It would have been complete if Chris olah also joined them

stop cheerleading and code yourself

I've checked a couple of time the youtube settings because I thought the video was accelerated :|

Yes, the sign of a brain limited by the bandwidth of speech. lol

Haha, it sounds much better 0.75 :'D

hahaha, show and gone

Thank you. I was watching at 2x speed before lol

Thats an amazing reply #13:02

Well said bro!

Truth has been spoken :)

Ayushman, if you've learnt the art, start transferring it.

Are you still in this field?

+Aditya Soni "..Cooncretely, Hansel has put all the pebbles in his pocket in a way... well, you really don't need to know all of the details of how did he do it to understand the rest o the story, the important thing for you to understand was that he had pebbles in his pocket..."

It's amazing how we can perceive honesty/passion and how we can resonate with it. Thank you Andrew and thank you Andrej!

Women who like ML are hot 🤩

Benito Teehankee this question is the best part of the entire interview to me. Good question is half of the answer. Digging into it.... Very interesting..

In case you have not yet figured this out: I skimmed over the CTC paper, cited by tensorflow, for a minute. Are you talking about how CTC works as a whole or only about how the cost/loss is calculated in the softmax (output) layer, as in how the loss function works for this classification algo? I can give some pointers on what I understood about the latter. My explanation might be either naive or complicated, depending on how deeply you understand ML. CTC calculates the cost of an error using the principles of maximum likelihood estimation (MLE). In particular, 'minimising it [the cost function] maximises the log likelihoods of the target labellings' - as the authors say. To label the output, it uses one extra unit in the softmax layer than the number of output labels, unlike traditional methods that use as many output units as there are labels to classify. The extra unit is reserved for observing a 'blank' or 'no label' class. If my understanding is correct, this gives the algorithm some breathing room to skip over labelling the data that is does not understand correctly and save it for later (?) rather than falsely classifying it as one of the labels because it was forced to do so. Couldn't get the time to learn about beam search decoding :)

He now works for Elon (maybe he had started by then and you knew(?))

Israel Abebe they're talking about the Stanford course here cs231n.stanford.edu

The course Andrew NG was talking about is in Coursera, not Udemy, if I understand your concern correctly. This is a brand new specialization. However, the best available Machine Learning course online, in my opinion, is Andrew NG's own course titled 'Machine Learning'. It's absolutely amazing, very detailed and free. It is probably the very first online ML course. I dropped out of a grad course at the university and spent that entire semester on this course. It eased me into my grad research.

Mercedes is only level 3 on certain situations on the highway but Tesla is on the way to be highest level on any road and every situation. The computer of the Tesla's are probably more powerful than of Mercedes. But why do you mention it on a video that is 5 years old? At that time Mercedes was no where with self driving and in Tesla's it was already an early not so good version available. Now fsd beta gets every update better and is already pretty amazing how it handles heavy traffic in cities which Mercedes can't.

Why is he not good person?

R1nz0R I think it's largely because of the way he interacts with others. But I think you're mistaken there, he might come across as not a good guy when he actually is.

Reeti Garg imo he actually seems like a kind person but ok xD

Gosh I thought Andrew seems an extremely good person, watching him in this video.