LLM Bootcamp - Sergey Karayev - link
In traditional programming, developers write explicit instructions for a computer to follow. The developer defines both the input and the expected output, creating a deterministic sequence of steps the computer needs to execute. The computer then processes the input strictly based on this predefined logic to produce the output. If there are errors or unexpected scenarios, the code needs manual adjustments. The software's performance is directly linked to the accuracy and comprehensiveness of the developer's instructions.
Machine learning, on the other hand, involves creating algorithms that allow computers to learn from and make decisions based on data. Instead of being explicitly programmed to perform a task, the machine uses training data and various algorithms to generalize and make predictions or decisions. The input is the data, and the output is derived from patterns the system identifies within this data. The more “quality” data it's trained on, the better its decisions or predictions typically become.
Large Language Models (LLMs) like GPT-3 are a subset of machine learning models specialized in understanding and generating human-like text. Instead of being programmed with specific responses, they're trained on vast amounts of text to generate contextually relevant outputs.
Chatbots can either be rule-based (more aligned with traditional programming) or powered by machine learning. The latter allows them to understand a wider range of user inputs and respond more naturally, as they can learn and adapt from interactions rather than relying solely on predefined scripts.
<aside> 💡 Overall, LLMs represent a significant advancement in the field of NLP due to their immense size and pre-training on vast amounts of data, allowing them to capture complex language patterns and contexts. Traditional ML models, while still crucial in many domains, may not have the same capacity for language understanding and generation without extensive task-specific training.
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At the foundational level, computers perceive and process everything as numbers. Text, images, sounds, or videos are all encoded into numerical representations. Text is converted using schemes like ASCII or Unicode, where each character is associated with a specific number. Images are arrays of pixel values, with numbers indicating the intensity and shade of colors. Audio files consist of waveform values over time, representing sound frequencies and amplitudes. Videos combine both image and audio data, capturing sequences of image frames with accompanying sound.
LLM Bootcamp - Sergey Karayev - link