、分析和創新組織的部門經理。本書的第一版由 Nishant Shukla 和 Kenneth Fricklas 共同撰寫。

目錄
第 1 部分 - 您的機器學習工具
1 機器學習之旅
2 TensorFlow 基礎知識

第 2 部分 - 核心學習演算法
3 線性回歸及其擴展
4 使用回歸進行呼叫中心量預測
5 分類的簡介
6 情感分類:大型電影評論數據集
7 自動聚類數據
8 從 Android 加速度計數據推斷用戶活動
9 隱馬爾可夫模型
10 詞性標註和詞義消歧

第 3 部分 - 神經網絡範式
11 自動編碼器簡介
12 應用自動編碼器:CIFAR-10 圖像數據集
13 強化學習
14 卷積神經網絡
15 構建真實世界的卷積神經網絡:VGG-Face 和 VGG-Face Lite
16 循環神經網絡
17 長短期記憶網絡和自動語音識別
18 用於聊天機器人的序列到序列模型
19 效用風景

作者簡介

Chris Mattmann is the Deputy Chief Technology and Innovation Officer at NASA Jet Propulsion Lab, where he has been recognised as JPL's first Principal Scientist in the area of Data Science. Chris has applied TensorFlow to challenges he's faced at NASA, including building an implementation of Google's Show & Tell algorithm for image captioning using TensorFlow. He contributes to open source as a former Director at the Apache Software Foundation, and teaches graduate courses at USC in Content Detection and Analysis, and in Search Engines and Information Retrieval.

作者簡介(中文翻譯)

Chris Mattmann是NASA喷气推进实验室的副首席技术和创新官,他被认定为JPL在数据科学领域的首席科学家。Chris在NASA面临的挑战中应用了TensorFlow,包括使用TensorFlow构建了Google的Show & Tell算法的图像字幕实现。作为Apache软件基金会的前任董事,他为开源做出了贡献,并在南加州大学教授研究生课程,包括内容检测与分析以及搜索引擎和信息检索。