Delve into neural networks, implement deep learning algorithms, and
explore layers of data abstraction with the help of this
comprehensive TensorFlow guide About This Book * Learn how to
implement advanced techniques in deep learning with Google's
brainchild, TensorFlow * Explore deep neural networks and layers of
data abstraction with the help of this comprehensive guide *
Real-world contextualization through some deep learning problems
concerning research and application Who This Book Is For The book
is intended for a general audience of people interested in machine
learning and machine intelligence. A rudimentary level of
programming in one language is assumed, as is a basic familiarity
with computer science techniques and technologies, including a
basic awareness of computer hardware and algorithms. Some
competence in mathematics is needed to the level of elementary
linear algebra and calculus. What You Will Learn * Learn about
machine learning landscapes along with the historical development
and progress of deep learning * Learn about deep machine
intelligence and GPU computing with the latest TensorFlow 1.x *
Access public datasets and utilize them using TensorFlow to load,
process, and transform data * Use TensorFlow on real-world
datasets, including images, text, and more * Learn how to evaluate
the performance of your deep learning models * Using deep learning
for scalable object detection and mobile computing * Train machines
quickly to learn from data by exploring reinforcement learning
techniques * Explore active areas of deep learning research and
applications In Detail Deep learning is the step that comes after
machine learning, and has more advanced implementations. Machine
learning is not just for academics anymore, but is becoming a
mainstream practice through wide adoption, and deep learning has
taken the front seat. As a data scientist, if you want to explore
data abstraction layers, this book will be your guide. This book
shows how this can be exploited in the real world with complex raw
data using TensorFlow 1.x. Throughout the book, you'll learn how to
implement deep learning algorithms for machine learning systems and
integrate them into your product offerings, including search, image
recognition, and language processing. Additionally, you'll learn
how to analyze and improve the performance of deep learning models.
This can be done by comparing algorithms against benchmarks, along
with machine intelligence, to learn from the information and
determine ideal behaviors within a specific context. After
finishing the book, you will be familiar with machine learning
techniques, in particular the use of TensorFlow for deep learning,
and will be ready to apply your knowledge to research or commercial
projects. Style and approach This step-by-step guide will explore
common, and not so common, deep neural networks and show how these
can be exploited in the real world with complex raw data. With the
help of practical examples, you will learn how to implement
different types of neural nets to build smart applications related
to text, speech, and image data processing.
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