Thank you for being a protagonist of open education. These videos help a lot
@GeorgeZoto4 жыл бұрын
The legend of Deep Learning! Thank you Professor Andrew Ng for sharing your light with the world 🕯️and for teaching us this awesome new field 😀Forever grateful!
@alireza1735 жыл бұрын
You’re a fantastic teacher. Thank you
@cipherxen23 жыл бұрын
If first two words were "Andrew" and "Ng" next two words will be "is", "best".
@blairt8101Ай бұрын
explained extremely well!
@zhuoerlyu47054 жыл бұрын
So helpful, thank you
@ze24113 жыл бұрын
Andrew Ng is the ML G.O.A.T!
@siloquant6 жыл бұрын
Best!
@wolfisraging6 жыл бұрын
u r best
@mohomedarshad72525 жыл бұрын
Hi, Thank you for your effort. I find your videos and explanations very instructive and detailed. But I was wondering if you could make a video about tree-to-string machine translation using tree transducers. It is something that I can't quite capture yet.
@danny-bw8tu6 жыл бұрын
thank you!
@jeonghwankim89736 жыл бұрын
It was an awesome video sir. I just have one question. If we instantiate the network for each highest probable word sequences, does it mean we should use separate GPUs to run the instantiated networks? Will it take more time if I use a single GPU? Just out of curiosity.
@jismonj15 жыл бұрын
can u please help me in local beam search coding on unity
@Kareem-hl8hj5 жыл бұрын
Thank you
@sandipansarkar92113 жыл бұрын
quite tough
@user-vm7we6bm7x4 жыл бұрын
Nice accent and vid
@PRATEEK301119895 жыл бұрын
what is the computation complexity of the above method? is the beam width only used in the first iteration?
@robertbracco83215 жыл бұрын
The beam width is used in every iteration. At each stage we evaluate every possibility for the 3 beams we carried over from the last stage (this produces 30,000 new possibilities in the example) and then we reduce it to just 3 (our beam width) before moving on to the next step. It looks like the computation complexity for a search of a sequence of k words from a dictionary of n possible words, with a beam width b would be as follows. n steps for the first word of the sequence. then b*n for the additional k-1 steps in the sequence, giving n+(b*n*(k-1)), which for simplicities sake could just be considered b*n*k. For a sequence of 10 words, a dictionary with 10,000 words, and a beam size of 3, it would take 3*10*10000, or 300,000 operations. Beam is just a reduction of breadth-first-search, so if the beam were infinite, it would be identical to BFS. The complexity of BFS would be the size of the dictionary to the power of the size of the sequence (10000^10 in our example). For even modest dictionary sizes and sequence lengths this quickly becomes infeasible so that's why we need beam search to narrow the possibility while still giving us a high likelihood of finding the optimal result.
@shankarbhaskaran57785 жыл бұрын
One question on this one , If we increase beam width then can September come as a candidate in the first 3 words.May be the African sentence was literally translated as "September is the best time for Jane to visit Africa"
@samuelbarham84833 жыл бұрын
Sure! The beam search candidate sequences he used in his example were just that -- examples. I don't believe they were taken from a real neural network. They were merely meant intuitively to motivate the algorithm.
@koolcoy5 жыл бұрын
During decoding process, could beam search be replaced with something like MCTS ?
@yatinarora96504 жыл бұрын
Can you please help me out with beam search,As am not getting how can implement using keras
@raghadalqobali9680 Жыл бұрын
how's the algorithm with the memory usage? is it better than greedy?
@tengpan98473 жыл бұрын
is there any different about training?
@jismonj15 жыл бұрын
can u please help me in local beam search coding on unity
@yatinarora96504 жыл бұрын
did you get the solution for beam search ?
@kutilkol2 жыл бұрын
is getting a decent microphone an issue these days in tech community or wtf
@astitva50026 ай бұрын
idk man gives it a very raw feel it's kinda nice to my ears