Now let's move on to the main block of ChatGPT - the Attention mechanism. Everything that we have considered so far is the ten-year-old architecture: fully connected neural networks, embedding. All the most interesting and modern is inside Attention.
Attention Is All You Need!
ChatGPT moves input sequence token vectors in embedding space. Tokens that are nearby become the output of the model. This is a brief description of how ChatGPT works. In the video, we will understand in detail how the ChatGPT core works.
ChatGPT (Chat Generative Pre-trained Transformer) by OpenAI is a machine learning model capable of interactively processing natural language queries, writing extended texts, code, music, and much more.
In this video, we will get to know it in more detail, delve a little into the history of the development of neural networks and understand what preceded its appearance, consider examples of ChatGPT.
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We continue to interpret the work of neural networks using SHAP diagrams. With this approach, not the processes that occur on a particular channel of a particular layer are analyzed, but the significance of a particular pixel in the original image.
With the help of SHAP diagrams, it is possible to analyze which pixels of the original image led to an increase in the probability of a given class, and which ones led to its decrease.
Today we will interpret the processes that occur "in the head" of convolutional neural networks. Let's find out how the miracle happens, which we then get, looking at the graphs of errors and metrics.
We warn you right away - there will be no complete answer :) And no one knows how this miracle occurs. But some additional understanding may appear.
Let's read and discuss together the article from deepmind.
The reward-is-enough hypothesis suggests that agents with powerful reinforcement learning algorithms when placed in rich environments with simple rewards could develop the kind of broad, multi-attribute intelligence that constitutes an artificial general intelligence.
In this video, we will understand a new group of methods that are based directly on the agent's policy. Let's get acquainted with the REINFORCE method, consider a combination of Actor Critic algorithms based on values similar to Policy Gradient and Q-Learning.
We continue to discuss the method of deep Q-learning (DQN), consider the software implementation of DQN, get acquainted with the Ray library.
Today, let's dive into the amazing world of neural networks and look at a deep Q-learning or DQN method that can play Atari 2600 games at the human expert level.
Consider the first working method of Q-learning, which underlies many other reinforcement learning algorithms. Also, using this method, we will solve two problems from those that were at the previous seminar.
We are starting a series of seminars on the topic of machine learning with reinforcement. Today we discuss the problem statement, basic definitions, look and study examples.
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