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Deep learning with Pythonlearn best ...

Ketkar, Nikhil.

Deep learning with Pythonlearn best practices of deep learning models with PyTorch /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning with Pythonby Nikhil Ketkar, Jojo Moolayil.
Reminder of title:	learn best practices of deep learning models with PyTorch /
Author:	Ketkar, Nikhil.
other author:	Moolayil, Jojo.
Published:	Berkeley, CA :Apress :2021.
Description:	xvii, 306 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-1-4842-5364-9
ISBN:	9781484253649$q(electronic bk.)

Deep learning with Pythonlearn best practices of deep learning models with PyTorch /
Ketkar, Nikhil.

Deep learning with Pythonlearn best practices of deep learning models with PyTorch /[electronic resource] :by Nikhil Ketkar, Jojo Moolayil. - Second edition. - Berkeley, CA :Apress :2021. - xvii, 306 p. :ill., digital ;24 cm.

Chapter 1 - Introduction Deep Learning -- Chapter 2 - Introduction to PyTorch -- Chapter 3- Feed Forward Networks -- Chapter 4 - Automatic Differentiation in Deep Learning -- Chapter 5 - Training Deep Neural Networks -- Chapter 6 - Convolutional Neural Networks -- Chapter 7 - Recurrent Neural Networks -- Chapter 8 - Recent advances in Deep Learning.

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. You will: 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 and its building blocks Work with tuning and optimizing models.

ISBN: 9781484253649$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-5364-9doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: QA76.73.P98 / K485 2021

Dewey Class. No.: 005.133

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