USAAIO Foundations: Deep Learning, Transformers & PyTorch

🧠 Course Overview:

Welcome to USAAIO Foundations: Deep Learning, Transformers & PyTorch!
This course offers a comprehensive introduction to the core techniques driving today’s AI revolution. Centered on PyTorch, you will learn foundational concepts in deep learning, from basic neural networks to cutting-edge Transformer models, and explore practical applications in natural language processing, computer vision, and generative AI. Whether you’re a student or aspiring AI developer, this course will equip you with the theory, hands-on coding skills, and confidence to build modern AI systems.

🧩 What You Will Learn:

PyTorch Basics: Tensors, operations, gradients, and building custom layers
Multi-Layer Perceptrons (MLP): Activation functions, forward and backward propagation, and full model implementation
Advanced Neural Network Layers: BatchNorm, Dropout, Softmax, Cross-Entropy loss, and their manual and PyTorch implementations
Transformers: Attention mechanism (Q, K, V), positional encoding, multi-head attention, and Transformer encoder layers, with coding practice
Applications of Transformers: NLP tokenization and embeddings, Vision Transformers (ViT), and optional Graph Neural Networks (GNNs)
NLP Techniques: Pre-training (masked language models), fine-tuning, and Hugging Face basics
Computer Vision & Generative AI: CNN architectures, object detection, UNet segmentation, autoencoders, GANs, and diffusion models (e.g., Stable Diffusion)

💻 Tools & Technologies:

Programming Language: Python
Deep Learning Framework: PyTorch
Libraries: Hugging Face Transformers, NumPy, Pandas
Projects & Practice: Custom layer implementations, Transformer coding from scratch, NLP and vision applications

🎓 Course Features:

Hands-on Exercises: Implement neural network layers, Transformer components, and generative models with guided coding projects
Theory + Practice: Manual derivations paired with PyTorch implementations for deep understanding
Flexible Learning: Recorded lessons, downloadable code, and datasets
Live Support: Instructor Q&A and discussion sessions

🎯 Ideal For:

Students and professionals with basic Python skills eager to learn deep learning
AI enthusiasts wanting to master PyTorch and modern architectures like Transformers
Future AI engineers, data scientists, and researchers interested in NLP, vision, and generative AI

⏳ Duration:

10+ Weeks

Weekly sessions (~1–2 hours each)
Self-paced exercises and projects

📘 Prerequisites:

Basic Python programming experience
Familiarity with linear algebra and basic statistics recommended

📦 Materials Included:

Video lectures and code walkthroughs
PyTorch notebooks and datasets
Additional reading materials and references

✅ Learning Outcomes:

By completing this course, you will be able to:

Understand and implement fundamental and advanced deep learning models in PyTorch
Build and train MLPs, Transformers, CNNs, and generative models
Apply Transformer architectures to real NLP and vision tasks
Gain hands-on experience with pre-training, fine-tuning, and Hugging Face tools
Confidently explore state-of-the-art AI models and contribute to modern AI projects