Book

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 byPackt Logo

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.

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

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