Deep Learning
Deep Learning
Build and train custom models with PyTorch
Master deep learning fundamentals and build production-ready models with PyTorch. This category covers everything from building neural networks from scratch to training, compressing, aligning, and distributing models at scale.
| Reason | Explanation |
|---|
| Foundation of Modern AI | Every LLM, embedding model, and vision system is built on deep learning primitives |
| Control and Customization | Pre-trained APIs have limits; custom models let you own the architecture and data |
| Career Impact | Deep understanding of training loops, loss functions, and optimization separates ML engineers from API users |
| Cost Optimization | Techniques like distillation, quantization, and LoRA can cut inference costs by 10x |
Real-world implementations showing deep learning in production.
- Neural network fundamentals with nn.Module
- Training loops and optimization
- Efficient fine-tuning (LoRA, QLoRA)
- Model compression and quantization
- Distributed training strategies