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Data Science & ML track

Generative AI

GANs, diffusion, autoregressive LLMs and generative workflows.

8 concepts3 labs1 challenges
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Concepts

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Work through the concepts in order, or jump to what you need.

1

Generative Models Overview

Concept · Hands-on lab included

2

Language Model Training

Concept · Hands-on lab included

3

Prompt Engineering

Concept · Hands-on lab included

4

Inference & Decoding Strategies

Concept · Hands-on lab included

5

Retrieval-Augmented Generation (RAG)

Concept · Hands-on lab included

6

Fine-Tuning & Adapters (LoRA)

Concept · Hands-on lab included

7

Multimodal Generation

Concept · Hands-on lab included

8

RAG in Production

Concept · Hands-on lab included

Practice

Hands-on labs

Apply what you've learned in guided, production-style environments.

Browse all labs

Compare Decoding Strategies

30 min · intermediate · Inference & Decoding Strategies

Build a Mini RAG Pipeline

40 min · intermediate · Retrieval-Augmented Generation (RAG)

Score a RAG Pipeline

35 min · advanced · RAG in Production

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Next modules

Keep your learning streak going with related modules.

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Mathematics

Linear algebra, calculus and notation needed for machine learning.

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Statistics

Distributions, estimators, hypothesis testing and statistical inference.

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Python

Scientific Python stack: environments, typing, numpy-style thinking.

DevOpsForge - Data Science & ML · Generative AI
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