How chatgpt works

Demystifying Transformers: Understanding Encoder-Decoder Architecture, Attention Mechanisms, and Training Techniques | Lucidate's NLP Series Part 5
How does ChatCPT work? How is it trained? How does it achieve such impressive results?
Dive into the world of Transformer Neural Networks with Lucidate's in-depth tutorial! In this video, we break down the powerful architecture behind some of the most popular AI models in natural language processing, such as ChatGPT, BERT, and GPT-3.
🔥 What you'll learn in this video:
The Encoder-Decoder architecture: The backbone of Transformer Neural Networks
Training and Inference: Unraveling the brute-force approach to perfecting AI models
Attention Mechanism: Decoding the secret sauce that powers Transformers
Positional Embeddings: How Transformers capture sequence information
Practical examples and use-cases for Transformers in NLP tasks
Whether you're an AI enthusiast, a student, or a seasoned professional, this comprehensive guide will enhance your understanding of the inner workings of Transformer Neural Networks and their significance in NLP and AI. Don't miss out on this opportunity to expand your knowledge and gain insights into the AI models that are revolutionizing the world of natural language processing.
👉 Subscribe to our channel for more AI and Machine Learning content: / @lucidateai
Get an in-depth understanding of the latest breakthrough in NLP technology - ChatGPT! In this video, we'll dive into the inner workings of this cutting-edge AI language model and explore the concepts of word embeddings and attention. You'll learn how ChatGPT uses these techniques to generate natural language responses during inference. As well as how it "learns" to update its weights and parameters during training. Whether you're an AI enthusiast or a beginner data scientist, this video is a must-watch for anyone interested in understanding the power and potential of ChatGPT. So, sit back, relax, and let's discover how ChatGPT works!
🔗 Useful Links:
GPT playlist: • Transformers & NLP
Semantics: • ChatGPT - Semantics: T...
Positional embeddings: • ChatGPT Position and P...
Attention: • Attention is all you n...
Neural Networks: • Neural Network Primer
Backpropagation: • How neural networks le...
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Link to introductory series on Neural networks:
Lucidate website: www.lucidate.c....
RUclips: www.youtube.co....
Link to intro video on 'Backpropagation':
Lucidate website: www.lucidate.c....
RUclips: • How neural networks le...
'Attention is all you need' paper - arxiv.org/pdf/...
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Transformers are a type of artificial intelligence (AI) used for natural language processing (NLP) tasks, such as translation and summarisation. They were introduced in 2017 by Google researchers, who sought to address the limitations of recurrent neural networks (RNNs), which had traditionally been used for NLP tasks. RNNs had difficulty parallelizing, and tended to suffer from the vanishing/exploding gradient problem, making it difficult to train them with long input sequences.
Transformers address these limitations by using self-attention, a mechanism which allows the model to selectively choose which parts of the input to pay attention to. This makes the model much easier to parallelize and eliminates the vanishing/exploding gradient problem.
Self-attention works by weighting the importance of different parts of the input, allowing the AI to focus on the most relevant information and better handle input sequences of varying lengths. This is accomplished through three matrices: Query (Q), Key (K) and Value (V). The Query matrix can be interpreted as the word for which attention is being calculated, while the Key matrix can be interpreted as the word to which attention is paid. The eigenvalues and eigenvectors of these matrices tend to be similar, and the product of these two matrices gives the attention score.
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#ai #artificialintelligence #deeplearning #chatgpt #gpt3 #neuralnetworks #attention #attentionisallyouneed