??ChatGpt : Explaining Math's behind ChatGpt without getting into math's
???What is Chat GPT?
???Chat GPT is an AI program that can understand natural language and generate human-like responses.
???It uses deep learning algorithms to analyze vast amounts of text data from the internet, books, and other sources.
???This process enables the model to learn the structure and patterns of human language, which it can then apply to generate its own responses.
???How Does Chat GPT Work?
???Chat GPT works by processing text input from a user and generating a response based on its understanding of natural language.
???The model uses a technique called "transformer architecture," which allows it to consider the context of the input text when generating a response.
Example:
???if you ask Chat GPT, "What is the weather like today?" the model will consider the location, time, and other factors to provide an accurate response.
???If you follow up with, "Can you recommend a good restaurant nearby?" the model will use the context from the previous question to generate a relevant response.
Fig1: Transformer
To better appreciate the powers and limitations of GPT-3, one needs some familiarity with pre-trained NLP models which came before it, say:
???BERT (Oct-11-2018)
???RoBERTa (July-26-2019)
???DistilBERT (Oct-2-2019)
???ALBERT (Sep-26-2019)
???ChatGPT is a language model that uses deep learning algorithms to generate natural language responses based on input text.
???Model architecture is based on the Transformer, a type of neural network that can process sequential data like text.
?? Explore mathematical concepts involved in ChatGPT:
???Language Modeling
???This is the task of predicting next word in a sequence of words.
???ChatGPT is pre-trained on a large corpus of text, and the language modeling objective is to predict the next word given the previous words in the sequence.
???Neural Networks
???Neural networks are computational models that are inspired by the structure and function of the human brain.
???ChatGPT uses a neural network architecture called the Transformer, which is designed to process sequential data.
领英推荐
-------------------------------------
Don't forget to Follow :? Mukesh Manral????
News Letter :?https://lnkd.in/dEAbBiaH
Medium :?https://lnkd.in/ddzYC_wX
-------------------------------------
???Attention Mechanism
???Key component of Transformer architecture.
???It allows the model to focus on different parts of the input sequence when generating the output.
???Attention mechanism computes a set of weights that indicate how important each input token is to the output at a given position.
???Self Attention
???Type of attention mechanism where the model attends to different parts of the input sequence at the same time.
???ChatGPT uses self-attention to compute a context vector for each input token based on its relationship with all the other tokens in the sequence.
-------------------------------------
Don't forget to Follow :? Mukesh Manral????
News Letter :?https://lnkd.in/dEAbBiaH
Medium :?https://lnkd.in/ddzYC_wX
-------------------------------------
???Pre Training
???Process of training a model on a large dataset before fine-tuning it on a specific task.
???ChatGPT is pre-trained on a massive corpus of text data, which enables it to learn the structure and patterns of natural language.
???Fine Tuning
???Process of adapting a pre-trained model to a specific task by training it on a smaller dataset.
???ChatGPT can be fine-tuned on a variety of tasks, including language generation, question answering, and conversational AI.
?? Conclusion:
???ChatGPT is a complex model that relies on a variety of mathematical concepts to generate natural language responses.
???By leveraging the power of deep learning and neural networks, ChatGPT is able to understand and respond to a wide range of input text, making it a powerful tool for natural language processing.