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GPUs vs TPUs vs NPUs
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How batches affect convergence
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FP16, BF16, QLoRA — precision formats for real-world training
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Distributed training basics (Data Parallel vs Model Parallel)
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Memory bottlenecks & compute cost planning
MODULE 1 Generative AI Foundations
Build a solid conceptual and architectural understanding.
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MODULE 2 Deep Learning Essentials for Generative AI
This module builds the technical backbone required to understand and create generative AI systems. Learners move from basic neural network mechanics into the advanced architectural concepts that power modern LLMs. The lessons drill into how networks learn, how attention replaces recurrence, and why deep learning techniques like residual connections, layer normalization, and transformer-based patterns dominate current AI systems. The module also covers the practical realities of training—optimizers, loss functions, precision formats, GPU requirements, and distributed strategies—ensuring learners can reason about model performance, stability, and scalability. By the end, students understand the essential engineering foundations behind any production-grade generative model.
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MODULE 3 Transformer Architecture & LLM Internals
This module provides a complete, practical understanding of the architecture powering modern generative AI systems: the Transformer. The content demystifies how these models process language, how attention mechanisms work, and why Transformers dominate every state-of-the-art model from GPT to Gemini to LLaMA. You’ll learn how the encoder and decoder blocks function, how they differ, and how they interact in more complex tasks like translation and summarization.
MODULE 4 Embeddings, Vector Databases & Semantic Search
A large part of the module focuses on self-attention, the mathematical engine that enables models to reason over long sequences and understand relationships between tokens. You’ll dismantle the Query–Key–Value mechanism, attention scores, softmax scaling, and multi-head attention, gaining a concrete understanding of what is actually computed inside each layer. You will also analyze cross-attention, used heavily in instruction-tuning, multi-modal pipelines, and closed-book question answering.
MODULE 5 Retrieval-Augmented Generation (RAG) Systems
This module focuses on Retrieval-Augmented Generation (RAG), the architecture that turns large language models into reliable, knowledge-grounded systems suitable for enterprise use. You’ll learn why LLMs hallucinate, where their knowledge limits lie, and how retrieval solves these limitations by supplementing the model with external facts at inference time.