Currently prioritizing deep learning architecture design, optimization, and ML systems. Planning to also cover RL in the near future.

There are 4 core pipelines here.

Theme: Math-driven NN design, PyTorch fluency, and optimization intuition.

Project repo

Project repo

• Task: Implement a fully connected network from scratch in PyTorch (no nn.Module).

• Focus:

  • Raw tensor operations + autograd.
  • Derive backpropagation for a 2-layer network on paper.
  • Compare performance with/without batch normalization.

• Math Layer: Derive gradients for cross-entropy loss.

• Task: Train the same model (e.g., ResNet-18 on CIFAR-10) with SGD, Adam, RMSProp, and a custom optimizer.

• Focus

  • Implement optimizers from scratch (override PyTorch’s torch.optim).
  • Visualize loss landscapes and convergence speed.

• Math Layer: Derive update rules for Adam (adaptive moments).

• Task: Modify a CNN (e.g., VGG) by:

  • Adding/removing skip connections (ResNet-style).
  • Swapping activation functions (Swish vs. ReLU).
  • Pruning 20% of weights and fine-tuning.

• Goal: Build intuition for how design choices affect performance.

Theme: Implement influential papers and experiment with variations.

• Task: Code a transformer for sequence-to-sequence tasks (e.g., time-series prediction).

• Focus:

  • Implement multi-head attention, positional encoding.
  • Compare with an LSTM baseline.

• Paper Read: Attention Is All You Need.

• Task: Combine CNNs and transformers (e.g., Vision Transformer) for image classification.

• Stretch Goal: Use PyTorch’s JIT to trace/script the model.

• Task: Pick a recent arXiv paper (e.g., MLP-Mixer) and replicate its results.

• Focus: Debug discrepancies between paper and your implementation.

Theme: Deploy, optimize, and monitor models in real-world scenarios.

• Task: Take a large model (e.g., BERT) and:
  • Prune 50% of its weights.
  • Quantize it to INT8 with PyTorch’s quantization tools.
  • Benchmark latency/accuracy on CPU vs. GPU.

• Tools: FastAPI, Docker, ONNX Runtime

• Steps:

  • Export a PyTorch model to ONNX.
  • Build a server with request batching and logging.
  • Stress-test with locust (simulate 1000+ RPS).

• Task: Track model drift in production:

  • Log predictions/confidence scores.
  • Detect data drift using statistical tests (KS test).

• Tools: Prometheus, Grafana, Python

Theme: Combine everything into a single deployable product.

• Task: Build a recommendation system with:

  • A custom neural architecture (e.g., graph neural nets for social data).
  • Distributed training (PyTorch + DDP).
  • A/B testing framework (compare with a matrix factorization baseline).
  • Deployment via TensorRT for low-latency inference.

• Stretch Goal: Optimize with C++ (e.g., write critical inference code in LibTorch).