I have always emphasized on the importance of mathematics in machine learning.

Here is a compilation of resources (books, videos & papers) to get you going.

(Note: It's not an exhaustive list but I have carefully curated it based on my experience and observations)

📘 Mathematics for Machine Learning

by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong

https://t.co/zSpp67kJSg

Note: this is probably the place you want to start. Start slowly and work on some examples. Pay close attention to the notation and get comfortable with it.
📘 Pattern Recognition and Machine Learning

by Christopher Bishop

Note: Prior to the book above, this is the book that I used to recommend to get familiar with math-related concepts used in machine learning. A very solid book in my view and it's heavily referenced in academia.
📘 The Elements of Statistical Learning

by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie

Mote: machine learning deals with data and in turn uncertainty which is what statistics teach. Get comfortable with topics like estimators, statistical significance,...
📘 Probability Theory: The Logic of Science

by E. T. Jaynes

Note: In machine learning, we are interested in building probabilistic models and thus you will come across concepts from probability theory like conditional probability and different probability distributions.
📺 Multivariate Calculus by Imperial College London

by Dr. Sam Cooper & Dr. David Dye

https://t.co/OYaqzlXmJG

Note: backpropagation is a key algorithm for training deep neural nets that rely on Calculus. Get familiar with concepts like chain rule, Jacobian, gradient descent,.
📜 The Matrix Calculus You Need For Deep Learning

by Terence Parr & Jeremy Howard

https://t.co/Gk96dRsX5t

Note: In deep learning, you need to understand a bunch of fundamental matrix operations. If you want to dive deep into the math of matrix calculus this is your guide.
📺 Mathematics for Machine Learning - Linear Algebra

by Dr. Sam Cooper & Dr. David Dye

https://t.co/lNYLiMKLma

Note: a great companion to the previous video lectures. Neural networks perform transformations on data and you need linear algebra to get better intuitions.
📘 Information Theory, Inference and Learning Algorithms

by David J. C. MacKay

Note: When you are applying machine learning you are dealing with information processing which in essence relies on ideas from information theory such as entropy and KL Divergence,...

More from elvis

The past month I've been writing detailed notes for the first 15 lectures of Stanford's NLP with Deep Learning. Notes contain code, equations, practical tips, references, etc.

As I tidy the notes, I need to figure out how to best publish them. Here are the topics covered so far:


I know there are a lot of you interested in these from what I gathered 1 month ago. I want to make sure they are high quality before publishing, so I will spend some time working on that. Stay


Below is the course I've been auditing. My advice is you take it slow, there are some advanced concepts in the lectures. It took me 1 month (~3 hrs a day) to take rough notes for the first 15 lectures. Note that this is one semester of

I'm super excited about this project because my plan is to make the content more accessible so that a beginner can consume it more easily. It's tiring but I will keep at it because I know many of you will enjoy and find them useful. More announcements coming soon!

NLP is evolving so fast, so one idea with these notes is to create a live document that could be easily maintained by the community. Something like what we did before with NLP Overview: https://t.co/Y8Z1Svjn24

Let me know if you have any thoughts on this?

More from Data science

You May Also Like

Recently, the @CNIL issued a decision regarding the GDPR compliance of an unknown French adtech company named "Vectaury". It may seem like small fry, but the decision has potential wide-ranging impacts for Google, the IAB framework, and today's adtech. It's thread time! 👇

It's all in French, but if you're up for it you can read:
• Their blog post (lacks the most interesting details):
https://t.co/PHkDcOT1hy
• Their high-level legal decision: https://t.co/hwpiEvjodt
• The full notification: https://t.co/QQB7rfynha

I've read it so you needn't!

Vectaury was collecting geolocation data in order to create profiles (eg. people who often go to this or that type of shop) so as to power ad targeting. They operate through embedded SDKs and ad bidding, making them invisible to users.

The @CNIL notes that profiling based off of geolocation presents particular risks since it reveals people's movements and habits. As risky, the processing requires consent — this will be the heart of their assessment.

Interesting point: they justify the decision in part because of how many people COULD be targeted in this way (rather than how many have — though they note that too). Because it's on a phone, and many have phones, it is considered large-scale processing no matter what.