Python Deep Learning Cookbook
Deep Learning is a rapidly evolving field of Machine Learning science which gives machines the ability to learn from information. This book contains detailed recipes to tackle with the common and not so common problems while dealing with deep learning algorithms and models in Python. You will benefit from this book by finding technical solutions to the issues presented, along with a detailed explanation of the solutions, and a discussion on corresponding pros and cons of implementing the proposed solution using Theano, Tensorflow, MXNet, and Keras. You'll come across recipes on data pre-processing, network models and topologies, supervised and unsupervised learning presented in a “solution to problem” fashion.
Offered by
Difficulty Level
Intermediate
Completion Time
11h
Language
English
About Book
Who Is This Book For?
This book is intended for machine learning professionals who are looking to use deep learning algorithms to create real-world applications using Python. Thorough understanding of the machine learning concepts and Python libraries such as NumPy, SciPy and scikit-learn is expected. Additionally, basic knowledge in linear algebra and calculus is desired.
Python Deep Learning Cookbook
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 11h total length
Programming Environment, GPU Computing, and Cloud Solutions
Feedforward Networks
Convolutional Neural Networks (CNN)
Recurrent and Recursive Neural Networks
Reinforcement Learning
Generative Adversarial Networks
Computer Vision
Natural Language Processing
Speech Recognition and Video Analysis
Time Series and Structured Data
Game Playing Agents and Robotics
Hyperparameter Selection and Tuning
Networks Internals
Pretrained Models
Related Resources
Access Ready-to-Use Books for Free!
Get instant access to a library of pre-built books—free trial, no credit card required. Start training your team in minutes!