Openpose is a library written in C++ with python wrapper available for real time multi person key point detection and multithreading. This model predicts the location of various human keypoints such as chest, hips, shoulder, neck, elbows, knees. This model uses part affinity fields and greedy inference to connect these localized keypoints.

In this talk, I'll be discussing how Openpose helps in the real time multi person detection system to jointly detect human body,hand,facial and foot keypoints detection and the part affinity field.

Also,discuss the model architecture,comparing with other models like Mask RCNN and AlphaPose. Finally show how pose estimation can be done on single as well as multiple person images using pretrained models

OpenAI made a breakthrough in Deep Reinforcement Learning when they created OpenAI five,a team of 5 agents and beat some of the best Dota2 players in the world.

Underlying algorithm that helped them in achieving this is Proximal Policy Optimization.OpenAI told that PPO has become its default reinforcement algorithm because of its ease and good performance.It uses novel policy gradient techniques that was more efficient and less costly than the traditional reinforcement learning methods.Key contribution of PPO is ensuring that a new update of policy does not change it too much from the previous policy.This leads to less variance at the cost of some bias but ensures smoother training.It also makes sure that the agent does not go down the unrecoverable path of taking senseless actions.

In this talk,I will be discussing the architecture of PPO,its major changes from Policy Gradient methods and how it can be implemented through code