Neural networks [1.1] : Feedforward neural network - artificial neuron

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Hugo Larochelle

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Great set of videos. Its taken me three days to watch them all. Thanks very much Hugo for sharing them with the world.

frogai

This video series on neural networks is excellent. Thank you so much.

jm

Thank you very much for this series, you're helping me greatly in understanding neural networking methods.

SebastianSkadisson

Thank you for delivering this whole series in english =D

mikechung

It is exactly what i'm looking for .. Thank you very much Hugo

lylylabylka

Thanks for the simple yet informative explanation. I decided to make this series my intro to neural networks. Keep it up.

O.Salah

Thanks for your useful course. That's what I need.

luannguyenthien

Sir, your course on Deep Learning is truly amazing. Regarding the minimization approach that you discussed, I have been thinking of a different methodology. If possible, would like to discuss.

rahulagarwal

These videos are very awesome! The only thing I could wish for would be a list of references in the video description if I wanted to get more in depth about certain topics.

silentsnooc

This series seems to be really helpful, thnx Hugo

ChaitanyaChandra

thanks for sharing your knowledge awesome

diya

This series is awesome, Hugo! You're super brilliant!

JaysonSunshine

Hi, Hugo. I am watching your videos. They are great!

yunxiangli

Very recommendable! E.g. great explanation of the bias

bingeltube

Please i want to know how to choose the number of neurons in the hidden layer if i have for example input matrix with 9*981 and target matrix with 2*981 and if i want to improve the performance of my neural network what should i do because i thought that if i have large dataset for the training it will work but unfortunatly the accuracy in the confusion matrix was 69.5% .So any advice please ?

afefsaidi

I am finding it difficult to understand Fast and Faster RCNN, emailed professors from IIT madras and IIT Kgp, no response, emailed Ross Girshick in all his addresses, no response, can you help me out?

nilarunm

I've seen a lot of videos that explain how a ANN work but none that explain why. Does someone can reference a paper or something that helps me with that?

bio

hey dude, i dont udnerstand the second slide, is the input X a vector thats being multiplied by a weight vector? isnt the W supposed to be a real value? vector multiplication result in dot product? thanks!

chimchikachim

there are so many "styles" to express neural net in mathematical notation, for example michael nielson defines w_jk as weight from k-th neuron to j-th andrew ng defines it otherwise, some people treat bias term as neuron with fixed weight equal to 0, it is not big deal i guess, but when i derive equation from different notation style its make different equation (however the both equations has same meaning), and suppose that i want to write bachelor thesis or something, what is the most common mathematical notation style to express neural net ? is there any standard to write to represent neural net in mathemaical notation...thank you

kikirizki

Great lecture!
I do't understand why W has to be perpendicular tu the ridge. Could you please give me a hint?

alexandra-stefaniamoloiu

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