PyTorch Recipes: A Problem-Solution Approach
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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.
What You Will Learn
- Master tensor operations for dynamic graph-based calculations using PyTorch
- Create PyTorch transformations and graph computations for neural networks
- Carry out supervised and unsupervised learning using PyTorch
- Work with deep learning algorithms such as CNN and RNN
- Build LSTM models in PyTorch
- Use PyTorch for text processing
Who This Book Is For
Readers wanting to dive straight into programming PyTorch.