Because it had commitment issues and always wanted to "overfit"!

It wanted to reduce its bias?

Have you ever tried explaining neural networks to your pet?

Have you ever felt like your AI assistant understands you better than your friends?

AI or Artificial Intelligence is the field where we try to make computers think, learn, and understand like humans, so they will be capable of writing, creating content, solving complex problems, drawing, and even coding and programming. like giving machines a piece of our brain but with less forgetfulness and no coffee breaks😅 What is NLP? NLP, or Natural language processing, is a field in AI where we train and make computers understand human language. So if we ask it a question, it understands and replies. 🧐 NLP is the reason why you can ask Siri about the weather or why your email can filter out spam. Imagine trying to understand a language from another planet. That’s NLP for computers! 🌐 What is GPT? This is the powerhouse behind many AI applications you see today. GPT, or Generative Pre-trained Transformer, is an NLP AI model. The idea is simple, in AI, we train the computer to do a certain task, and when we finish, we call the output an AI model. Here, GPT is the name of the NLP model that is trained to understand human language. We have multiple versions like GPT-2, GPT-3, 3.5, and 4 that are used by ChatGPT. What is LLM? We use this term a lot in prompt engineering. It is an abbreviation of the Large Language Model. Like GPT 3 or 3.5. that has 175 billion parameters. GPT is an example of an LLM. Large Language Models are trained on vast amounts of text data, allowing them to generate impressively human-like text. What are the Parameters? When we say that GPT-3 has 175 billion parameters, we mean that the model has 175 billion adjustable settings or “knobs” that can be tuned to improve its performance on various language tasks. So imagine you have a big puzzle that you need to solve, and you have a lot of different pieces that you can use to solve it. The more pieces you have, the better your chance of solving the puzzle correctly. In the same way, when we say that GPT-3 has 175 billion parameters, we mean that it has a lot of different pieces that it can use to solve language puzzles. These pieces are called parameters, and there are 175 billion of them!