MIT Introduction to Deep Learning | 6.S191
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- čas přidán 19. 05. 2024
- MIT Introduction to Deep Learning 6.S191: Lecture 1
New 2024 Edition
Foundations of Deep Learning
Lecturer: Alexander Amini
For all lectures, slides, and lab materials: introtodeeplearning.com/
Lecture Outline
0:00 - Introduction
7:25 - Course information
13:37 - Why deep learning?
17:20 - The perceptron
24:30 - Perceptron example
31;16 - From perceptrons to neural networks
37:51 - Applying neural networks
41:12 - Loss functions
44:22 - Training and gradient descent
49:52 - Backpropagation
54:57 - Setting the learning rate
58:54 - Batched gradient descent
1:02:28 - Regularization: dropout and early stopping
1:08:47 - Summary
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After being in college for 4 years and dealing with loads of professors, I can hands down say this guy is the best lecturer I've ever seen! Explains tough concepts so well.
I usually find neural networks challenging to grasp until I watched this lecture. I truly appreciate how you simplified the concept for me.
Finally I can follow live lectures
since you strongly pointed that out, what are these big advantages over offline lectures that you're so in favor of?
@@webgpu ofline lectures? I guess you meant to say, "What are the advantages of following live online lectures over recorded online lectures? Did I get the question correctly?
@@page002 sorry I most probably was not able to express myself properly. I meant "what are the advantages of the [opposite of live] lectures - so I think that's what you also meant in your past comment 👍
@@webgpu don't worry. So here's my point -
I prefer Live Recorded lectures over only recorded lectures because when we follow live I think we can connect more with the instructors. Also it gives us the impression that we are also a part of it which a recorded and already published can never give(at least that's what I think).
And last but not the least if we follow the live (recorded) lectures here we will have a clear goal and a Dateline to follow. And I think that's a great thing.
So, any day I prefer Live Recorded lectures or Live lectures if possible over recorded lectures specially for technical things and programming.
I am a pretty bad communicator so, I hope you got your answer even a little. BTW, if you don't mind, try to follow Live lectures once I think you will be able to see the difference personally.
Happy Learning
Both theory and actual implementation in industry code! Perfect! Also, great pacing and depth!
After 5 minutes in one episode, and i can already tell this is the best beginner ai lecture series I have seen!
It's wonderful to see universities of the calliber of MIT making education accessible to everyone for free. Thanks MIT!!
This is not for beginners. Having 3+ years of experience in deep learning i found it interesting on how much information is shoved into 1 single video . Note that each concept is very vast if we dig deeper
could you link some real beginner information so i can understand this course?
There is a playlist in CZcams names 100 days of deep learning by campusx. You can find everything in deep
U know where we can find some real number training example of using a basic liquid neural network ?
this 1 video covered an entire semester worth of deep learning course of my college
@@adityaverma1298 you mean this video series right?
Sir's explanation is better than any Udemy and Coursera course out there fr😮
Yesterday we started system identification using neural network, I watched your lecture and now I feel quite comfortable using the concept of deep learning. Thank you Sir and love from Pakistan....
I've been following these MIT Deep Learning lectures since 2019. I've learned so much. Thank you, Alexander and Ava.
So do I need to watch all previous lectures too? Or are the ones in this 2024 course enough?
@@lakshyajain6765 don't need to since every semester course is self contained unit. This is not created for CZcams, it's for MIT students and every semester there is new batch.
@@user-xn5do6xc1u Thanks a lot!!! Do you have any other resources on MIT ML lectures for their students? this is my alt acc
@@user-xn5do6xc1u Thanks a LOT!!! this is my alt. Do you have any idea on some more MIT ML related lectures. I would like to do some research in this field and try to get into a phd program
@@user-xn5do6xc1uWhere can I find the next part?
Hands down, this is the best low level explanation of deep neural networks I have seen so far.
It's not low level... It's High level like programming languages.
@@HeyMr.OO7 What do you mean by low level in your definition? It is as low level as you can get in this field that you can perform calculations on an entire network by hands without having to rely on computers, not to mention programming languages or libraries. Some data scientists or self-taught professionals I have talked to who are fluent in machine learning tools which are considered high levels do not quite completely understand this low level fundamental and I doubt if they could hand calculate an entire network from scratch.
@@paultvshow alright man ! Now, Go get some air !
@@HeyMr.OO7Stop it and get some help if you can’t even reason. You don’t even know what level means lol.
@@paultvshow God bless your brain man ! Now leave 😅😅
YahoooOoo!! Another great season ahead!
Thanks for the videos and the slides, they are great assets for students and teachers.
I wish that you have explained more about back propagation with a numerical example, and the different activation functions we can use in the last layer for the different classification problem, like binary classifications multi-class classification and regression problems
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 :)
I am a high school student and I am currently self-studying deep learning and I find it very helpful.
I hope one day I can attend your lectures in person.
Thank you very much.
Always be your big fan, really excellent teachings. These are the ones I'd love to go through again and again!
Thanks for the lecture, please please make a video or provide a pdf of MATH too, I wanna know the math behind deep learning, svms, pca, ML in general aka grad descent etc, how then that changes when many layers are involved (as in deep learning) so basically
normal ML -> i/p -> mat mul -> o/p
deep learning -> i/p -> mat mul = linear x matrix . non linear x matrix . linear or non linear x matrix ..... -> o/p
etc etc etc I mean try and simplify what goes on mathematically then also give enough formalization that some of us can begin to understand a few of the key ML papers on Arxiv. This has been our biggest challenge truly.
