
PyTorch Recipes: A Problem-Solution Approach
Pradeepta Mishra
Résumé
Moving on to algorithms; you will learn how PyTorch works with supervised and unsupervised algorithms. You will see how convolutional neural networks, deep neural networks, and recurrent neural networks work using PyTorch. In conclusion you will get acquainted with natural language processing and text processing using PyTorch.
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
Readers wanting to dive straight into programming PyTorch.
Chapter 1: Introduction PyTorch, Tensors, Tensor Operations and Basics.-
Chapter 2: Probability distributions using PyTorch.-
Chapter 3: Convolutional Neural Network and RNN using PyTorch.-
Chapter 4: Introduction to Neural Networks, Tensor Differentiation .-
Chapter 5: Supervised Learning using PyTorch.-
Chapter 6: Fine Tuning Deep Learning Algorithms using PyTorch.-
Chapter 7: NLP and Text Processing using PyTorch.-Caractéristiques techniques
PAPIER | |
Éditeur(s) | Apress |
Auteur(s) | Pradeepta Mishra |
Parution | 27/01/2019 |
Nb. de pages | 184 |
EAN13 | 9781484242575 |
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