Hands On Machine Learning with Scikit Learn Keras and TensorFlow

Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.

Hands On Machine Learning with Scikit Learn  Keras  and TensorFlow

Hands On Machine Learning with Scikit Learn Keras and TensorFlow

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets

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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Language: en
Pages: 856
Authors: Aurélien Géron
Categories: Computers
Type: BOOK - Published: 2019-09-05 - Publisher: "O'Reilly Media, Inc."

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples,
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition
Language: en
Pages: 848
Authors: Aurélien Géron
Categories: Computers
Type: BOOK - Published: 2019 - Publisher:

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples,
Neuronale Netze Selbst Programmieren
Language: de
Pages: 232
Authors: Tariq Rashid
Categories: Computers
Type: BOOK - Published: 2017 - Publisher:

Neuronale Netze sind Schlüsselelemente des Deep Learning und der Künstlichen Intelligenz, die heute zu Erstaunlichem in der Lage sind. Dennoch verstehen nur wenige, wie Neuronale Netze tatsächlich funktionieren. Dieses Buch nimmt Sie mit auf eine unterhaltsame Reise, die mit ganz einfachen Ideen beginnt und Ihnen Schritt für Schritt zeigt, wie
Hands-On Machine Learning with Scikit-Learn and TensorFlow
Language: en
Pages: 566
Authors: Aurélien Géron
Categories: Computers
Type: BOOK - Published: 2017-04 - Publisher: O'Reilly Media

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples,
Applied Deep Learning with Python
Language: en
Pages: 334
Authors: Alex Galea, Luis Capelo
Categories: Computers
Type: BOOK - Published: 2018-08-31 - Publisher: Packt Publishing Ltd

A hands-on guide to deep learning that’s filled with intuitive explanations and engaging practical examples Key Features Designed to iteratively develop the skills of Python users who don’t have a data science background Covers the key foundational concepts you’ll need to know when building deep learning systems Full of step-by-step

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