PyTorch Lecture 13: RNN 2 - Classification

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Please keep up the good work! Can't wait for Lecture 14! Much appreciated for what you are doing here, Sung Kim! Thanks!

lesliechiang

Very high quality in depth explantation and implementation

AdityaSingh-xctr

Thank you for great lecture series on Deep Learning using Pytorch.
Best concept clearing lectures I've ever seen on neural networks. Great work Sung Kim

manishnarnaware

모든 걸 감사드립니다. thank you for everything

williamkyburz

Thank you for the great lectures in English !

aa-xnhc

I have to say, 감사합니다 Sung, the whole course is amazing and very helpful!

MW-vgdn

Thank you for your wonderful lectures. The last slide of lecture13 mentions about lecture14 but I didn't find the link to that video in this playlist, is there any link to that video?

shivashankar_

Greetings sir and congrats for the great work!
Looking forward for lecture 14!

alexandrughiurutan

Awesome!!.. Really hard to decrypt PyTorch. Your videos helped a lot

sanjaykrish

Thank you! Great lectures (actually helped me greatly in my current homework!)

littlefiend

Amazing Work! Please provide more lectures!

vpertsas

thanks for the lectures, the code explanation part is superb!

jiashenglai

can you please release more video in English....your video is really helpful

saminmahmud

Is it possible that this architecture does not allow for more than one hidden layer?
If I choose multiple hidden layers I get an error message because my input size is too high (double with 2 hidden layers, triple with 3 and so on...):
line 104, in training: y_hat = y_hat.view(batch_size, n_categories)
RuntimeError: shape '[26718, 6]' is invalid for input of size 320616"

simonvalerioneumeyer

hello. what do i need to know to create my own python deep learning framework? tell me the books and courses to get knowledge for this.

aidenstill

Thanks u so much. Your tutorial is really useful for me.

luantaminh

너무 감사드립니다!! 항상 디멘젼이 헷갈렸는데 영상을 보고나서 궁금증이 다 풀렸습니다.

로쓰를 계산할때 masked_cross_entropy 를 써야한다는 점을 한가지 추가하고 싶네요. 또는 파이토치 0.3 이후부턴 CrossEntropy(ignore_index = 0) 를 할수 있고요. 감사합니다!

maoskillskid

Is embedding matrix same as the the weight matrix ??

divyajain

The hidden size is the input size and the hidden size is the output size lol

BelowIQ

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