MIT Introduction to Deep Studying 6.S191: Lecture 2 Recurrent Neural Networks Lecturer: Ava Amini 2023 Version For all lectures, …
source
Tags: 6.s1916s191aialexander aminiaminiartificial intelligenceartificial intelligence newsartificial intelligence news 2023Attentionava soleimanybasicsComputer visionDeep Learningdeep learning basicsdeep learning pythonDeep Minddeeplearningintroductionlatest news about robotics technologylatest robotslatest robots 2023lecture 2long short term memorylstmmachine learningmitmit deep learningnetworksNeuralNeural NetworksOpenAIRecurrentrecurrent neural networksrnnrobot newsrobotics newsrobotics news 2023robotics technologies llcrobotics technologysequence modelingsequencessequentialsoleimanyTensorFlowtensorflow tutorialtransformerswhat is a rnnwhat is deep learning
The fact that these videos now have millions of views…. the world is evolving so fast scientifically or at least scientific culture.
good
I am an auditor and have very little to do with this subject, except for my curiosity. I feel lucky that these kind of videos are available for free
Thanks for the invaluable knowledge. You guys are awesome, keep up the good work. Can you guys also suggest where to go next from here after attending all the lectures. Any sample projects or lectures we can attend to get a deeper understanding to build commercial products similar to chatgpt/dall-e.
Will I get a certificate if I complete the course of machine learning 2024?
Thank you very much for this great oppurtunity to watch MIT lectures. always dreamt of a world class education and finally im doing a degree in AI and such videos are supporting my learning process very much
Great Presentation @8:00 minutes it really explained a circuitry I was looking forward to exploring
@18:00 minutes, it seems the RNN would confuse an LLM as to majority agreement
@36:00 minutes was this, an intentional description of money?
@47:37 minutes wouldn`t you not want this part of the network to be self-affecting
@53:04 minutes I think here one would have to check for a hung thread at the system level
it curves in on itself like spacetime. Any prediction outside the curve is clearly wrong. like planets circling the sun cruuzing through space
Great lecture! I have a question regarding transformers: must the word embeddings be pre-trained or the transformer will train the word embeddings by itself? I believe if the word embeddings are not trained then self-attention would not work well since the embeddings don't carry any semantic information because of that doing the cosine similarity will be useless.
🎯Course outline for quick navigation:
[00:09–02:02]Sequence modeling with neural networks
-[00:09–00:37]Ava introduces second lecture on sequence modeling in neural networks.
-[00:55–01:46]The lecture aims to demystify sequential modeling by starting from foundational concepts and developing intuition through step-by-step explanations.
[02:02–13:24]Sequential data processing and modeling
-[02:02–02:46]Sequential data is all around us, from sound waves to text and language.
-[03:10–03:50]Sequential modeling can be applied to classification and regression problems, with feed-forward models operating in a fixed, static setting.
-[05:02–05:26]Lecture covers building neural networks for recurrent and transformer architectures.
-[11:56–12:37]Rnn captures cyclic temporal dependency in maintaining and updating state at each time step.
[13:24–20:04]Understanding rnn computation
-[14:40–15:04]Explains rnn's prediction for next word, updating state, and processing sequential information.
-[15:05–15:47]Rnn computes hidden state update and output prediction.
-[16:17–17:05]Rnn updates hidden state and generates output in single operation.
-[18:45–19:39]The total loss for a particular input to the rnn is computed by summing individual loss terms. the rnn implementation in tensorflow involves defining an rnn as a layer operation and class, initializing weight matrices and hidden state, and passing forward through the rnn network to process a given input x.
[20:05–29:13]Rnn in tensorflow
-[20:05–20:54]Tensorflow abstracts rnn network definition for efficiency. practice rnn implementation in today's lab.
-[21:16–21:43]Today's software lab focuses on many-to-many processing and sequential modeling.
-[22:53–23:21]Sequence implies order, impacting predictions. parameter sharing is crucial for effective information processing.
