> By getting into machine or deep learning I mean building upto a stage to do ML/DL research.
> The target ability:
> 1. To understand the theory behind the algorithms
> 2. To implement an algorithm on a dataset of choice. (Data
cleaning and management should also be learned)
> 3. Read research publications and try to implement them.
There are many different ways that people do ML/DL research these days. Some people do more theory-work which will necessarily be more focused on mathematics, and others do more of an applied approach which will be more focused on coding and iterating.
For theory-driven work, I think Michael I Jordans list is still pretty solid:
> https://news.ycombinator.com/item?id=1055389
I would focus on the fundamentals first though:
1. get a solid background in mathematics
- analysis (a suggestion is Baby Rudin)
- probability (Grimmet and Stirzaker, maybe something with measure theory after)
- statistics (Casella and Berger or Wasserman's book is a good start)
2. get a solid foundation in statistical machine learning
- Introduction to Statistical Learning is a fantastic start
- Then choose 1 or both of the following:
- Elements of Statistical Learning for a Frequentist Approach
- Pattern Recognition & Machine Learning for a Bayesian Approach
3. get a baseline understanding of deep learning
- the deep learning book by Goodfellow is decent
- start reading papers here and trying to implement them
If you get through to this last step, you are probably solid enough to get a job building models. If that's the route you want, then begin iterating on learning about new approaches in papers (look for papers with code / data) and implementing them.
If you want to go the academic route, you have enough of a view of the field to begin specializing further. Choose a sub-domain and dig deep if you want to do more deep learning work. Maybe revisit Michael I Jordan's list if you're still confused about where to go. A lot of those books will feel a lot more familiar.
Best of luck!