| > I am very motivated to delve into this space (it's been on my mind a while) and I want to do it right, which is why I am asking for personal experiences on this forum given that there is a very healthy mix of technology hobbyists as well as professionals on HN, of which the opinion of both is equally valuable to me for different reasons. Regarding personal experiences, I moved to ML engineering after almost 15 years in Software Development. I found it challenging at first, to cope up with the terminology and Math. Although I was able to create data processing pipelines and simple models it was still a mystery how it all worked. After a good year and a half of trying to teach myself ML, I decided that I needed formal education. After researching possible options that work would for my work schedule and skill level, the Stanford SCPD AI Certificate program seemed to be the best. Here are some useful pointers (in no particular order). - This blog by Pavel helped me a lot, to understand what the course was about and how to approach it -- http://coldattic.info/post/122/ - Most of Stanford Lectures notes and slides are publicly available. CS229 is a good beginner class to take (http://cs229.stanford.edu/syllabus.html) - The best and the most interesting IMO, is CS236 on Generative Modeling. It is taught by Prof. Ermon and his team. Some of the topics covered in class (especially Score based models) were mind blowing. Here is a talk by Prof. Ermon if you are interested in generative modeling (https://www.youtube.com/watch?v=8TcNXi3A5DI). - If your math skills are a bit rusty, then you will have to practice and work a lot more. I found the TA sessions and office hours extremely helpful. Some additional personal experiences: - "Deep Learning with Python" by Francois Chollet (Creator of Keras) is a good book to get started. The code samples are in TF Keras and easy to understand and implement. - Avoid TensorFlow if you can. Its unnecessarily complicated (personal opinion). You will find PyTorch and PyTorch Lightning much more approachable to start learning. - I also found Kaggle tutorials helpful for practical aspects of ML. For example: Categorial Variables (https://www.kaggle.com/alexisbcook/categorical-variables). - Yannic Kilcher's ML News series is a great way to keep in touch with the latest events in ML (https://www.youtube.com/c/YannicKilcher). Also very entertaining :) - Prof. Jeff Heaton has a bunch of good videos on practical ML applications - https://www.youtube.com/c/HeatonResearch ML is very exciting and rewarding, Good luck on your new adventure! Feel to reach out and I would be happy to help in any way. |