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)

🔥 Applied Deep Learning - 2021 🔥

Just came across this absolute gem.

200+ free video lectures on all sorts of topics related to deep learning.

From Graph Attention Networks to RL algorithms.

repo: https://t.co/2IRF3CcR5p

playlist: https://t.co/N4zqkqnfdu

Machine Learning Notes

I've been writing notes introducing some of the most important topics in AI today.

This thread lists a few notes I've published so far:

Introduction to Machine

Introduction to Deep

Introduction to Sequential

Introduction to Deep Computer

Some pretty amazing slides and notes for this Data Structures course.

https://t.co/JF7FSFKFT6

Recently, I shared a list of some courses that were useful in my transition to machine learning.

While I took most of the courses in person, there are some alternative online courses you can check out.

Here is a thread of a few interesting online courses based on the list:

⭐️ Linear Algebra ⭐️

A classical online course by Professor Gilbert Strang based on his popular textbook "Introduction to Linear Algebra". Learn about matrix theory and systems of

⭐️ Introduction to Complex Analysis ⭐️

Learn about the geometry of complex

⭐️ Differential Calculus ⭐️

Pay close attention to the chain rule as it's heavily referenced in machine learning, specifically when discussing optimization. This course is part of a specialization called MathTrackX. I recommend checking that as

⭐️ Information Theory ⭐️

When you are working with machine learning algorithms applied to data you are dealing with information processing which in essence relies on ideas from information theory such as entropy. This course should provide the

Tired of word clouds? Want to do better sentiment analysis? Not sure how to look at the words underneath your measures?

Our long overdue paper on generalized word shift graphs is finally here!
https://t.co/lIBXvbMJWX
https://t.co/vSL1REYT8V

So what are they?

1/n

If we have two texts, there are many ways we can compare them. Weighted averages are a particularly useful measure because they're flexible and interpretable

Proportions, Shannon entropy, the KLD, the JSD, and dictionary methods can all be written as weighted averages

2/n

But weighted avgs are also slippery. When we try to compress complex phenomena like happiness, surprise, divergence, or diversity into a single number, it can be unclear what we're measuring

If the measure goes up, what does that mean? Why did it do that? Can we trust it?

3/n

Very often, that's the end of the line and we're left with an uneasy feeling in the pit of our stomach that our weighted avg is actually picking up a data artifact or some other unintended peculiarity

Word shift graphs help us address those concerns

4/n

First, word shifts look under the hood of weighted averages to see what's going on

All weighted averages are a sum of contributions from individual words. We can pull out those words, and rank which ones contribute the most to the difference between two texts

5/n

Tips for AI writers:

1. Spend 30% of your effort on skimming all student ML papers (e.g. Stanford NLP CS224n) the past 3 years and prototype your favorites

The idea is everything. Pick an area you are interested in and ideally something that has a visual aspect to it

Most of my 'on the top of my mind' ideas were bad in retrospect. Skimming 100s of student papers will give you an overview of what's interesting.

Student papers are overlooked, easy to understand, and have good compute constraints.

2. Spend 30% on your effort on coding

Create an edge to the project. Apply it to something new and use FastAI or Keras to improve the accuracy with 5-30%.

3. Spend 30% writing an in-depth article

Have a north star article in terms of structure and quality. Find something that stretches you to your utmost capability. I used @copingbear’s Style transfer article:

4. Spend 10% marketing your project

Invest a week in studying the strategies to rank on sites like HN and Reddit, then use them. If you have an interesting result and a great article, you've done the hard work.

This is a more wonky thread about how I made this visualization in #Rstats using the awesome visNetwork

First step is to create the underlying network data. We need one file of "nodes" - i.e. the people and organizations. And one file of "edges" - i.e. the connections between them.

I created these by hand, based on excellent investigate journalism:

Now we can pull these together to create a network visualization!

You'll notice that I included a column for "type" in the nodes file. This allows me to use different icons for people vs firms vs political organizations.

All the icons are taken from @fontawesome. I *think* the visNetwork 📦 currently only works with fontawesome version 4.7, which is a bit limited – e.g. I decided to use a book icon to represent the fringe Evangelical Christian sect "Exclusive Brethren"! 😂

I very much enjoyed getting to use the "incognito" icon to represent all the unknown donors that have funded Tory MP Owen Paterson's overseas jaunts!

An amazing new project from @bearpelican was just released: https://t.co/DBov6sZTVS . A beautiful design; you can auto-generate a melody from chords, chords from a melody, and more.

It's technically brilliant, combining BERT, seq2seq, and Transformer XL
https://t.co/jF3mO5aXiu

It's also a wonderful example of leveraging and customizing the fastai framework in a deep & thoughtful way.

Here's the full set of blog posts diving in to this