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Machine Learning with Tensorflow, Second Edition by Mattmann A Chris: Used
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eBay-Artikelnr.:365143409912
Artikelmerkmale
- Artikelzustand
- Book Title
- Machine Learning with Tensorflow, Second Edition
- Publication Date
- 2021-02-02
- Edition Number
- 2
- Pages
- 456
- ISBN
- 9781617297717
Über dieses Produkt
Product Identifiers
Publisher
Manning Publications Co. LLC
ISBN-10
1617297712
ISBN-13
9781617297717
eBay Product ID (ePID)
8050410090
Product Key Features
Number of Pages
456 Pages
Language
English
Publication Name
Machine Learning with Tensorflow, Second Edition
Subject
Neural Networks, General, Data Visualization
Publication Year
2021
Type
Textbook
Subject Area
Computers, Science
Format
Trade Paperback
Dimensions
Item Height
0.9 in
Item Weight
26.3 Oz
Item Length
9.2 in
Item Width
7.4 in
Additional Product Features
Edition Number
2
LCCN
2021-287272
Reviews
'A practical, no-nonsense, original approach to machine learning.' Alain Couniot, Sopra Steria Benelux 'An excellent book for readers who want to learn TensorFlow and machine learning.' Bhagvan Kommadi, ValueMomentum 'A great way to learn the ins and outs of TensorFlow, from the fundamentals to autoencoders, CNNs, and sequence-to-sequence models.' Ariel Gamiño, GLG 'Full of practical examples illustrating the concepts in a clear, progressive approach. This book is worth your while!' Alain Lompo, ISO-GRUPPE
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.31
Table Of Content
table of contents PART 1. YOUR MACHINE-LEARNING RIG READ IN LIVEBOOK 1A MACHINE-LEARNING ODYSSEY READ IN LIVEBOOK 2TENSORFLOW ESSENTIALS PART 2. CORE LEARNING ALGORITHMS READ IN LIVEBOOK 3LINEAR REGRESSION AND BEYOND READ IN LIVEBOOK 4USING REGRESSION FOR CALL-CENTER VOLUME PREDICTION READ IN LIVEBOOK 5A GENTLE INTRODUCTION TO CLASSIFICATION READ IN LIVEBOOK
Synopsis
This fully revised second edition of Machine Learning with TensorFlow teaches you the foundational concepts of machine learning and how to utilize the TensorFlow library to rapidly build powerful ML models. You'll learn the basics of regression, classification, and clustering algorithms, applying them to solve real-world challenges. 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. 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. Key Features - Visualizing algorithms with TensorBoard - Understanding and using neural networks - Reproducing and employing predictive science - Downloadable Jupyter Notebooks for all examples - Questions to test your knowledge - Examples use the super-stable 1.14.1 branch of TensorFlow Developers experienced with Python and algebraic concepts like vectors and matrices. About the technology TensorFlow, Google's library for large-scale machine learning, makes powerful ML techniques easily accessible. It simplifies often-complex computations by representing them as graphs that are mapped to machines in a cluster or to the processors of a single machine. Offering a complete ecosystem for all stages and types of machine learning, TensorFlow's end-to-end functionality empowers machine learning engineers of all skill levels to solve their problems with ML. 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. Nishant Shukla wrote the first edition of Machine Learning with TensorFlow., 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, This fully revised second edition of Machine Learning with TensorFlow teaches you the foundational concepts of machine learning and how to utilize the TensorFlow library to rapidly build powerful ML models. You'll learn the basics of regression, classification, and clustering algorithms, applying them to solve real-world challenges. 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. 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. Key Features · Visualizing algorithms with TensorBoard · Understanding and using neural networks · Reproducing and employing predictive science · Downloadable Jupyter Notebooks for all examples · Questions to test your knowledge · Examples use the super-stable 1.14.1 branch of TensorFlow Developers experienced with Python and algebraic concepts like vectors and matrices. About the technology TensorFlow, Google's library for large-scale machine learning, makes powerful ML techniques easily accessible. It simplifies often-complex computations by representing them as graphs that are mapped to machines in a cluster or to the processors of a single machine. Offering a complete ecosystem for all stages and types of machine learning, TensorFlow's end-to-end functionality empowers machine learning engineers of all skill levels to solve their problems with ML.
LC Classification Number
Q325.5
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