-[25:04–25:29]Language must be numerically represented for processing, requiring translation into a vector.
-[28:29–28:56]Predict next word with short, long, and even longer sequences while tracking dependencies across different lengths.
[29:14–41:53]Rnn training and issues
-[30:02–30:27]Training neural network models using backpropagation algorithm for sequential information.
-[30:45–31:43]Rnns use backpropagation through time to adjust network weights and minimize overall loss through individual time steps.
-[32:03–32:57]Repeated multiplications of big weight matrices can lead to exploding gradients, making it infeasible to train the network stably.
-[35:45–37:18]Three ways to mitigate vanishing gradient problem: change activation functions, initialize parameters, use a more robust version of recurrent neural unit.
-[36:13–37:01]Relu activation function helps mitigate vanishing gradient problem by maintaining derivatives greater than one, and weight initialization with identity matrices prevents rapid shrinkage of weight updates.
-[37:54–38:25]Lstms are effective at tracking long-term dependencies by controlling information flow through gates.
-[40:18–41:13]Build rnn to predict musical notes and generate new sequences, e.g. completing schubert's unfinished symphony.
[41:53–50:11]Challenges in rnn and self-attention
-[43:58–44:40]Rnns face challenges in slow processing and limited capacity for long memory data.
-[46:37–47:00]Concatenate all time steps into one vector input for the model
-[47:21–47:45]Feed-forward network lacks scalability, loses in-order information, and hinders long-term memory.
-[48:11–48:34]Self-attention is a powerful concept in deep learning and ai, foundational in transformer architecture.
-[48:58–49:25]Exploring the power of self-attention in neural networks, focusing on attending to important parts of an input example.
[50:13–56:20]Neural network attention mechanism
-[50:13–50:43]Understanding the concept of search and its role in extracting important information from a larger data set.
-[51:52–55:24]Neural networks use self-attention to extract relevant information, like in the example of identifying a relevant video on deep learning, by computing similarity scores between queries and keys.
-[53:32–53:54]A neural network encodes positional information to process time steps all at once in singular data.
-[55:32–55:57]Comparing vectors using dot product to measure similarity.
[56:20–01:02:47]Self-attention mechanism in nlp
-[56:20–57:14]Computing attention scores to define relationships in sequential data.
-[59:11–59:39]Self-attention heads extract high attention features, forming larger network architectures.
-[01:00:32–01:00:56]Self-attention is a key operation in powerful neural networks like gpt-3.
offered by Coursnap
What an amazing content! Thank you! ❤️
This is amazing stuff, thanks so much for providing it. Can anyone point me to the labs material, I can't seem to find it on the website.
Thanks a lot!
I teach an AI in healthcare course and send all my students to this video. The professor teaching here has done brilliant job of breaking this down. Thanks so much.
She is so good!!!!🎉🎉❤❤
I am trying to step into deep learning for last couple of month. This is the best thing I have found so far. Thank you sir!.
Summary by Gemini:
The lecture is about recurrent neural networks, transformers, and attention.
The speaker, Ava, starts the lecture by introducing the concept of sequential data and how it is different from the data that we typically work with in neural networks. She then goes on to discuss the different types of sequential modeling problems, such as text generation, machine translation, and image captioning.
Next, Ava introduces the concept of recurrent neural networks (RNNs) and how they can be used to process sequential data. She explains that RNNs are able to learn from the past and use that information to make predictions about the future. However, she also points out that RNNs can suffer from vanishing and exploding gradients, which can make them difficult to train.
To address these limitations, Ava introduces the concept of transformers. Transformers are a type of neural network that does not rely on recurrence. Instead, they use attention to focus on the most important parts of the input data. Ava explains that transformers have been shown to be very effective for a variety of sequential modeling tasks, including machine translation and text generation.
In the last part of the lecture, Ava discusses the applications of transformers in various fields, such as biology, medicine, and computer vision. She concludes the lecture by summarizing the key points and encouraging the audience to ask questions.
badass
Till Now best Course,
I am doing great when I found these MIT's Lecture