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afiodorov.github.io | ||
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amatria.in
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| | | | | "We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run." - Amara's law | |
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blog.adnansiddiqi.me
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| | | | | Learn the basics of Large Language Models (LLMs) in this introduction to GenAI series. Discover how LLMs work, their architecture, and practical applications like customer support, content creation, and software development. | |
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blog.pdebruin.org
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| | | | | Retrieval Augmented Generation Hackathon starts on September 3. Repo with more info, stream schedule, samples, registration: https://aka.ms/raghack Large language models are powerful language generators, but they don't know everything about the world. RAG combines the power of large language models with the knowledge of a search engine. This allows you to ask questions of your own data, and get answers that are relevant to the context of your question. LLM AI YouTube playlists Thanks for reading! :-) | |
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matt.might.net
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| | | [AI summary] This text explains how a single perceptron can learn basic Boolean functions like AND, OR, and NOT, but fails to learn the non-linearly separable XOR function. This limitation led to the development of modern artificial neural networks (ANNs). The transition from single perceptrons to ANNs involves three key changes: 1) Adding multiple layers of perceptrons to create Multilayer Perceptron (MLP) networks, enabling modeling of complex non-linear relationships. 2) Introducing non-linear activation functions like sigmoid, tanh, and ReLU to allow networks to learn non-linear functions. 3) Implementing backpropagation and gradient descent algorithms for efficient training of multilayer networks. These changes allow ANNs to overcome the limitations of ... | ||
