
Deep Learning with Python: Learn Best Practices of Deep Learning Models with PyTorch, 2nd Edition
- Length: 323 pages
- Edition: 2
- Language: English
- Publisher: Apress
- Publication Date: 2021-05-05
- ISBN-10: 1484253639
- ISBN-13: 9781484253632
- Sales Rank: #3758382 (See Top 100 Books)
go to site 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.
https://semichaschaver.com/2025/04/03/a7w35c8 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.
Buy Soma Watson Brand 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 https://colvetmiranda.org/9avm01bsn Deep Learning with Python explains the best practices in taking these models to production with PyTorch.
Tramadol Order Online Cod https://kirkmanandjourdain.com/anlkd1csjvn 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 and its building blocks
- Work with tuning and optimizing models
Cheap Tramadol http://jannaorganic.co.uk/blog/2025/04/03/xqwyr9elq Who This Book Is For
https://www.masiesdelpenedes.com/926jyty Beginners with a working knowledge of Python who want to understand Deep Learning in a practical, hands-on manner.
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