A Front-end dev's hobbyist approach into Machine Learning
summary
Documents a three-month hobbyist journey integrating tensorflow.js into React projects, with concrete notes on setup, testing, bundling and tradeoffs for client-side versus server-side models. Demonstrates in-browser training and a MNIST-style demo that plots loss and accuracy, explains convolutional nets, SGD, activation choices, batch sizing, and when to use static vs dynamic architectures. Shows prototypes that train and predict in-browser and discusses performance and deployment implications.
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chapters (5)
- 0:00Start
- 16:01TENSORFLOWJS CORE CONCEPTS
- 18:36KEY CONCEPT 1 DATA
- 25:23KEY CONCEPT 2 MODEL
- 32:33KEY CONCEPT TRAINING
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