Take your machine learning skills to the next level by mastering Deep Learning concepts and algorithms using Python.
Key Features:
- Explore and create intelligent systems using cutting-edge deep learning techniques
- Implement deep learning algorithms and work with revolutionary libraries in Python
- Get real-world examples and easy-to-follow tutorials on Theano, TensorFlow, H2O and more
Book Description:
With an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used broadly across different industries.
The book will give you all the practical information available on the subject, including the best practices, using real-world use cases. You will learn to recognize and extract information to increase predictive accuracy and optimize results.
Starting with a quick recap of important machine learning concepts, the book will delve straight into deep learning principles using Sci-kit learn. Moving ahead, you will learn to use the latest open source libraries such as Theano, Keras, Google's TensorFlow, and H20. Use this guide to uncover the difficulties of pattern recognition, scaling data with greater accuracy and discussing deep learning algorithms and techniques.
Whether you want to dive deeper into Deep Learning, or want to investigate how to get more out of this powerful technology, you'll find everything inside.
What You Will Learn:
- Get a practical deep dive into deep learning algorithms
- Explore deep learning further with Theano, Caffe, Keras, and TensorFlow
- Learn about two of the most powerful techniques at the core of many practical deep learning implementations: Auto-Encoders and Restricted Boltzmann Machines
- Dive into Deep Belief Nets and Deep Neural Networks
- Discover more deep learning algorithms with Dropout and Convolutional Neural Networks
- Get to know device strategies so you can use deep learning algorithms and libraries in the real world
Who this book is for:
This book is for Data Science practitioners as well as aspirants who have a basic foundational understanding of Machine Learning concepts and some programming experience with Python. A mathematical background with a conceptual understanding of calculus and statistics is also desired.
