Full Stack Deep Learning
FSDL 2022

Lecture 01: When to Use ML and Course Vision

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Formulating the problem and estimating project cost

Sourcing, cleaning, processing, labeling, synthesizing, and augmenting data

Picking the right framework and compute infrastructure

Troubleshooting training and ensuring reproducibility

Deploying the model at scale

✨ Monitoring and continually improving the deployed model ✨

✨ How ML teams work and how to manage ML projects ✨

✨ Building on Large Language Models and other Foundation Models ✨