Thank you for making this amazing fast-paced boot camp on introduction to deep learning accessible to all!

Best course I've seen in a while! Super friendly to self-teaching. Thank you!

This was an awesome and easy to follow presentation. Thank you. I have noticed that more and more professionals working in this field are some of the most lucid and eloquent speakers. Thanks again.

Well done! These are the best descriptions of overfitting and regularization I've heard/seen. Your example of testing loss makes it clear why we take checkpoints. Every topic you cover has a great thought-provoking graphic, and each example is just right for the topic.

Hello, My name is Moro and am enjoying your class from Ghana. A big thank you to all the organizers of such intellectually simulating lecture series.

Just perfect!!! Cant wait for more amazing lectures from you. Well done!!!

Hi Alex, Thanks for sharing the 2023 lectures. I've following your lectures from 2020 and these have helped me immensely in my professional career. Many thanks.

I look forward to this MIT Deep Learning series every single year. Thank you so much for making this readily available.

Great Content!Informative, consice and easy to comprehend.What a time to be alive!. Thank you Mit allowing us to watch high quality teaching.

Takeaways: • [00:09] Introduction by Alexander Amini as a course organizer of Introduction to Deep Learning at MIT, alongside Ava • [00:42] The course will cover a lot of material in just one week and provide hands-on experience with software labs • [01:04] AI and deep learning have had a huge resurgence in the past decade, with incredible successes and problem-solving ability • [01:38] The past year has been the year of generative deep learning, using deep learning to generate brand new types of data that never existed before • [02:10] Introduction video of the course played, which was synthetically generated by a deep learning algorithm • [03:26] Deep learning can be used to generate full synthetic environments to train autonomous vehicles entirely in simulation and deploy them on full-scale vehicles in the real world • [04:03] Deep learning can generate content directly from the language we speak and imagine things that have never existed before • [05:04] Deep learning can be used to generate software and algorithms that can take language prompts to train a neural network • [06:40] Intelligence is the ability to process information to inform some future decision or action, while artificial intelligence is the ability to build algorithms that can do exactly this • [07:18] Machine learning is a subset of AI, which focuses specifically on teaching machines how to process data and extract features through experiences or data • [07:44] Deep learning is a subset of machine learning, which focuses explicitly on neural networks to extract features in the data to learn and complete tasks • [08:11] The program is split between technical lectures and software labs, with updates this year in the later lectures and guest lectures from industry and academia • [09:13] Dedicated software labs throughout the week will be provided, and a project pitch competition will be held on Friday, with significant prizes for the winners. • 12:13 - The speaker explains the fundamental building block of deep learning, which is extracting and uncovering core patterns in data to use when making decisions. • 15:11 - The speaker introduces the perceptron, a single neuron that takes inputs, multiplies them by corresponding weights, adds them together, applies a non-linear activation function, and outputs a final result. • 17:00 - The speaker uses linear algebra terms to express the perceptron equation as a vector and dot product. They also introduce the sigmoid function as an example of a non-linear activation function. • 18:04 - The speaker introduces more common non-linear activation functions, including the sigmoid function and the ReLU function. They explain the importance of non-linear activation functions in deep learning. • 19:28-19:53: Real world data is highly non-linear, so models that capture those patterns need to be non-linear. Non-linear activation functions in neural networks allow for this. • 21:01-21:35: A perceptron uses three steps to get its output: multiplying inputs with weights, adding the results, and applying a non-linearity. The decision boundary can be visualized as a two-dimensional line. • 23:11-23:39: A multi-layered neural network can be built by initializing weight and bias vectors and defining forward propagation using the same three steps as the perceptron. The layers can be stacked on top of each other. • 27:02-27:55: Each node in a layer applies the same perceptron equation to different weight matrices, but the equations are fundamentally the same. • [28:52] Sequential models can be defined one layer after another to define forward propagation of information from the layer level. • [29:18] Deep neural networks are created by stacking layers on top of each other until the last layer, which is the output layer. • [29:53] A simple neural network with two inputs (number of lectures attended and hours spent on final project) is used to train the model to answer the question of whether a student will pass the class. • [30:52] The neural network has not been trained and needs a loss function to teach it when it makes mistakes. • [32:16] A loss function is a way to train the neural network to teach it when it makes mistakes. • [33:22] A loss function can be referred to as an objective function, empirical risk, or cost function. • [34:29] Different loss functions can be used for different types of outputs, such as binary cross-entropy for binary classification and mean squared error for continuous variables. • [35:32] The neural network needs to find the set of weights that minimizes the loss function averaged over the entire data set. • [37:11] The optimal weights can be found by starting at a random place in the infinite space of weights and evaluating the loss function, then computing the gradient of the loss function to find the direction of steepest descent towards the minimum loss. Introduction to computing derivatives of functions across the space of weights using the gradient, which tells the direction of the highest point. Gradient Descent algorithm involves negating the gradient and taking a step in the opposite direction to decrease loss. Gradient Descent algorithm is initiated by computing the gradient of the partial derivative with respect to the weights, updating weights in the opposite direction of the gradient. The gradient is a line that shows how the loss changes as a function of the weights, and computing it is critical to training neural networks. Back propagation is the process of computing the gradient by propagating these gradients over and over again through the network, from output to input. Challenges in optimization of neural networks include setting the learning rate, which determines how big of a step to take in the direction of the gradient. Setting the learning rate too low may converge slowly or get stuck in a local minimum, while setting it too high may overshoot and diverge from the solution. One option is to try out a bunch of learning rates and see what works best, but there are more intelligent ways to adapt to the neural network's landscape. Adaptive learning rate algorithms depend on how large the gradient is in that location and how fast the algorithm is learning. • The Labs will cover how to put all the information covered in the lecture into a single picture that defines the model at the top [47:24] • For every piece in the model, an optimizer with a learning rate needs to be defined [47:24] • Gradient descent is computationally expensive to compute over an entire dataset, so mini-batching can be used to compute gradients over a small batch of examples [48:20-50:30] • Mini-batching allows for increased gradient accuracy, quicker convergence, increased learning rate, and parallelization [50:30-51:04] • Regularization techniques, such as dropout and early stopping, can be used to prevent overfitting in neural networks [51:41-56:19] Introduction to putting all information into a single picture for defining the model and optimizing the lost landscape with a learning rate. • [48:20] The idea of batching data into mini-batches for faster and more accurate computation of gradients using a batch size of tens or hundreds of data points. • [51:41] Discussion on overfitting and the need for regularization techniques such as Dropout and early stopping to prevent the model from representing the training data more than the testing data. • [56:45] The importance of stopping training at the middle point to prevent overfitting and producing an underfit model. • [57:12] Summary of the three key points covered in the lecture: building blocks of neural networks, optimizing systems end to end, and deep sequence modeling with RNNs and Transformer architecture.

