The term GPT-3 stands for the third-generation Generative Pre-trained Transformer. It is a neural network machine learning model.
Furthermore we train model using internet data to generate any type of text.
Developed by OpenAI, it requires a small amount of input text to generate large volumes of relevant and sophisticated machine-generated text.
Actually, GPT stands for Generative Pretrained Transformer.
If you are a complete beginner your journey to learn TensorFlow might start from here.
For the TensorFlow beginners we have a dedicated category – TensorFlow for Beginners.
But besides that, you may need to learn several other machine learning and data science libraries.
As a result, you may check these categories as well – NumPy, Pandas, Matplotlib.
However, without learning Python, you cannot learn the usages of these libraries. Why? Because they all use Python as the Programming language.
Therefore please learn Python at the very beginning and start learning TensorFlow.
And, finally please check our Mathematics, Discrete Mathematics and Data Structures categories specially. We have tried to discuss from basic to intermediate level so that you can pick up the core ideas of TensorFlow.
By the way, GPT is a language model developed by OpenAI.
Hence it is based on the Transformer architecture and trained on a large corpus of text data using unsupervised learning.
The model is then fine-tuned for specific tasks such as language translation, question-answering, and text generation.
The goal of GPT is to generate human-like text based on the input provided to it.
What is unsupervised learning?
Unsupervised learning is a machine learning technique where the model learns from data without the need for labeled examples or explicit supervision.
In the case of GPT, it was trained on a large corpus of text data, which refers to a collection of written or spoken words.
As a result, The model learns patterns and relationships between words and phrases in the text data through unsupervised learning.
So it allows GPT to generate coherent and contextually appropriate text when given a prompt.
This is different from supervised learning, where we train model on labeled data, where the desired output is already known.
What Next?
TensorFlow, Machine Learning, AI and Data Science
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