I'm a developer, how can I become an ML developer?
summary
Practical roadmap: follow the TensorFlow Developer Professional Certificate (CNNs, NLP, time series) then a deep learning nanodegree with PyTorch and a Stanford ML refresher; practice with projects — an autoencoder project for denoising, a digit recognizer using TensorFlow data APIs and dataset caching (faster training), and a book-cover classifier that fails on tiny thumbnails; contribute to docs and OSS (9,000 API docs updated, 40+ blog posts reaching ~250k readers), and consider certification, mentorship, and mentoring 2–3 students to sharpen skills.
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chapters (6)
- 0:00Start
- 0:38Software Engineering
- 9:56Learning from experience
- 16:39Knowledge sharing
- 21:58A poet and a math major
- 32:44Developer Advocacy at Google
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