This is my roadmap to go from an undergraduate CS and math background to a very good understanding of Machine Learning and Deep Learning concepts, and more importantly to successfully apply ML and DL. I will put here everything I’ve done (in italic), I’m doing (in bold), and I’m planning on doing (normal text) in order to reach that goal.
Courses
- Andrew Ng’s Coursera Machine Learning course
- fast.ai course part 1 v3
- Udacity Intro to deep learning with PyTorch
- fast.ai course part 2 v2
- (when it’s out) fast.ai part 2 v3
- fast.ai machine learning course
- deeplearning.ai Coursera Deep Learning Specialization
Personal projects
- Write a deep learning web app
- Blog about deep learning, preferably about a technical subject (like a dive in fast.ai source code)
- Contribute to the fast.ai library
- Take part in Kaggle competitions and reach top 10%
- Freelance to gain real world experience in deep learning
Books and blogs
- Deep learning, Ian Goodfellow et al.
- Neural Networks and Deep Learning, Michael Nielsen
- Colah’s blog
- Distill
- (when it comes out) Terence Parr and Jeremy Howard book (some material available at explained.ai)
General CS and Software Engineering resources
- Data Structures and Algorithms in Python by Michael T. Goodrich
- Cracking the Coding Interview by Gayle Laakmann McDowell
- Python 3 : Deep Dive (part 1 and part 2).