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Inhaltsangabe
DESCRIPTION
Being able to make near-real-time decisions is becoming increasingly
crucial. To succeed, we need machine learning systems that can turn
massive amounts of data into valuable insights. But when you're just
starting out in the data science field, how do you get started creating
machine learning applications? The answer is TensorFlow, a new open
source machine learning library from Google. The TensorFlow library
can take your high level designs and turn them into the low level
mathematical operations required by machine learning algorithms.
Machine Learning with TensorFlow teaches readers about machinelearning algorithms and how to implement solutions with TensorFlow.
It starts with an overview of machine learning concepts and moves on
to the essentials needed to begin using TensorFlow. Each chapter
zooms into a prominent example of machine learning. Readers can
cover them all to master the basics or skip around to cater to their
needs. By the end of this book, readers will be able to solve
classification, clustering, regression, and prediction problems in the
real world.
KEY FEATURES• Lots of diagrams, code examples, and exercises
• Solves real-world problems with TensorFlow
• Uses well-studied neural network architectures
• Presents code that can be used for the readers’ own applications
AUDIENCE
This book is for programmers who have some experience with Python and
linear algebra concepts like vectors and matrices. No experience with
machine learning is necessary.
ABOUT THE TECHNOLOGY
Google open-sourced their machine learning framework called TensorFlow
in late 2015 under the Apache 2.0 license. Before that, it was used
proprietarily by Google in its speech recognition, Search, Photos, and
Gmail, among other applications. TensorFlow is one the most popular
machine learning libraries.
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Über die Autorin bzw. den Autor
AUTHOR BIO
Nishant Shukla is a computer vision researcher at UCLA, focusing onmachine learning techniques with robotics. He has been a developer for
Microsoft, Facebook, and Foursquare, and a machine learning engineer for
SpaceX, as well as the author of the Haskell Data Analysis Cookbook.„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
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Machine Learning with TensorFlow
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Taschenbuch. Zustand: Neu. Neuware - SummaryMachine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. Purchase of the print book includes a free Elektronisches Buch in PDF, Kindle, and ePub formats from Manning Publications.About the TechnologyTensorFlow, Google's library for large-scale machine learning, simplifies often-complex computations by representing them as graphs and efficiently mapping parts of the graphs to machines in a cluster or to the processors of a single machine.About the BookMachine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. You'll learn the basics by working with classic prediction, classification, and clustering algorithms. Then, you'll move on to the money chapters: exploration of deep-learning concepts like autoencoders, recurrent neural networks, and reinforcement learning. Digest this book and you will be ready to use TensorFlow for machine-learning and deep-learning applications of your own. What's InsideMatching your tasks to the right machine-learning and deep-learning approachesVisualizing algorithms with TensorBoardUnderstanding and using neural networksAbout the ReaderWritten for developers experienced with Python and algebraic concepts like vectors and matrices.About the AuthorAuthor Nishant Shukla is a computer vision researcher focused on applying machine-learning techniques in robotics. Senior technical editor, Kenneth Fricklas, is a seasoned developer, author, and machine-learning practitioner.Table of ContentsPART 1 - YOUR MACHINE-LEARNING RIGA machine-learning odysseyTensorFlow essentialsPART 2 - CORE LEARNING ALGORITHMSLinear regression and beyondA gentle introduction to classificationAutomatically clustering dataHidden Markov models PART 3 - THE NEURAL NETWORK PARADIGMA peek into autoencodersReinforcement learningConvolutional neural networksRecurrent neural networksSequence-to-sequence models for chatbotsUtility landscape. Bestandsnummer des Verkäufers 9781617293870
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