Deep Learning with Python

Learn Best Practices of Deep Learning Models with PyTorch
 
 
Apress
  • erschienen am 15. Juni 2020
  • |
  • XIII, 271 Seiten
 
E-Book | PDF mit Wasserzeichen-DRM | Systemvoraussetzungen
978-1-4842-5364-9 (ISBN)
 
Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This new 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 and its 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.
1st ed.
  • Englisch
  • CA
  • |
  • USA
APRESS
77 s/w Abbildungen
  • 5,51 MB
978-1-4842-5364-9 (9781484253649)
10.1007/978-1-4842-5364-9
weitere Ausgaben werden ermittelt
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.

Deep Learning with Python 2E Learn Best Practices of Deep Learning Models with PyTorch
Chapter 1 Introduction to Deep Learning
Chapter 2 Machine Learning Fundamentals
Chapter 3 Feed Forward Neural Networks
Chapter 4 Convolution Neural Networks
Chapter 5 Recurrent Neural Networks
Chapter 6 PyTorch Basics: Linear Algebra
Chapter 7 PyTorch In Depth
Chapter 8 Optimization in Deep Learning
Chapter 9 Automatic Differentiation in Deep Learning
Chapter 10: Training Deep Learning Models
Chapter 11: Regularization Techniques



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