Deep Learning
Deep Learning
Build and train custom models with PyTorch
Deep Learning
Master deep learning fundamentals and build production-ready models with PyTorch.
Projects
Beginner
| Project | Description | Time |
|---|---|---|
| Custom Text Classifier | Build a text classifier from scratch | ~2 hours |
| Embedding Model | Create embeddings with PyTorch | ~2 hours |
| Inference API | Serve models with FastAPI | ~2 hours |
Intermediate
| Project | Description | Time |
|---|---|---|
| LoRA Fine-tuning | Efficient LLM fine-tuning with PEFT | ~6 hours |
| Custom Reranker | Train a cross-encoder for RAG | ~4 hours |
| Knowledge Distillation | Compress large models | ~6 hours |
| Quantization | Optimize models for inference | ~4 hours |
Advanced
| Project | Description | Time |
|---|---|---|
| DPO Alignment | Align LLMs with human preferences | ~4 days |
| Distributed Training | Multi-GPU training with DDP | ~3 days |
| Custom Transformer | Build transformer from scratch | ~5 days |
Case Studies
Real-world implementations showing deep learning in production.
| Case Study | Industry | Description | Status |
|---|---|---|---|
| Document Understanding Model | Enterprise | Custom document classification and extraction | Available |
Key Concepts
- Neural network fundamentals with nn.Module
- Training loops and optimization
- Efficient fine-tuning (LoRA, QLoRA)
- Model compression and quantization
- Distributed training strategies