This hands-on book (Link:Hands-On MachineLearning with Scikit-Learn and TensorFlow)
shows you how to:
- 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.
- Apply practical code examples without acquiring excessive machine learning theory or algorithm details
MAIN CONTENTS
¨
The Machine Learning Landscape
§ What Is
Machine Learning?
§ Why Use
Machine Learning?
§ Types of
Machine Learning Systems
§ Main
Challenges of Machine Learning
¨
End-to-End Machine Learning Project
§ Prepare
the Data for Machine Learning Algorithms
¨
Neural Networks and Deep Learning
¨
Up and Running with
TensorFlow
¨
Introduction to
Artificial Neural Networks
¨
Training Deep Neural
Nets
¨
Distributing TensorFlow
Across Devices and Servers
¨
Convolutional Neural
Networks
¨
Recurrent Neural
Networks
¨
Autoencoders
