Recommended study Materials
Textbooks / Books:
- “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville — for deep learning fundamentals.
- “PyTorch Deep Learning Hands-On” by Sherin Thomas and Sudhanshu Passi — practical PyTorch implementation and GPU usage.
- “Programming Massively Parallel Processors: A Hands-on Approach” by David B. Kirk and Wen-mei W. Hwu — for GPU architecture and parallel programming concepts.
- “Distributed Machine Learning Patterns” by Yuan Tang — for distributed/deep learning parallelism strategies.
Online Resources / Tutorials:
- PyTorch Official Tutorials: https://pytorch.org/tutorials/ — for practical exercises and examples.
- NVIDIA Deep Learning GPU Training Guide: https://developer.nvidia.com/deep-learning — for GPU optimization techniques.
- Stanford CS231n: Convolutional Neural Networks for Visual Recognition: http://cs231n.stanford.edu/ — covers neural network