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Chris A Mattmann

Machine Learning with TensorFlow, Second Edition

Edition 2

Machine Learning with TensorFlow, Second Edition

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Best.Nr.
978-1-6172-9771-7
Erscheint im
Februar 2021
Sprache
Englisch
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€ 46,19
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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.



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.

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