Deep learning and scientific computing with R torch. (2023)

• Record Type: Book
• Further Information: Note: Sigrid Keydana.
• Authors: Keydana, Sigrid
• Contents: Part 1. Getting familiar with torch 1. Overview 2. On torch, and how to get it 3. Tensors 4. Autograd 5. Function minimization with autograd 6. A neural network from scratch 7. Modules 8. Optimizers 9. Loss functions 10. Function minimization with L-BFGS 11. Modularizing the neural network Part 2. Deep learning with torch 12. Overview 13. Loading data 14. Training with luz 15. A first go at image classification 16. Making models generalize 17. Speeding up training 18. Image classification, take two: Improving performance 19. Image segmentation 20. Tabular data 21. Time series 22. Audio classification Part 3. Other things to do with torch: Matrices, Fourier Transform, and Wavelets 23. Overview 24. Matrix computations: Least-squares problems 25. Matrix computations: Convolution 26. Exploring the Discrete Fourier Transform (DFT) 27. The Fast Fourier Transform (FFT) 28. Wavelets
• Edition: 1st
• Publisher Details: Boca Raton : Chapman & Hall/CRC
• Publication Date: 2023
• Extent: 1 online resource (404 pages), illustrations (black and white)
• Subjects: 006.31; Deep learning (Machine learning); Science -- Mathematics -- Data processing; R (Computer program language); Python (Computer program language)
• Languages: English
• ISBNs: 9781000863017; 9781000862935
• Related ISBNs: 9781032231389; 9781032231396
• Notes: Note: Description based on CIP data; resource not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.