Welcome to Applied Deep Learning

Welcome to Applied Deep Learning: From Neural Foundations to Production Transformers.

In this course, we demystify modern deep learning by building architectures from first mathematical principles before scaling them with PyTorch and CUDA acceleration.

What You Will Achieve

1. First-Principles Mastery: Code backpropagation and multi-layer perceptrons with pure Python and NumPy.
2. Computer Vision & CNNs: Train and fine-tune convolutional architectures with residual connections (ResNet) on custom vision datasets.
3. Sequence Modeling & Attention: Implement Scaled Dot-Product Attention and decode the inner mechanisms of modern Transformers.
4. Production Deployment: Export models with TorchScript / ONNX and serve inference through high-performance async endpoints.

Development Environment

We strongly recommend setting up a local virtual environment with CUDA support, or utilizing Google Colab / Paperspace Gradient for free GPU tier access:

# Recommended environment setup
python3 -m venv dl-env
source dl-env/bin/activate
pip install torch torchvision torchaudio numpy matplotlib jupyter