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?

Interesting view regarding the kolomogorov Arnold representation. His buddies at MIT just released KAN paper, I wonder how this idea evolves.

Software Lab 1 still not made available, when will that happen?

Have any of the labs been published yet?

I loved this session! I am getting interested in it.

is this course taught to mit students as well? or is it just like an online? I liked the course but i wish there were readings attached with it, which went into the mathematical details.

Waiting patiently

just WOW! You almost summarize my learning of 4 years PhD in 1 hour. Keep it up dear. You have everything to speculate your expertise :)

Cant wait...

I really appreciate you posting this material!! Thank you 🙏

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But the lab between Lecture 2 and 3 is still not published in the website?

Waiting ..

Great lecture, thank you! When will the labs be available?

Is there any similar lessons on liquid neural network with some real number calculation ?

When lab code will be released?

Wow great 👍 job buddy i wanna your book suggestion for DSA!

Please can anyone tell me, i am beginner and self learning these, can i do it or its too advance???!

Omg everything makes sense! Your explanations were so simple and easy to understand 😭🙏

I'm a beginner in ml and ai fields and it's amazing to have these lectures online and free. I've a doubt: the neural network showed in 33:44 shouldn't be named 'multi' layer rather than 'single' layer neural network since it has an output layer separated of the hidden layer? Thanks!

Fantastic 🎉 thank you

Gemini: This lecture is about reinforcement learning, a type of machine learning where an agent learns through trial and error. The lecture starts with comparing reinforcement learning with other learning paradigms. Reinforcement learning is different from supervised learning where the agent is given labeled data. It is also different from unsupervised learning where the agent is only given unlabeled data. In reinforcement learning, the agent is given rewards for taking desired actions. The core idea of reinforcement learning is to learn a policy, which is a function that maps states to actions. The agent tries to learn a policy that maximizes the total reward it gets over time. There are two main approaches to reinforcement learning: Q-learning and policy learning. Q-learning focuses on learning a Q-function, which estimates the expected future reward for taking a particular action in a particular state. Policy learning focuses on directly learning a policy that maps states to actions. The lecture also talks about deep reinforcement learning, which combines reinforcement learning with deep learning. Deep learning allows reinforcement learning algorithms to learn complex policies from high-dimensional data. One example of deep reinforcement learning is AlphaStar, a program developed by DeepMind that can defeat professional human players in the real-time strategy game StarCraft II.

too bad, even MIT courseware gives a false definition of AI in the slide 7:40

Can absolute beginners follow these lectures???

amazing video

How do you predict the first word? Can you only start predicting after the first word has come in? Or can you assume a zero input to predict the first word?

The mathematics which I studied this semester is completely making sense now.

Great lecture as always! Can’t wait to start the software labs. Just curious why isn’t the website served over https? Is there any particular reason?

Explained so well, hopefully I will get more video to watch.... Can somebody suggest me to find best free material (video) like this video for AI, I desperately want to make my career in field of data science and AI

Can't believe how amazingly the two lecturers squeeze so much content and explain with such clarity in an hour! Would be great if you published the lab with the preceding lecture coz the lecture ended setting up the mood for the lab haha. But not complaining, thanks again for such amazing stuffs!

good Presentation agood overview about deep learning thanks sir Alexander Amini

You are a great teacher. I wish my professor explained this way. 🎉

Love your style!

Miss was stressed if she made the presentation complex

Is Lex up for another participation as lecturer ? Is he still working on this topic? He's such a great speaker.

Mark it, learn later

Initially, N-gram statistical models were commonly used for language processing. This was followed by vanilla neural networks, which were popular but not enough. The popularity then shifted to RNN and its variants, despite their own limitations discussed in the video. Currently, the transformer architecture is in use and has made a significant impact. This is evident in applications such as ChatGPT, Gemini, and other Language Models. I look forward to seeing more advanced models and their applications in the future.

Was waiting for it from the last one last week, Amazing ! Please i have send you an email asking for some quires, could you let me know how can i get the answers or if there is any channel to connect? thanks in advance

This was an extraordinary explanation of Transformers!

Ava is such a talented teacher. (And Alex, too, of course.)

That was explained very well! Thanks a lot Ava

thanks daddy

I now feel like a fully connected neural network bye myself now because I've watched hundreds of videos at night that concern deep learning. Best regards from Berlin!

I'm sitting here in wonderful Berlin at the beginning of May and looking at this incredibly clear presentation! Wunderbar! And thank you very much for the clarity of your logic!

Amazing for free lectures ❤

Another extraordinary lecture FULL of refreshing insights. Thank you, Alex and Ava.