Thank you so much! This is the best explanation to deep neural networks that I could find on UKposts.

Thank you so much!!! everything is so clearly explained and I finally understood how neural network works, stay blessed. 👏

I'm doing computer vision research right now and love to watch these every new year.

The best intro to deep learning lecture I've ever heard! Thank you so much!!!

I never thought deep learning could be explained so plainly thought it had to be complex since it's called deep learning...but you did justice to this I must admit.

Greetings from New Zealand. This is amazing. Thank you so much! So excited for these!

Thank you for such incredible jobs and for making this available to everyone!

This is the first time that I have watched a course about Deep Learning. I want to say it is the best Intro for this topic, very organized and clear. I Just understanded about 75% of the content but I got what I need to know. Thank you

Alexander Amini, you're a gem! I'm taking Machine Learning course this semester and the course lecture is already finished but when I evaluate myself against course goals and how much I understand what Machine Leaning is in general, deep learning/Neural Network/ specifically I felt like I did not either attend the class or I'm not smart enough to know exactly what it does. Then, I directly ran to You tube and came across your great lecture and now I know what it is and I can apply to solve a real business world problem. I need to be honest with you guys this course lecture is really helpful and awesome to attend seriously. Indeed wonderful, easy and great takeaway of this semester for me! Thank you so much!

In my opinion, what makes a lecture either interesting or boring is not just the content of the lecture itself, but also the lecturer's approach to presenting the material. A good lecturer is one who is able to empathize with the students and present the information in a way that is easy to understand, making an effort to simplify complex concepts. This is what I believe makes a lecture truly worthwhile and enjoyable. Alexander did an outstanding job in making the lecture engaging and captivating.

Awesome lecture and really easy to digest in terms of content, speed, and taking the small moments to re-iterate or go back a bit to bring everyone up to speed. Less lingo == better for new students. Nice work

Beautiful presentation. Very clear and concise. Everything makes sense with just 1 "watch" iteration.

Thank you for the video - it is easy to understand even for not IT experts.

Amazing delivery and presentation, thank you for sharing this material with us.

Thank you very much Professor to make this lecture series open to all. Thank you very much again from India

Thanks for the great lecture , Mr. Alexander

The clearest explanation I've ever heard. Thanks!

Thanks a lot Alexander! You are doing great! So excited to watch future lectures.

This lecture is exceptional. Keep them coming!

Thank you for making this available.

Absolutely fantastic. Thank you!

Hello World!

thanks so much for your great explanation and before that for sharing the knowledge for all!

I saw this content since 2018 and I always have learnt something new. Congrats and thank you so much.

Thank you so much for making this course accessible for free. I feel so lucky today 🙏

Great video! The MIT faculty has done an exceptional job of explaining deep learning concepts in a clear and understandable manner. Their expertise and ability to break down complex ideas into simple terms is impressive. It's evident that they are passionate about educating and inspiring the next generation of AI and machine learning professionals. Thank you for sharing this informative and engaging video. It's no surprise that it has received such positive feedback from viewers. Keep up the excellent work!

@Alexander Amini. Thanks for such an amazing video. I am currently pursuing my Masters and this video came at the very right time. Thanks once again for your work and publishing the material for students like us.

That sounds very aweaomeS!!! We can see deep learing is changing our world!

