Hands-on Machine Learning With Scikit-learn And Tensorflow: Concepts, Tools, And Techniques To Build Intelligent Systems
by Aurélien Géron /
2017 / English / PDF
51.6 MB Download
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.
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.
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
Explore the machine learning landscape, particularly neural
netsUse scikit-learn to track an example machine-learning
project end-to-end
Use scikit-learn to track an example machine-learning
project end-to-endExplore several training models, including support vector
machines, decision trees, random forests, and ensemble methods
Explore several training models, including support vector
machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural nets
Use the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional
nets, recurrent nets, and deep reinforcement learning
Dive into neural net architectures, including convolutional
nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural nets
Learn techniques for training and scaling deep neural netsApply practical code examples without acquiring excessive
machine learning theory or algorithm details
Apply practical code examples without acquiring excessive
machine learning theory or algorithm details