Hi, i’m looking for an advice or an idea. I’m writing a thesis and i chose to use what i learned in another project (prediction using Google Trends) to investigate the difference between the meaning of a search on Google and a Tweet on Twitter.

I’m using Pytrends and Tweepy and i collected some data about a set of trending topics. I’m comparing them using various techniques, but i’d like to use those data in some kind of ML model (recomendation maybe?).

I’d really appreciate some advice to make my thesis more than just a data analysis, thanks in advance!

Since the vocab file has over 30,000 entries a softmax vector would be gigantic.

But a similarity lookup would recquire embedding the vocab file myself, correct? In a way similar to the BERT embedding, which we can’t get because the entire point of BERT is an embedding depndent on the text itself.

Obligatory disclaimer – this is not my project, I just thought it’s awesome and you guys should know about it.

This time, it’s Directions OVer PANDAs or DOVPANDA. From the GitHub description:

Directions are hints and tips for using pandas in an analysis environment. dovpanda is an overlay for working with pandas in an analysis environment.
If you think your task is common enough, it probably is, and Pandas probably has a built-in solution. dovpanda is an overlay module that tries to understand what you are trying to do with your data, and help you find easier ways to write your code.

In other words, it will help you write better Pandas code by giving you great hints & tips on how to do just that.

I always like packages with a simple integration, and this one is no different. Just:

import pandas as pd import dovpanda 

And you’re good to go.
It’s most useful when working via notebook but displays hints and tips in the console as well.

I wonder if there are any similar tools for other widely used data science / ML libraries. Please share if you know any.

An article by Lilian Weng discuss self-supervised learning techniques applied to images (there are many articles on self-supervised NLP), with an emphasis on control-based RL tasks.

https://lilianweng.github.io/lil-log/2019/11/10/self-supervised-learning.html

20.6% acceptance rate this year compared to 16.2% last year. Another “record number of submissions.” 7737 papers reviewed this year compared to 7095 last year.

It used Common crawl for training data which i believe is a crawl of the entire internet right?

So what if the GLUE and SuperGLUE task answers were in the training data?

During training I generate triplets using an anchor sample along with a positive match and negative match. The L2 distance of each from the anchor is used to calculate loss, but how does this work in the deployed case where you have only two samples where one might be an anchor? I have been looking at FaceNet but the paper only mentions “a squared L2 distance threshold”. Is this something that needs to be determined empirically? Or is there a better analytical solution?

Personally I like to watch(/listen) moreso than read,

what youtube channels would you recommend for interesting developments/information w.r.t. ML or AI.

I would recommend, in no particular order:

• This week in Machine Learning and AI
• Lex Fridman’s Artificial Intelligence podcast
• Deepmind
• Institute for Advanced Study
• Microsoft research
• Two Minute Papers

This is my implementation of the proposed Fast-SCNN. You can learn more about it [here](https://arxiv.org/abs/1902.04502)

https://github.com/kenanajkunic/fast-scnn

https://tfworld.devpost.com/

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