MIT Introduction to Deep Learning (2023) | 6.S191

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

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.

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.

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.

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

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

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.

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

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

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

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

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.

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

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.

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

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

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

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.

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

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