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Deep Learning with Python: Learn Best Practices of Deep Learning Models with Pytorch

Name: Deep Learning with Python: Learn Best Practices of Deep Learning Models with Pytorch
Brand: Apress
SKU: 9781484253632
Price: 27.99 USD
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Contributors:

Nikhil Ketkar, Jojo Moolayil

Publisher:

Apress

Binding:

Paperback

Book Category:

Computers, Mathematics

Book Subcategory:

Computer Science, Programming, Logic

Book Topic:

Object Oriented

Edition Description:

2nd Edition

Shipping & Return +

The product is eligible for a complimentary return within 30 days from the date of purchase. Furthermore, individual items are shipped within 24 hours of receiving the order.

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Specifications +

Release Date:

2021-04-10

ISBN 13:

9781484253632

Weight:

1.01 lbs

Size:

9.21 x 6.14 x 0.68 inches

Pages:

306

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product description

Description

Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how with PyTorch, a platform developed by Facebook's Artificial Intelligence Research Group.
You'll start with a perspective on how and why deep learning with PyTorch has emerged as an path-breaking framework with a set of tools and techniques to solve real-world problems. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization. Having established this foundation, you'll move on to key components and functionality of PyTorch including layers, loss functions and optimization algorithms.
You'll also gain an understanding of Graphical Processing Unit (GPU) based computation, which is essential for training deep learning models. All the key architectures in deep learning are covered, including feedforward networks, convolution neural networks, recurrent neural networks, long short-term memory networks, autoencoders and generative adversarial networks. Backed by a number of tricks of the trade for training and optimizing deep learning models, this edition of Deep Learning with Python explains the best practices in taking these models to production with PyTorch.
What You'll Learn

Review machine learning fundamentals such as overfitting, underfitting, and regularization.
Understand deep learning fundamentals such as feed-forward networks, convolution neural networks, recurrent neural networks, automatic differentiation, and stochastic gradient descent.
Apply in-depth linear algebra with PyTorch
Explore PyTorch fundamentals andits building blocks
Work with tuning and optimizing models

Who This Book Is For
Beginners with a working knowledge of Python who want to understand Deep Learning in a practical, hands-on manner.

About the Author
Nikhil S. Ketkar currently leads the Machine Learning Platform team at Flipkart, India's largest e-commerce company. He received his Ph.D. from Washington State University. Following that he conducted postdoctoral research at University of North Carolina at Charlotte, which was followed by a brief stint in high frequency trading at Transmaket in Chicago. More recently he led the data mining team in Guavus, a startup doing big data analytics in the telecom domain and Indix, a startup doing data science in the e-commerce domain. His research interests include machine learning and graph theory.
Jojo Moolayil is an artificial intelligence, deep learning, machine learning, and decision science professional with over five years of industrial experience and is a published author of the book Smarter Decisions - The Intersection of IoT and Decision Science. He has worked with several industry leaders on high-impact and critical data science and machine learning projects across multiple verticals. He is currently associated with Amazon Web Services as a research scientist. He was born and raised in Pune, India and graduated from the University of Pune with a major in Information Technology Engineering. He started his career with Mu Sigma Inc., the world's largest pure-play analytics provider and worked with the leaders of many Fortune 50 clients. He later worked with Flutura - an IoT analytics startup and GE. He currently resides in Vancouver, BC. Apart from writing books on decision science and IoT, Jojo has also been a technical reviewer for various books on machine learning, deep learning and business analytics with Apress and Packt publications. He is an active data science tutor and maintains a blog at http: //blog.jojomoolayil.com.

Deep Learning with Python: Learn Best Practices of Deep Learning Models with Pytorch

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