Machine learning with tensorflow

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Machine 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-le…

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Title Statements

Full title :Machine learning with tensorflow / Nishant Shukla ; Kenneth Fricklas, Senior Technical Editor.

Summary

Edition Statements

Publication Date:©2018

Publication Statements

Publisher(s):Manning
Place of Publication:Shelter Island, NY
Language:eng
Publication Date:©2018

The Physical Object

Description:xx, 251 pages : : illustrations ; : 25 cm

Edition Identifiers

ISBN:9781617293870, 1617293873
oclc:ocn954427264
LC Classification:Q325.5
Dewey Decimal:006.3/1

Machine generated contents note: pt. 1 YOUR MACHINE-LEARNING RIG -- 1.A machine-learning odyssey -- 1.1. Machine-learning fundamentals -- Parameters -- Learning and inference -- 1.2. Data representation and features -- 1.3. Distance metrics -- 1.4. Types of learning -- Supervised learning -- Unsupervised learning -- Reinforcement learning -- 1.5. TensorFlow -- 1.6. Overview of future chapters -- 1.7. Summary -- 2. TensorFlow essentials -- 2.1. Ensuring that TensorFlow works -- 2.2. Representing tensors -- 2.3. Creating operators -- 2.4. Executing operators with sessions -- Understanding code as a graph -- Setting session configurations -- 2.5. Writing code in Jupyter -- 2.6. Using variables -- 2.7. Saving and loading variables -- 2.8. Visualizing data using TensorBoard -- Implementing a moving average -- Visualizing the moving average -- 2.9. Summary -- pt. 2 CORE LEARNING ALGORITHMS -- 3. Linear regression and beyond -- 3.1. Formal notation

Note continued: How do you know the regression algorithm is working'? -- 3.2. Linear regression -- 3.3. Polynomial model -- 3.4. Regularization -- 3.5. Application of linear regression -- 3.6. Summary -- 4.A gentle introduction to classificat…

Subjects

Apprentissage automatique artificial intelligence COMPUTERS -- Computer Science COMPUTERS -- Information Theory COMPUTERS -- Neural Networks COMPUTERS -- Programming COMPUTERS -- Programming Languages COMPUTERS -- Software Development & Eng… Intelligence artificielle Machine learning Nonfiction TensorFlow (electronic resource)