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.
Also, cross-publish. Aim for top tech publications on Medium(e.g. @TDataScience), @freecodecamp's blog and youtube channel, @hackernoon's blog, FastAI's blog, Keras's blog, @thepracticaldev, and email 10-30 established tech sites like @thenextweb.
5. Spend 1-2 months on each article/project

Articles will market you 24/7 worldwide. You want them to be relevant for a decade. High-quality articles increase your reputation and spread easier on the web.

cc @remiconnesson @mehtadata_

More from Data science

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

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.
To my JVM friends looking to explore Machine Learning techniques - you don’t necessarily have to learn Python to do that. There are libraries you can use from the comfort of your JVM environment. 🧵👇 : Deep Learning framework in Java that supports the whole cycle: from data loading and preprocessing to building and tuning a variety deep learning networks. Framework for defining machine learning models, including feature generation and transformations, as directed acyclic graphs (DAGs). a machine learning library in Java that provides multi-class classification, regression, clustering, anomaly detection and multi-label classification. : TensorFlow Java API (experimental)

You May Also Like

“We don’t negotiate salaries” is a negotiation tactic.

Always. No, your company is not an exception.

A tactic I don’t appreciate at all because of how unfairly it penalizes low-leverage, junior employees, and those loyal enough not to question it, but that’s negotiation for you after all. Weaponized information asymmetry.

Listen to Aditya

And by the way, you should never be worried that an offer would be withdrawn if you politely negotiate.

I have seen this happen *extremely* rarely, mostly to women, and anyway is a giant red flag. It suggests you probably didn’t want to work there.

You wish there was no negotiating so it would all be more fair? I feel you, but it’s not happening.

Instead, negotiate hard, use your privilege, and then go and share numbers with your underrepresented and underpaid colleagues. […]