Customize and train GPT-3 and other transformer neural networks such as BARD & BERT.
, Welcome to Lucidate's video on Foundation Models and Fine-Tuning in AI! In this tutorial, we'll explore the basics of foundation models and how they form the basis for many advanced AI applications. We'll also dive into the concept of fine-tuning, a process that enables users to tailor pre-trained AI models to meet their specific needs.
In Part 1, we'll discuss foundation models and their importance in providing a solid base for building new AI applications. We'll also explore the challenges associated with these models, including their lack of subject matter expertise in specific disciplines.
In Part 2, we'll explain how fine-tuning allows users to modify pre-trained AI models to better perform tasks related to a specific dataset. We'll also discuss the use of prompts and completions in the fine-tuning process, and why fine-tuning can lead to greater customization and improved performance.
Finally, in Part 3, we'll illustrate some applications of fine-tuned AI models in finance and capital markets. We'll explore how these models can be used to analyze financial news articles and other primary sources to determine secular changes in market sentiment, and how they can automate the process of financial reporting.
Overall, this video will provide you with a comprehensive overview of foundation models and fine-tuning in AI, and explain why they are revolutionizing the AI industry. If you're interested in learning more, be sure to subscribe to our channel for future updates!
=========================================================================
Link to introductory series on Neural networks:
Lucidate website: www.lucidate.c....
RU-vid: www.youtube.co....
Link to intro video on 'Backpropagation':
Lucidate website: www.lucidate.c....
RU-vid: • How neural networks le...
'Attention is all you need' paper - arxiv.org/pdf/...
=========================================================================
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.
=========================================================================
#ai #artificialintelligence #deeplearning #chatgpt #gpt3 #neuralnetworks #attention #attentionisallyouneed
15 сен 2024