Develop deep neural networks in Theano with practical code examples
for image classification, machine translation, reinforcement
agents, or generative models. About This Book * 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. Who This Book Is For
This book is indented to provide a full overview of deep learning.
From the beginner in deep learning and artificial intelligence, to
the data scientist who wants to become familiar with Theano and its
supporting libraries, or have an extended understanding of deep
neural nets. Some basic skills in Python programming and computer
science will help, as well as skills in elementary algebra and
calculus. 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 In Detail 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. Style and approach It is an
easy-to-follow example book that teaches you how to perform fast,
efficient computations in Python. Starting with the very
basics-NumPy, installing Theano, this book will take you to the
smooth journey of implementing Theano for advanced computations for
machine learning and deep learning.
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