ISLP (Introduction to Statistical Learning)
Used in: ML & DL Foundations
Review: A not-so-gentle introduction to statistical learning. Great if you're just learning ML, with great Python examples — but it can get lengthy.
My Learning Roadmap
A living timeline of what I'm studying — arranged by timeline and dependency. Want to propose changes or share resource advice? Send me a personal email (it's in the footer). We're all in the loop, after all 😉
ML & DL Foundations
DoneBasic concepts in ML & DL, setting the stage for more advanced topics.
- ISLP (Introduction to Statistical Learning)
- Machine Learning with PyTorch and Scikit-Learn
NLP & Generative DL
DoneFocused on understanding transformers and text generation.
- NLP with Transformers
- Generative Deep Learning
Time Series
In progressExploring time series forecasting with both classical and DL approaches.
- Time Series Analysis and Its Applications
- Deep Learning for Time Series Forecasting Cookbook
- Weekly Paper: Transformer Variants
ML Theory & Math
Not startedDeepening understanding of ML theory and foundational math.
- Elements of Statistical Learning
- Math for Machine Learning
Transformer Deep Dive
In progressReading core papers and internals of transformers for various modalities.
- Attention is All You Need (Vaswani et al.)
- Upcoming Papers…
Resources & Reviews
Machine Learning with PyTorch and Scikit-Learn
Used in: ML & DL Foundations
Review: Honestly the best book I've read so far on ML and DL. Brings complex concepts like GNNs across in a fun, simple way — a must read!
NLP with Transformers
Used in: NLP & Generative DL
Review: Covers HuggingFace's ecosystem and modern NLP. Very hands-on and up to date.
Generative Deep Learning
Used in: NLP & Generative DL
Review: Walks you through generating images, text, and more with DL models. Great for GenAI beginners.
Time Series Analysis and Its Applications
Used in: Time Series
Review: Still not complete, but enjoying the math and exercises.
Deep Learning for Time Series Forecasting Cookbook
Used in: Time Series
Review: Didn't read enough for a review yet!
Elements of Statistical Learning
Used in: ML Theory & Math
Review: Tried reading this before ISLP and found it really hard — can't wait to try again!
Math for Machine Learning
Used in: ML Theory & Math
Review: Essential math — linear algebra, calculus, probability — explained with ML context.
Attention is All You Need (Vaswani et al.)
Used in: Transformer Deep Dive
Review: A must-read to understand how transformers work. Consider reading my bachelor's thesis too, hehe.