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!!

All object detection approaches I know are trained with some data sets to detect only specific classes from the respective data set (also e.g. something like YOLO9000 which can detect 9000 classes). I want to have some more general approach, that can detect arbitrary objects (of any class, returning the containing bounding box) in images.

I did not found anything here, can anyone recommend a related paper or website?

Hello all. I want to know whether this seems like a good idea. I actually am looking for ideas for my Business Plan class and this seems to be like something that hasn’t been done yet.

Basically, the idea is that people can upload their data online with instructions on how to label it. The label can be as simple as assigning a label to the whole picture and it can also be as complicated as marking individual pixels or something like that. I don’t really plan on going too deep into this since it is only for academic reasons but feedback on this would be appreciated.

I think the recent boom in the usage of neural networks means there will be a big market for this idea as everyone from independent machine learning enthusiasts to big corporations would take advantage of this. What do you guys think?

According to article in The Register:

A Netflix spokesperson confirmed to The Register it wasn’t working with Raval, and the ESA has cancelled the whole workshop altogether.

“The situation is as it is. The workshop is cancelled, and that’s all,” Guillaume Belanger, an astrophysicist and the INTEGRAL Science Operations Coordinator at the ESA, told The Register on Monday.

Raval isn’t about to quit his work any time soon, however. He promised students who graduated from his course that they would be referred to recruiters at Nvidia, Intel, Google and Amazon for engineering positions, or matched with a startup co-founder or a consulting client.

In an unlisted YouTube video recorded live for his students discussing week eight of his course, and seen by El Reg, he read out a question posed to him: “Will your referrals hold any value now?”

“Um, yeah they’re going to hold value. I don’t see why they wouldn’t. I mean, yes, some people on Twitter were angry but that has nothing to do with… I mean… I’ve also had tons of support, you know. I’ve had tons of support from people, who, uh, you know, support me, who work at these companies.

He continues to justify his actions:

“Public figures called me in private to remind me that this happens. You know, people make mistakes. You just have to keep going. They’re basically just telling me to not to stop. Of course, you make mistakes but you just keep going,” he claimed.

When The Register asked Raval for comment, he responded:

I’ve hardly taken any time off to relax since I first started my YouTube channel almost four years ago. And despite the enormous amount of work it takes to release two high quality videos a week for my audience, I progressively started to take on multiple other projects simultaneously by myself – a book, a docu-series, podcasts, YouTube videos, the course, the school of AI. Basically, these past few weeks, I’ve been experiencing a burnout unlike anything I’ve felt before. As a result, all of my output has been subpar.

I made the [neural qubits] video and paper in one week. I remember wishing I had three to six months to really dive into quantum machine-learning and make something awesome, but telling myself I couldn’t take that long as it would hinder my other projects. I plagiarized large chunks of the paper to meet my self-imposed one-week deadline. The associated video with animations took a lot more work to make. I didn’t expect the paper to be cited as serious research, I considered it an additional reading resource for people who enjoyed the associated video to learn more about quantum machine learning. If I had a second chance, I’d definitely take way more time to write the paper, and in my own words.

I’ve given refunds to every student who’s asked so far, and the majority of students are still enrolled in the course. There are many happy students, they’re just not as vocal on social media. We’re on week 8 of 10 of my course, fully committed to student success.

“And, no, I haven’t plagiarized research for any other paper,” he added.

https://www.theregister.co.uk/2019/10/14/ravel_ai_youtube/

I hated the guy. I fucking despised him, between his “AI for Supply Chain” video, which made a mockery of one the most challenging domains in ML and Analytics, and his “How to predict the stock market with LSTM” video which downright dangerous (what if somebody actually went out and bet their retirement on 7 lines of Keras?!?!?!?) – I found the guy not just annoying, but pathological.

Yet over the last few days, the sheer completeness of his collapse, has me actually feeling sorry for him. Some of the time he was indeed intentionally malicious, but most of the time he came of more like he a D-bag who just way in over his head and who somehow found himself in a spotlight he didn’t expect.

I mean I know people who are at their core, just refined, more nuanced version of Siraj Raval, who managed to make it to Director and VP level positions on pure self promotions and ability to sell themselves. I am sure we all know people like that as well. What’s the difference? They were simply smart enough to not get caught.

Yet we gawk and we laugh at poor Siraj. Its every started chewing when the guy was pulled over for doing 5 m over the speed limit, and they’re all ignoring all the cars doing 85 and 90…..

Hi,

We’ve recently worked on imperfect information games and reinforcement learning, and we would like to share our toolkit to everyone. RLCard supports various popular card games such as UNO, blackjack, Leduc Hold’em and Texas Hold’em. It also has some examples of basic reinforcement learning algorithms, such as Deep Q-learning, Neural Fictitious Self-Play (NFSP) and Counter Factual Regret Minimization (CFR). Also, it has a simple interface to play with the pre-trained agent. Any generous comments will be appreciated. Have fun!

Github: https://github.com/datamllab/rlcard

Hello everyone!

I was wondering if anyone here has gone through the AI resident interview circuit at X? If yes, could you please provide an overview of what the interview structure is and how many rounds are there in total?

Thanks!