Відео

great stuff, do we still need egen decomposition to get lambda_max ?

ممنونم استاد خیلی عالی بود

Thank you so much for sharing your amazing course!

Thanku sir for explaining this course in easy way Now I have started enjoying the course❤

Thank you professor. This lecture explains exactly what I was looking for - why principal components are the eigenvectors of the sample covariance matrix.

I got answers to almost every"WHY?" that I had while reading books.

Amazing Lectures

Immediately after explaining the importance of centering the data, he purposely neglects this in his first demo! But then he mentions this at 22:38, and his second demo he does center the data. Great instructions, thanks!

Ye lecture kis bachhe ke liye h

I am myself one of the person which you are claiming to be GAN generated. Please don't misled people.

Thank you thank you!

best video oh PCA

That was great Dr. Ghodsi! Many thanks!

I love this lecture! Finally I have a better overview of Transformers! Thank you so much prof!

Much more clear than my professor.

How can we add x(d by 1)and z(m by 1) when their shapes are different? 🙄

what is the guarantee that u1 sigma1 v1 are non negative if A is non negative?

wrong derivation of derivative of st w.r.t w , its a recursive equation , since s implicitly depends on w

excellent !

رائع جدا ..الله يعطيه العافيه ويسعده ويوفقه ويوفق عياله ان كان عنده عيال

Truly Enjoyed! Thanks

Prof. You used to be very verbose and invasive on the board. Why this hybrid mode of ppt and some board? Love from Pakistan!

Thank you for this informative lecture, I have a question though. Why the expectaion of epsilon^2 with the normal distribution of mean zero is going to be sigma^2?

subtle mistake for perceptron learning : we dont update gradient for correct classified point , the update happens only on mis classified point .

I think the proff wrote the wrong formula for marginal P(x=x0).

c'est un grand

thank you, professor! The explanation is very intuitive.

Great lecture on why we use testing errors for true error estimation

Thanks very much

Thanks for the useful course

Great, Thanks a lot

What is the "intuition" behind using Chebyshev polynomials in GNN rather than other orthonormal basis functions? Why it works well?

Are the slides available?

Thanks for sharing this amazing information ❤❤❤❤

Detail mathematical formula explanation start @47:00

thank you

Thank you, very helpful and informative

Amazing lecture! Thanks for uploading the videos! Is there anyway we can have access to the coding assignments?

Variational Autoencoder: 24:52

1000000 Thanks ❤

I'm so excited to finally see a Diffusion models lecture by Professor Ali. Thank you.

Supervise PCA start @57:19

Amazing, this topic is new in the professor's course. It would be awesome to have an only RL course from professor Ali. Also looking forward to seeing amazing diffusion models lectures.

5:35 Why would it be one row of W prime? In the first case, we got a column vector of W because we multiplied a one-hot encoded vector with W. However, multiplying with h should not give one particular row as h is not a one-hot encoded vector.

🎯 Key Takeaways for quick navigation: 00:07 📚 *Introduction to GPT and BERT* - GPT and BERT are both Transformer-based models. - GPT stands for Generative Pre-Trained Transformer, while BERT stands for Bidirectional Encoder Representations from Transformers. 05:26 🧠 *How BERT Works* - BERT is a stack of encoders with multiple layers and attention heads. - It is trained by masking words in sentences and predicting the masked words, making it bidirectional in nature. 10:17 🏭 *Applications of BERT* - BERT can be used in various applications by fine-tuning its pretrained model. - It's especially useful for tasks like sentiment analysis and can handle domain-specific tasks. 14:55 🧬 *Domain-Specific BERT Models* - There are domain-specific BERT models trained for specific fields like bioinformatics and finance. - These models can be used in applications within their respective domains. 25:09 📝 *Introduction to GPT* - GPT is a stack of decoder layers, where each decoder is similar to the transformer decoder but without cross-attention. - GPT is trained to predict the next word in a sequence. 29:48 🚀 *GPT's Evolution* - GPT models have evolved over time, with each version becoming larger and more powerful in terms of parameters. - GPT-4, for instance, has an enormous 175 billion parameters, making it highly capable in natural language understanding and generation. 30:28 🧠 *Introduction to GPT 4 and its size* - Introduction to GPT 4 and its undisclosed size. - Speculation on the impact of model size on performance. 34:04 🌐 *T5: Combining BERT and GPT* - T5 is a combination of BERT and GPT. - Transformation of various NLP problems into text-to-text format. - The application of T5 to a wide range of NLP tasks. 44:12 🔐 *Challenges in Aligning Language Models with User Intent* - The challenge of aligning language models with user instructions. - The importance of alignment for ethical and practical reasons. - The need to avoid harmful or offensive responses. 49:30 🎯 *Steps for Alignment and Reinforcement Learning* - Overview of the three steps for alignment: Supervised, Fine-Tuning, and Reinforcement Learning. - Introduction to reinforcement learning from human feedback. - The importance of understanding reinforcement learning for alignment. Made with HARPA AI

It is simple sir please accept my humble request 🙏

Kindly help me sir i am failing miserably

Thanks in advanced.so that can replace actual data to get required predication i think it is very easy for a person who has knowledge of CNN/NN THANKS SIR. say example, I have lid driven cavity problem I get velocities u and v of bith sizes are 2 dimensional (say 33 by 33 example) , it is time dependent so I want to use cnn to predict u for t=25 providing u for t= 10,15,20. And I will give actual u at t=25 for comparison and add statistical regressions , loss gain, training plots. Thanks sir. Please kindly help and I would be grateful and appreciative for kindness and support.

any lnks to the slides in this course ?

Thank you for a new 2023 course! You are one of the best to teach the subject!!!