Applied Deep Learning
Master deep neural networks from mathematical foundations to production Transformers. Implement backprop from scratch, train deep CNNs, and build modern self-attention mechanisms with PyTorch.
Course Overview
Why This Course Matters
Modern Artificial Intelligence is driven by deep learning. Yet most tutorials teach superficial syntax without imparting the architectural intuition needed to train models that actually converge in real-world environments.
In this comprehensive, code-centric course, you will bridge the gap between academic research papers and production-ready PyTorch implementations.
What You Will Build:
- A custom autograd engine simulating PyTorch’s reverse-mode differentiation.
- High-accuracy convolutional classifiers with residual connections and mixed precision.
- A functional multi-head self-attention module powering modern Generative AI.
Key Competencies You'll Build
Course Curriculum
4 chapters • 9 total lessons & checkpoints
Learning Path & Prerequisite Graph
How this course connects to your end-to-end engineering career.
Python for AI & Memory
PrepObject references, slicing, vectorization, NumPy broadcasting.
Review moduleLinear Algebra & Calculus
PrepMatrix dot products, partial derivatives, chain rule fundamentals.
Review moduleDeep Foundations & Perceptrons
In ProgressDeriving backprop from scratch and gradient descent mechanics.
CNNs & Residual Networks
In ProgressSpatial feature hierarchies, convolutions, and transfer learning.
Transformers & Self-Attention
In ProgressScaled dot-product attention, multi-head projections, and decoding blocks.
Production RAG & LLMs
Next UpVector databases, dense embeddings, hybrid search, and LangChain/LlamaIndex agents.
Explore specializationAutonomous Multi-Agent Systems
Next UpTool use, self-reflection loops, and distributed model serving.
Explore specialization