PyTorch Recipes: A Problem-Solution Approach

PyTorch Recipes: A Problem-Solution Approach
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Book Description
Get up to speed with the deep learning concepts of Pytorch using a problem-solution approach. Starting with an introduction to PyTorch, you'll get familiarized with tensors, a type of data structure used to calculate arithmetic operations and also learn how they operate. You will then take a look at probability distributions using PyTorch and get acquainted with its concepts. Further you will dive into transformations and graph computations with PyTorch. Along the way you will take a look at common issues faced with neural network implementation and tensor differentiation, and get the best solutions for them.

Content

Chapter 1:​ Introduction to PyTorch, Tensors, and Tensor Operations
Chapter 2:​ Probability Distributions Using PyTorch
Chapter 3:​ CNN and RNN Using PyTorch
Chapter 4:​ Introduction to Neural Networks Using PyTorch
Chapter 5:​ Supervised Learning Using PyTorch
Chapter 6:​ Fine-Tuning Deep Learning Models Using PyTorch
Chapter 7:​ Natural Language Processing Using PyTorch

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