Awesome lecture. 🎉

this is an "intro" class, however it is attended by experts 😅

Would you get in touch with me?

Thanks for the video, but statquest did better video on this topic with more details.

I have a dataset of 120 images of cell phone photographs of the skin of dogs sick with 12 types of skin diseases, with a distribution of 10 images for each dog. What type of Generative Adversarial Network (GAN) is most suitable to increase my dataset with quality and be able to train my DL model? DcGAN, ACGAN, StyleGAN3, CGAN?

if tf.keras.layer.Dense(units = 2) does not work then try this import tensorflow as tf from tensorflow.python.keras.layers import Dense layer = Dense(units = 2)

can you please share the Lab session or codes as well to try out?

Nice amini teaching❤ and your curly hair nice😮

Thank you so much for the course. So much interesting.

Who's here for the curly hair lady 🥰 ?

5 mins more let's gooooo

awesome, many thanks for your initiative ! keep up the great work

It's finally out!! 🤗🤗

Please send me a degree certificate to get a job in US. Some degree certificate is okay. God bless USA. Or great Britain. All AI only. Some degree tag is enough. I ask because Bill gates really needs a degree for a job offer for me.

Thank you!

when gpt 4o lectures :D

TOP G

so excited for this!

what an awesome lecture, thank you!

I'm confused, is the 2024 version a short form of this course? because you mentioned its just 1 week or am I missing something?

Excellent video! just a minor comment: about 27:00 i think you should state clear that (1+3x1-2x2) = z and include the "hat" to y (in the graph)...🖖

🔥

Sir's explanation is better than any Udemy and Coursera course out there fr😮

So basically what Meta with Llama 3 has done is give to the community the weights for each perceptron?

Dear Alex and Ava, Thank you so much for the insightful sessions on deep learning which are the best I've come across in youtube. I've a query and would appreciate a response from you. In case if we want to translate a sentence from English to French and if we use an encoder decoder transformer architecture, based on the context vector generated from encoder, the decoder predicts the translated word one by one. My question is, for the logits generated by decoder output, does the transformer model provides weightage for all words available in French. For e.g. if we consider that there are N number of words in French, and if softmax function is applied to the logits generated by decoder, does softmax predicts the probability percentage for all those N number of words.

Hey if possible please upload how you implement this things practically in labs. Theory is important so does practical work

I loved this, It's my major course......It's extremely helpful...love from Bangladesh

Why Tensorflow, why not Pytorch?🥲

How can we be sure that our predicted output vector will always correspond to a word? There are an infinite number of vectors in any vector space but only a finite number of words in the dictionary. We can always compute the training loss as long as every word is mapped to a vector, but what use is the resulting callibrated model if its predictions will not necessarily correspond to a word?

thank for sharing that course , that's so usefull !

how many clocks can go off in a school

look for the countdowns, what does ai look for and if it can understand you training with it. what were the biggest fixes.

Thanks Amini- such a great introduction 🙋🏾‍♂️👌🏾

Thank you for sharing, please i need a help and i send an email to you but no response, could you please help me? thanks in advance.

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This is the amazing work. Thank you for sharing.

right?

Ojalá todo el mundo fuera así de competente. Da gusto aprender de gente que tiene las ideas claras.

Thank you very much, it is a great lecture. I hope that you develop the lectures over the years as it seems to be the same contents. topics like pretrained models and knowledge transfer, YOLO might be good to be added to CNN

Thank you for sharing this wonderfully put together course for the general public's benefit. I would love to get some insight as to what goes in the lab work the student's go through as an adjunct to the course lectures. Will that be possible in the future.

excellent so great idris italy

Amazing, top content! Out of curiosity: Why TensorFlow instead of Pytorch?

🔥🔥🔥

Finally i looked your session Next session pls came

You make it so understandable

51:52 Position Encoding - isn't this just the same as giving everything a number/timestep? but with a different name (order,sequence,time,etc) ,so we're still kinda stuck with discrete steps. If everything is coded by position in a stream of data wont parts at the end of the stream be further and further away in a space from the beginning. So if a long sentence started with a pronoun but then ended with a noun the pronoun representing the noun would be harder and harder to relate the two: 'it woke me early this morning, time to walk the cat'

Thanks for sharing this knowledge. Be blessed

Attended Deep Learning lectures at a topmost college of a country, here he clearly explained all that in a single lecture for which the former took 10s of lectures to explain.

Thank you for sharing quality content like this for free for several years

Where is the software lab?