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Deep Learning with Python = Learn Be...
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Deep Learning with Python = Learn Best Practices of Deep Learning Models with PyTorch /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Deep Learning with Python/ by Nikhil Ketkar, Jojo Moolayil.
其他題名:
Learn Best Practices of Deep Learning Models with PyTorch /
作者:
Ketkar, Nikhil.
其他作者:
Moolayil, Jojo.
面頁冊數:
XVII, 306 p. 82 illus.online resource. :
Contained By:
Springer Nature eBook
標題:
Open Source. -
電子資源:
https://doi.org/10.1007/978-1-4842-5364-9
ISBN:
9781484253649
Deep Learning with Python = Learn Best Practices of Deep Learning Models with PyTorch /
Ketkar, Nikhil.
Deep Learning with Python
Learn Best Practices of Deep Learning Models with PyTorch /[electronic resource] :by Nikhil Ketkar, Jojo Moolayil. - 2nd ed. 2021. - XVII, 306 p. 82 illus.online resource.
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
Standard No.: 10.1007/978-1-4842-5364-9doiSubjects--Topical Terms:
1113081
Open Source.
LC Class. No.: QA76.73.P98
Dewey Class. No.: 005.133
Deep Learning with Python = Learn Best Practices of Deep Learning Models with PyTorch /
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