An innovative approach +encouraging results make this paper worthwhile

Medical Machine Learning has serious issues when it comes to reproducibility and data.

  1. Thanks to differing data regulations, data sharing across borders can be difficult. This can lead to a lot of problems when it comes to the reproducibility of their research. To read more about the replication crisis, check out this article that explains this issue and what to do about it.
  2. More specific to Medical Machine Learning, datasets themselves are often small. Creating larger datasets requires a lot of expert time invested. …


This one has a full Wikipedia Page Dedicated to it.

As I was reading the paper, “SinGAN-Seg: Synthetic Training Data Generation for Medical Image Segmentation”, something stood out to me. Here we have an excerpt from the paper:

According to the analyzed medical image segmentation studies in [33], 30% have used private datasets. As a result, the studies are not reproducible. Researchers must keep datasets private due to medical data sharing restrictions.

This seemed like a pretty big deal to me. As someone involved in Machine Learning Research, the fact that 30% of the Datasets are private is kind of worrying. When results are not reproducible this causes issues. Not…


No, I’m not going to tell you to do personal projects

As an educator in the Machine Learning space, I often see lots of people give out advice on how beginners can break into Machine Learning and do well there. Typically this advice follows something along these lines:

  1. Take a combination of Math Classes. Typically involving Probability and Statistics, Calculus, Linear Algebra, and Logic.
  2. Find one or two personal projects that interest you. Really good advice will suggest building an end-to-end pipeline starting from Data Collection all the way to analysis and report generation.
  3. Look at some tutorials/documentation online for learning the implementations.
  4. Enjoy your ML expertise.

Some might recommend ML…


The super-secret method developed by ancient masters

Aside from my machine learning research, I am also involved in tutoring people to ace their technical interviews. The people that reach out to me are of two types.

  1. They are students struggling with coursework. This is the most diverse group, and they ask me to help with everything from coding, to math, machine learning, or theoretical computer science (who didn’t struggle with Finite Automata).
  2. Working professionals. These are people that are working full-time jobs in tech. They have decided to either transition into software development (like this student of mine who nailed a Facebook Software Dev job after starting…


The framework is simpler AND outperforms everything

Photo by Shahadat Rahman on Unsplash

Machine Learning is a diverse field with lots of different aspects. One of the chief concerns of ML is the learning of visual representations (pictures/diagrams etc corresponding to areas). This has applications in all kinds of problems, ranging from Computer Vision, Object Detection, to more futuristic applications like learning from and developing schematics. Recently, people have started to look into Contrastive Learning as an alternative to supervised and unsupervised learning. It involves teaching the model to learn the general features of a dataset without labels by teaching the model which data points are similar or different. The focus thus shifts…


Hint: Did not bribe the hiring team

Software Engineering at one of the big firms is an extremely lucrative job. According to Glassdoor, the level 3 (entry-level) software engineer at Facebook earns … (take a guess. Then take a sip of water and sit down, because the number is higher than what you were expecting)

The typical Facebook Software Engineer III salary is $120,261- glassdoor.com

That’s for entry-level. Just the interns at Facebook are said to earn around 8.5K USD a month with benefits. It’s only natural that these jobs are very prestigious and very competitive (A Google Interview is 10 times harder to get than acceptance…


Robust models, Stronger performance, Lesser Data Needed

Recently my AI class went over Hill Climbing and Different modifications to the protocol. We had the opportunity to implement several improvements and tweaks like stochastic hill climbing (we don’t always pick the best one, just something better), simultaneous hill climbing ( we run a batch of k algorithms at once, and pick the k best states at every turn), etc. What was most interesting to me, however, was the random restart hill-climbing. Here, once we reach local maxima, we restart from a new random state. We keep track of the best performer, and once we finish (run out of…


Why am I doing what I am doing

Recently, I picked up new research work in Deepfakes Detection. I am interested in this field for a number of reasons, a major one being that there is a lot of potential for exploration here. Unlike fields like Customer Segmentation, Health System Analysis, or Parkinson’s Disease detection (my prior work experience), the rules of engagement are messy.

With the ever-changing nature of DeepFakes, we don’t have a true representative dataset that I can just analyze.

People find new ways to create a DeepFake, which will throw off detection algorithms. The addition of more types of videos (especially as the world…


You mean that we can use the best of both worlds?

Recently I’ve been learning about Discriminative and Generative Modeling in honor of a breakdown of a very special paper, coming soon. Learning about these topics, I was fascinated by the nuances behind these two approaches, and how they are implemented. As I learned more, I came across the fact that models now tend to combine the best of both worlds. In this article I will be going over some of the hybrid approaches, talking about the problems they solve. By the end, you will hopefully have some knowledge of these approaches and may even choose to implement one of these…


This way of measuring Digital Strategy can be huge for investors

I am someone into Investing and Machine Learning. I think these are both fields that have a rich complexity, ones that when understood can be leveraged to drastically improve quality of life. The authors of the groundbreaking “Deep Learning Framework for Measuring the Digital Strategy of Companies from Earnings Calls” combine the fields in an interesting way. The authors take the earnings calls of Fortune 500 Companies, and apply Natural Language Processing (NLP) with Deep Learning to classify their strategy into various labels.

In this article, I will talk about the paper, breaking down some interesting points to note. I…

Devansh

I write high-performing code and scripts for organizations to help them generate more revenue, identify areas of investment, isolate redundancies, and automate

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