In this video we learn about a very important object detection metric in Mean Average Precision (mAP) that is used to evaluate object detection models. In the first part of the video we try to understand how this method works and then move on to PyTorch to implement this from scratch.

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0:00 - Introduction
0:09 - Explanation of mAP
8:19 - Implementation in PyTorch

What if you want to leverage the power of GPT-3, but don't want to wait for Open-AI to approve your application? Introducing GPT-Neo, an open-source Transformer model that resembles GPT-3 both in terms of design and performance.In this video, we'll discuss how to implement and train GPT-Neo with just a few lines of code.

We'll use Happy Transformer to implement GPT-Neo. Happy Transformer is an open-source Python library build on top of Hugging Face's Transformer library to allow programmers to implement state-of-the-art NLP models with just a few lines of code.

Thank you Eleuther AI for creating and training GPT-Neo.

New Course on how to create a web app to display GPT-Neo: https://www.udemy.com/course/n....lp-text-generation-p

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