I continued the “AI for Everyone” course today.
Key takeaways:
- Deep learning, a subset of machine learning, is synonymous with neural networks (some shallow neural nets aren’t strictly ‘deep learning’, but broadly speaking, ‘deep learning’ is the catch-all term people have settled with for neural networks).
- ML works well when you have (a) simple concepts and (b) lots of data. Hard concepts are things like trying to precisely predict what someone will think or do.
- When working on ML projects, a good idea is to start small with proof-of-concepts early, even if you don’t have all the perfect data you’d want. You can then iterate and figure out what types of data work well, and how to build better. This is an important lesson for me and something I’ve been practising plenty. Building small projects. Running small experiments. Writing throwaway code, and only refining things later if something shows some promise.