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Deep Learning with Theano

Deep Learning with Theano: Perform large-scale numerical and scientific computations efficiently

By Christopher Bourez
$39.99 $27.98
Book Jul 2017 300 pages 1st Edition
eBook
$39.99 $27.98
Print
$48.99
Subscription
$15.99 Monthly
eBook
$39.99 $27.98
Print
$48.99
Subscription
$15.99 Monthly

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Product Details


Publication date : Jul 31, 2017
Length 300 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781786465825
Vendor :
Google
Category :
Concepts :
toc View table of contents toc Preview Book toc Download Code

Key benefits

  • Learn Theano basics and evaluate your mathematical expressions faster and in an efficient manner
  • Learn the design patterns of deep neural architectures to build efficient and powerful networks on your datasets
  • Apply your knowledge to concrete fields such as image classification, object detection, chatbots, machine translation, reinforcement agents, or generative models.

Description

This book offers a complete overview of Deep Learning with Theano, a Python-based library that makes optimizing numerical expressions and deep learning models easy on CPU or GPU. The book provides some practical code examples that help the beginner understand how easy it is to build complex neural networks, while more experimented data scientists will appreciate the reach of the book, addressing supervised and unsupervised learning, generative models, reinforcement learning in the fields of image recognition, natural language processing, or game strategy. The book also discusses image recognition tasks that range from simple digit recognition, image classification, object localization, image segmentation, to image captioning. Natural language processing examples include text generation, chatbots, machine translation, and question answering. The last example deals with generating random data that looks real and solving games such as in the Open-AI gym. At the end, this book sums up the best -performing nets for each task. While early research results were based on deep stacks of neural layers, in particular, convolutional layers, the book presents the principles that improved the efficiency of these architectures, in order to help the reader build new custom nets.

What you will learn

[*]Get familiar with Theano and deep learning [*]Provide examples in supervised, unsupervised, generative, or reinforcement learning. [*]Discover the main principles for designing efficient deep learning nets: convolutions, residual connections, and recurrent connections. [*]Use Theano on real-world computer vision datasets, such as for digit classification and image classification. [*]Extend the use of Theano to natural language processing tasks, for chatbots or machine translation [*]Cover artificial intelligence-driven strategies to enable a robot to solve games or learn from an environment [*]Generate synthetic data that looks real with generative modeling [*]Become familiar with Lasagne and Keras, two frameworks built on top of Theano

What do you get with eBook?

Feature icon Instant access to your Digital eBook purchase
Feature icon Download this book in EPUB and PDF formats
Feature icon Access this title in our online reader with advanced features
Feature icon DRM FREE - Read whenever, wherever and however you want
Buy Now

Product Details


Publication date : Jul 31, 2017
Length 300 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781786465825
Vendor :
Google
Category :
Concepts :

Table of Contents

22 Chapters
Deep Learning with Theano Packt Packt
Credits Packt Packt
About the Author Packt Packt
Acknowledgments Packt Packt
About the Reviewers Packt Packt
www.PacktPub.com Packt Packt
Customer Feedback Packt Packt
Preface Packt Packt
Theano Basics Packt Packt
Classifying Handwritten Digits with a Feedforward Network Packt Packt
Encoding Word into Vector Packt Packt
Generating Text with a Recurrent Neural Net Packt Packt
Analyzing Sentiment with a Bidirectional LSTM Packt Packt
Locating with Spatial Transformer Networks Packt Packt
Classifying Images with Residual Networks Packt Packt
Translating and Explaining with Encoding – decoding Networks Packt Packt
Selecting Relevant Inputs or Memories with the Mechanism of Attention Packt Packt
Predicting Times Sequences with Advanced RNN Packt Packt
Learning from the Environment with Reinforcement Packt Packt
Learning Features with Unsupervised Generative Networks Packt Packt
Extending Deep Learning with Theano Packt Packt
Index Packt Packt

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