Phase 3: Deep Learning & Generative AI

1. Neural Networks

Neural networks are layered models that learn complex patterns. Start with perceptrons, then move to multilayer networks and backpropagation.

2. Convolutional Neural Networks (CNNs)

CNNs are specialized for images and spatial data. They are widely used in computer vision tasks like object detection and image classification.

3. Recurrent Networks and Sequence Models

RNNs and LSTMs process sequences like text or time‑series data. While they are less common today, they still provide the conceptual bridge to transformers.

4. Transformers and LLMs

Transformers are the architecture behind GPT‑style models. They use self‑attention to process long sequences efficiently and are now the foundation of most generative AI systems.

5. RAG and Modern AI Pipelines

Retrieval‑Augmented Generation (RAG) combines LLMs with external knowledge sources to improve accuracy and reduce hallucinations. This is critical for production systems.

FAQ

Do I need GPUs for deep learning?

For serious training, yes. But you can start with smaller models or free cloud notebooks.

Why are transformers better than RNNs?

Transformers process sequences in parallel and scale to much larger datasets.

What is the fastest way to get started?

Try a small CNN or fine‑tune a pretrained transformer on a real dataset.