I know it’s easier to learn robotics when I’ve decided on a robot to build. In the same way, I bet it’s easier to learn ML when you have a specific goal in mind. Maybe even something novel. So.

My challenge to you is to train a ML artist. this would be a network that inputs a bitmap picture and outputs a set of vectors that could be drawn by a plotter robot. I have lots of non-ML image->vector converters and I’m a big fan of turtletoy.net. I would like to see your network run on turtetoy given a source image. They could be any style you want. You don’t have to share your training data, just the final resulting weights and the NN to run in javascript on their site. Your result would be public for everyone to try and enjoy, while your trade secret training stays all yours.

I suspect I’m going to get a lot of “that’s dumb I won’t do that”, and I feel embarrassed making this post. But you miss 100% of the shots you don’t take, right?

Extrapolation on math is hard for NNs. We propose a new set of benchmarks and find current methods are fragile https://arxiv.org/abs/1910.01888.

So how should we build NNs that can learn the logic behind math? Inductive bias? More data does not seem to solve this problem!

A recent blog post How Exactly UMAP Works provides a different perspective on explaining the UMAP dimensionality reduction, providing a more direct comparison with t-SNE in terms of computational approach. While the post is unfairly dismissive of t-SNE, readers here may gain some insight from this different presentation and detailed comparisons of how and why UMAP and t-SNE differ in various aspects on different tasks.

Application US15/009,647 events

2015-01-28 Priority to US201562108984P

2016-01-28 Application filed by Google LLC

2016-07-28 Publication of US20160217368A1

2019-09-17 Publication of US10417562B2

2019-09-17 Application granted

2019-10-15 Application status is Active

2038-01-01 Adjusted expiration

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using a neural network system that includes a batch normalization layer. One of the methods includes receiving a respective first layer output for each training example in the batch; computing a plurality of normalization statistics for the batch from the first layer outputs; normalizing each component of each first layer output using the normalization statistics to generate a respective normalized layer output for each training example in the batch; generating a respective batch normalization layer output for each of the training examples from the normalized layer outputs; and providing the batch normalization layer output as an input to the second neural

https://patents.google.com/patent/US20160217368A1/en

So, recently I was interviewed for the position of Data Scientist The interview went into two stages with one being a telephonic round which ended in 35-40 minutes and the other being a Hangout call which ended up in 50-60 minutes. The interviewer was very good and asked a lot of amazing questions mostly focusing on the fundamentals. Here is the list of questions that were asked to me:-

1. What is overfitting? Describe how models actually overfit using a scenario.
2. What is gradient descent? Difference between gradient descent and backpropagation?
3. Is the gradient a vector or a scaler?
4. Bias-Variance Tradeoff
5. Working of LDA using an example.
6. How Infersent generates sentence embedding (Working of the entire architecture).
7. How would you do NER from scratch?
8. In AllenNLP, one of the models which it uses to do NER is based on ELMO. Given a piece of text (say, “Jack is playing football), how would ELMO go on about doing tagging Jack to PER?
9. Given a piece of text (say, “Jack and Mary had been married for a long time but gradually drifted apart until they separated.”) how would you do relation extraction from scratch? The outcome should be: Jack – Married_To – Mary

Other questions from my previous interviews:-

1. Describe the sequential minimal optimization(SMO) algorithm.
2. Suppose there are four persons, each one is standing at the corner of a square table. The probability of any one of them moving in either direction (clockwise/anticlockwise) is 1/2. If all of them started moving together at the same time at the same speed, what is the probability that none of them will collide?

General Questions:-

1. Recent trends in NLP
2. Data structures – coding questions

I hope this helps anyone who is preparing for there interviews. I will keep on updating this, meanwhile, I also request others to please do share your interview experience and put forward some questions which you faced in your interview.

Cheers!!