Back to Roadmaps
Artificial Intelligence
Machine Learning, Neural Networks, and NLP.
Section: Artificial Intelligence
01
History - what is AI, how did we end up at transformers (Fast tracking to AI)
02
History - what is DL, backprop, NLP
03
Neural networks, Pytorch
04
Optional extra class - RNNs, LSTMs, Sequential models
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Optional extra class - CNNs
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Coding simple attention
07
Vanilla attention to industry -- Coding other variations of attn - Adding Kv cache, MQA, GQA, (Grouped q atn), MLA (multi-head latent attention), DSA
08
Huggingface end to end - datasets, spaces, models, blogs, courses etc.
09
Instrumenting LLM calls/observability/tracing
10
Vector DBs and RAG
11
Context engineering - Summarization, data collection
12
Agents from first principles. Building an agent framework.
13
Agent frameworks
14
Memory
15
MCP
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Computer use & multimodal agents
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What is Finetuning
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Finetuning a model for any usecase
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RL fine tuning.
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Evals -- Testing agents.
21
Advance topics
22