Choose your path based on your goals and level of interest.
Not interested in understanding? Just want to use the tools?
Skip the theory:
If you want to understand what you're doing and go beyond using tools:
- Prerequisites - Build the necessary foundation.
- Getting Started in ML - Introductory concepts and applications.
- Neural Network Basics - Backpropagation, training, and core intuition.
- PyTorch - Hands-on experimentation.
For those with foundational knowledge, use the resources below to deepen your expertise:
- Papers collection - Curated foundational and topic-specific research
- Recommended books - Established references for deep learning and ML theory
- Advanced PyTorch – Performance tips, C++ extensions, custom backprop, dataloading
- Computer Vision (CV) - CNNs, object detection, Vision Transformers
- Natural Language Processing (NLP) - Classical NLP, Transformers, LLMs