Every year I'm here, you remain the best
Great presentation, thanks for always simplifying these concepts to the understanding of all.
Thanks for the session, Alex.
was waiting from last December. Thnak you
I loved this, It's my major course......It's extremely helpful...love from Bangladesh
This is the amazing work. Thank you for sharing.
Looking forward to this! 🙏🏻
what not to love in things that seem good and are about to happen ?
This is one the best lecture series for deep learning out there... keep up the good work!!!! Will there be any lecture on the lab assignment - on how do you configure your tensorflow on Google Colab for the assignement/project? I believe that it would be idea/good if there is some lecture video to show how do you configure the Tensorflow on Google Colab. Thank you.
Your way of explaining is like movie screenplay or storytelling we are totally into the world you created.
WOW!!!!😍My professor is chinese and I know he knows a alot of things but after watching this teacher teachin, I understood the importance of a good presentation and most importantly, what a good presentation look like.
Absolutely amazing. Great to be here.
i am also very happy that i am really right here where i am now.
I was waiting for this for so long ❤
Awesome course !! Can't wait to complete it 😁
Amazing lecture. I can't thank you enough!
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!
You make it so understandable
You are a great teacher. I wish my professor explained this way. 🎉
Amini genius is back!!
Looking forward 😃
A big Thank you to you for this great course
Hello Alex 😊😊 Thank you so much ❤❤
I loved this session! I am getting interested in it.
These lectures are goated 🔥
Love your style!
Excellent lecture! Was wondering, when would the next lecture (in the same series of year 2024) be coming out? :D
good Presentation agood overview about deep learning thanks sir Alexander Amini
Hi dear, Thanks for the course. Like always informative and to the fundamentals of DNN.
Great work thank you❤
Finally the wait over 😊
Thank you!
Thank You Sir
If I were just starting to learn deep learning, I would start with this video
Game changer lecture is stating.
You teach fabulous
Amazing for free lectures ❤
Sir you are doing a great job, I am student of BSCS, last year from Pakistan. But being a student to learn Deep Learning from last 2 year, I am still a beginner, as the system is not very modern. This lecture seems like a new start for me, which feels very promising. Can you please share the other lectures, so I (students like me) can really advance in this field, and maybe start working at MIT someday. Thanks for teaching in such a beutifull way.
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.
Genial, saludos desde Chile.
sir you don't know how much i needed this! i am begining to start my research very soon, is there anythingyou recommend to get started with dl ?
Finally i looked your session
Next session pls came
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)...🖖
Amazing, top content! Out of curiosity: Why TensorFlow instead of Pytorch?
Great video! Where can I watch the software lab lessons? And will the first lab Intro to TensorFlow/Music Generation be available this week? Thank you!
I am waiting to know what's next in that amazing field.
The mathematics which I studied this semester is completely making sense now.
Let's gooo!!!
Every year I look forward to this!
i too love to expect things that occur periodically to happen the next time!
my favorite youtuber just dropped a new episode!
ah that moment when someone who produces good content, produces good content!
amazing video
yea buddy!!
NEW SEASON BOYS
Awesome!
I love it
Thanks!
Wao Amazing And cool live labs
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
What is the prerequisites one must know before diving into this lecture?
At 22:45 you mention the ReLU function has a discontinuity at '0', IIUC this is not true, ReLU is a continuous function, even at '0'. It is however not differentiable at '0'.
28:24 This is a very basic idea of deeplearning. I should have watch these lectures before I started my computer vision courses.
As a society we should be open sourcing education it’s a net + no matter what
TOP G
So basically what Meta with Llama 3 has done is give to the community the weights for each perceptron?
I am in a bit of a fix, hope someone in the comments or the publishers of the series can shed some light.
I am a professional embedded software engineer. I have been fiddling with the idea to develop expertise in embedded AI and thus looking for an inroad, lets call it that. Now i know there are options like TinyML(TF lite) , edge impulse and other frameworks where maybe i could jump in.
The question is, is this a good way or is it more suggested to take up an uni/grad level curriculum like this one?
I guess something that brings me to hands on implementation at the embedded level relatively quickly will be very enticing.
Great lecture, but I wonder why u guys take out the robotic learning from your curriculum ?
will the lab class be available in the youtube too?
Is there any group to follow with other peers? Has anyone made a link?
None yet, but you could start one 😊
If yiou made one, I'll join, if not, I have a Telegram one.
@@mihaidanielbeuca1083 what's the Telegram link?
Please if you send here the Link please
Can anyone tell me,
Are there other videos that are going to be premiered for this 2024 course, or is this the only video we get a chance to watch?
Love 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?
At 46 min. Where does the initial loss landscape came from?
Can general public have access to projects and other course materials.
1 week course he said ?
Then schedule link says - 1 every week. Whats right ?
0:51 How about inventing a microphone that does not suck?
Where can we find the labs? I cant find them on the website
🎉🎉
Can absolute beginners follow these lectures???
Please can anyone tell me, i am beginner and self learning these, can i do it or its too advance???!
Nice lecture ! How can we access labs
link in the syllabus
where we can find slides?
Only a few minutes left ;D.
Nigeria on 6S191
Is this all free including labs ? Or we are only getting this 1st lecture free ?
100% of all content is released online for free :)
What is an iteration?
Yeeehawww