Thanks for making this introduction session!

Great Content! Informative, consice and easy to comprehend. What a time to be alive!

Thank you Sir. I appreciate you from bottom of my heart for your services.

Thank you so much for a wonderful lecture. Indeed helpful to understand AI.

Very informative and precise. Thank you very much.

Thank you for this fantastic information about deep learning! It's really helpful!

I started it today. I will be continuing with you Cause currently I have started a research work on image processing. Thank You

Awesome explanation, really loving your way of teaching

Thanks I have learned a lot from your classes!

Such a well put together lecture! It was so easy to understand.

Thanks for the sharing. Very inspired

thank you so much Mr.Amini (ممنون از شما )

One of the best courses I hv ever seen, congrats

@48:03 the tape context closes with the indentation coming out, so the line grads = tape.gradient(loss, model.trainable_variables) may give an error since tape is closed after exiting the with context.

Amazing content and education. thank you.

Happy to say i knew everything that was discussed in this video! Looking forward to the next one

Thank you for your wonderful explanation.

The structure of the course 🔥

The start of my learning in this field ! Wish me luck 🍀

Wonderful way explanied. Thanks a lot

thank you Alexander and the team for this great effort. wanted to ask, what is the prerequisites for this course.

Thank so much, Alexander. It was a great of explanation.

Actual legend for making all of this (lecture + labs + lab solutions) accessible and free.

I really loved your lecture. Your lecture is so easy to understand. Thank you for posting this on UKposts

Thank you Alexander, this is quite capable fundamental lesson

Loved the class today. Is there any reading material associated to all that was covered - since lots of new concepts was out there.

Great lecture, explained every aspect and flow of dealing with NN, was Fun!

Amazing video! Thanks for sharing!

You are awesome. Thank you very much.

This is the best lecture I've seen in UKposts about deep learning.

Omg!!! The courses are awesome!!!

Your lecture is really easy to understand🔥

This is an amazing lecture!! Thank you so much!

Dear Alexander, thank you for your AI course on UKposts! It is the best among all of these on UKposts.

The amount of effort that has been put into the presentation is highly commendable.

After watching just a few UKposts videos I have a neural network running on my computer (Python), built from scratch, and no fancy libraries (except NumPy). Forward propagation, non-linear activation, backward propagation, gradient descent... maybe 50 lines of code... that's it. It was able to train itself to recognize handwritten digits (0 - 9) in a couple of minutes. I'm completely blown away - can hardly imagine what serious networks accomplish. Looking forward to this series for a deeper understanding.

Excellent lecture!!! Thank you!!!

Best explanation ever!!!! thank you

Great Explanation! Thank You very much for the knowledge.

Thank you so much. Your lecture helped me a lot.

Thank you sir, the way of your explain things mesmerizing.

Thanks to this Course and I startd my ML journey...Today I am doing ML Engineer Internship...Thank you MIT..

Bravo! This tutorial is exceptional.

You are so clear and the topic is presented so effectively - in one foul-swoop you put in plain language what I have been using CHATGPT for, so many pennies have dropped and lights went on - thank you.

Excellent. Many thanks for the very good video.

Thank you for your outstanding presentation

It's really informative and helpful for us 💙💙

Thank you Mit allowing us to watch high quality teaching

this one lecture contains everything in depth.

Always wait for your videos

Hi @Alexander Amini I am a graduated student of Master's of Computer Science and Engineering from KUET, Bangladesh. I have my thesis on Protein Secondary Structure determination by RNN (LSTM & GRU). It took me lots of time and effort to understand the basics of NN. Moreover, I have a paper published on EICT 2021 on this field. However, today as I am watching your lecture, I found you made those complex explanations very easy. I really appreciate your work. I understand I have zero knowledge on NN but if there is a chance to work with you or any way to reach you, I would be very grateful to you. Thanks. Soumen Ghosh.

Hi! It is very interesting introduction video. Now I'm working in small company in my country as DS/ML specialist. It's helping me to approve my chances to get a job in foreign country and to be part of AI world. Thanks for sharing with us!

Thanks so much for sharing materials.

This is why I like MIT. Open Source Knowledge.

I am 16 and from a rural part of South ASIA and I will be forever thankful for the resources available on UKposts thanks to IVY league Universities like MIT (6S191), Harvard(CS50) and others. Thanks you so much for making these resources publicly available for free of cost. I owe a debt of gratitude to all.

Awesome! Great way of teaching!

@AAmini thank you very much for a detailed master piece . i am watching this video repeatedly to understand each second. until 30 min , i am clear.

Great information in simple explanations

Great study material for the beginner of deep learning

It is very interesting lecture . Thank you for this .

Thank you for posting this. I'm a retired electrical engineer who spend much of my career doing software. I'm excited and motivated, as well as concerned, by AI breakthroughs.

Alexander, I am a Kaggle expert ( 2 bronze one silver and counting). This lecture is the clearest explanation of deep learning that I came across, thank you so much for sharing this. I hope you are considering writing a book about the topic The clarity you explained this is remarkable. Best wishes for continued success