I am on Day 27 today and I’m quite convinced already that consistent efforts, however small, can help someone go a long way. I’ve been wanting to actively pursue Machine Learning and Data Science for more than a year now but haven’t been consistent and usually forget after 3-4 days.

The challenge includes posting what you do on your social media handles so that you stay more committed to this challenge. After a few days, the habit sticks and you simply can’t go to sleep without learning something. I’ve had a few very busy and tiring days too in these 27 days, but I’ve made sure I did something at least in those days. I’d strongly recommend anyone who’s passionate about Machine Learning to take up this challenge.

I post my challenge details in the blog below, on Github, on Twitter, and my projects on Linkedin. https://hitheshai.blogspot.com

Here’s a summary of how much I was able to learn because of this challenge in 25 Days. Also, getting best wishes from Josh Starmer on one of my Twitter posts (a scholar who runs Statquest channel on YouTube, one of the best in the genre) was a great deal of encouragement.

(Note: In my version of this challenge, I don’t necessarily have to code everyday because of college and other commitments. Some days, even watching a single YouTube video might be sufficient as long as I make some progress from the previous day.)

Completed two MOOCs on Coursera •Machine Learning (Days 1-10) •Neural Networks and Deep Learning, Part 1 of Deep Learning Specialization (Days 20-25)

Did 2 mini-projects •Clustering (Day 4) •Anomaly Detection (Day 7)

Participated in a Kaggle competition (Boston House Price Prediction) and learnt useful tree based models and data cleaning techniques. (Days 11-18)

I’d be very surprised if no one has tried this before. Imagine a GAN, except instead of a generator generating fake samples and a discriminator trying to distinguish between real and fake samples, you have a generator and a classifier where the generator is attempting to find, for example, the optimal set of 10 pixels to remove from the image the classifier is attempting to classify, in an attempt to get the classifier to be more robust. The only reason I haven’t tried to search and see if this has been done is because I don’t know what I would search for, hahaha

In this paper: https://arxiv.org/pdf/1612.08083.pdf

In figure 1 I see Y = softmax(W H_L) and can’t figure out what W is (I don’t see it discussed in the paper) – it appears to be different than the matrix of conv kernels W in equation 1.

Can someone help explain how Y is computed and what that W is?

Hello everyone. I have written the equivalent of arxiv_abstract_bot, but for OpenReview. The code is available here. Example of the comments here.

I’m training a neural net in Keras for the prediction of two-person sports contests. The data are therefore not time series as such, but they are time-ordered, so I’m doing walk-forward validation to calibrate model complexity.

I’ve experimented with weight decay, drop-out and L1/L2 regularisation. The model always performs best on unseen data when there is no regularisation at all. This feels intuitively wrong.

Has anyone experienced something like this before, and is there an obvious answer to why this might happen? Failing that, any tests that I can do to help diagnose the problem?

This is a place to share machine learning research papers, journals, and articles that you’re reading this week. If it relates to what you’re researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you’ve read.

Please try to provide some insight from your understanding and please don’t post things which are present in wiki.

Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links.

Previous weeks :

Most upvoted papers two weeks ago:

Besides that, there are no rules, have fun.

I was wondering if anyone could clear up some of the roles within machine learning. I’ve noticed on sites like Indeed and Glassdoor that there are jobs that are titled “Machine Learning Engineer” and “Software Engineer – Machine Learning”, like this. Is there an actual difference between the two, or are they pretty much interchangeable? I realize that different companies will handle these roles differently, but is there a general thread that differentiates between these two machine learning roles?

I stumbled across a paper today from 2016 which presents reasonable evidence that Gaussian error linear units (GELU) perform better than RELU.

https://arxiv.org/pdf/1606.08415.pdf

I have a couple ideas about why I’ve never heard of this before and I’m curious what others think.