In this liveProject, you’ll take on the role of a machine learning engineer working for a company developing augmented reality apps. These apps include games, virtual shopping assistants, and fitness coaches that need to be able to reliably recognize the shape of a human body. Your challenge is to create an application for human pose estimation: detecting a human body in an image and estimating its key points such as knees and elbows. To do this, you’ll build a convolutional neural network from scratch, training your model using Google Colab and your GPU. At the end of this liveProject, you’ll have completed an interactive demo application that uses a simple webcam to detect and predict human keypoints.
This project is designed for learning purposes and is not a complete, production-ready application or solution.
This liveProject is for intermediate Python programmers who are familiar with machine learning. Knowledge of PyTorch and NumPy will be helpful. To begin this liveProject, you will need to be familiar with:
- Basics of PIL
- Basics of JSON
- Basics of Matplotlib
- Intermediate PyTorch
- Intermediate NumPy
- Intermediate machine learning concepts such as classification and regression
- Basics of matrix and vector operations
you will learn
In this liveProject, you’ll learn how to build deep neural networks, and how to utilize them for computer vision. The building blocks of this project are easily transferable to other deep learning and computer vision challenges, such as face recognition, optical character recognition, and self-driving vehicles.
- Object detection algorithms
- Keypoint detection algorithms
- Transfer Learning
- Fully convolutional networks
- Learning and using the main building blocks and layers of deep learning models
- Training deep learning models on images
- Deploying deep learning models