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PyTorch recipes : a problem-solution approach /
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 lo...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[California] :
Apress,
[2019]
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Mishra, Pradeepta, |e author. | |
| 245 | 1 | 0 | |a PyTorch recipes : |b a problem-solution approach / |c Pradeepta Mishra. |
| 264 | 1 | |a [California] : |b Apress, |c [2019] | |
| 264 | 4 | |c ©2019 | |
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| 588 | |a Online resource; title from PDF title page (EBSCO, viewed January 31, 2019). | ||
| 500 | |a Includes index. | ||
| 505 | 0 | |a Introduction to PyTorch, Tensors, and Tensor operations -- Probability distributions using PyTorch -- CNN and RNN using PyTorch -- Introduction to neural networks using PyTorch -- Supervised learning using PyTorch -- Fine-tuning deep learning models using PyTorch -- Natural language processing using PyTorch. | |
| 520 | |a 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. 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. You will: 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. | ||
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| 650 | 6 | |a Réseaux neuronaux (Informatique) | |
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