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Human Pose Estimation with Deep Neural Networks

  • Course Code: Data Science - Human Pose Estimation with Deep Neural Networks
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 1 Days Audience: This course is geared for those who wants to build a human pose estimation algorithm based on convolutional neural networks.

Course Snapshot 

  • Duration: 1 days 
  • Skill-level: Foundation-level Deep Neural Networks skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to build a human pose estimation algorithm based on convolutional neural networks. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

In this course, you will learn about the building blocks of deep neural networks and how to use them. After this, you will be able to build basic image classification, image segmentation or key point detection algorithms yourself. You will also learn how to use and integrate more complex models, such as an object detector into your course. 

The building blocks of this course are also used in many other computer vision/machine applications. Object detection, for example, is also used for face recognition/detection, autonomous driving and OCR. The same algorithms used for key point detection are also used for image segmentation, facial landmark detection or action recognition. This course will give you the basic understanding of how all these algorithms work. 

Working in a hands-on learning environment, led by our Deep Neural Networks expert instructor, students will learn about and explore: 

  • we will build a human pose estimation algorithm based on convolutional neural networks.  
  • First, we use an object detector to detect a person in an image, and then build and train a convolutional neural network from scratch to detect key points of the human body. We will use Google Colab to train a model using GPU/TPUs.  
  • At the end of the course, the student will have an interactive demo that uses a laptop’s webcam to do human pose estimation. 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Getting the Data 
  • Introduction to Convolutional Neural Networks 
  • Object Detection 
  • Human Key point Estimation 
  • Model Deployment/Inference Demo Using the Webcam 

Audience & Pre-Requisites 

This course is for intermediate Python programmers who are familiar with machine learning. Knowledge of PyTorch and NumPy will be helpful.  

Pre-Requisites:  Students should have familiar with: 

TOOLS 

  • Basics of PIL 
  • Basics of JSON 
  • Basics of Matplotlib 
  • Intermediate PyTorch 
  • Intermediate NumPy 

TECHNIQUES 

  • Intermediate machine learning concepts such as classification and regression 
  • Basics of matrix and vector operations 

Course Agenda / Topics 

  1. Getting the Data 
  • Getting the Data 
  • Submit Your Work 
  1. Introduction to Convolutional Neural Networks 
  • Introduction to Convolutional Neural Networks 
  • Convolutional Neural Networks (CNNs) 
  • Structuring Deep Learning Projects and Hyperparameters Tuning 
  • Submit Your Work 
  1. Object Detection 
  • Object Detection 
  • Object Detection with R-CNN, SSD, and YOLO 
  • Submit Your Work 
  1. Human Keypoint Estimation 
  • Human Keypoint Estimation—Model Training 
  • Human Keypoint Estimation—Transfer Learning 
  • ResNet 
  • Transfer Learning 
  • Submit Your Work 
  1. Model Deployment / Inference Demo using the Webcam 
  1. Model Deployment/Inference Demo Using the Webcam 
  2. Submit Your Work 
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