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Home » Technology » Big Data & Data Science » Human Pose Estimation with Deep Neural Networks

Course Skill Level:

Foundational

Course Duration:

1 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:

    HPEDNNL21E09

Who should attend & recommended skills:

Intermediate Python programmers with basic PIL, JSO, Matplotlib, Matrix and vector and operations, machine learning, and intermediate PyTorch, & NumbPy

Who should attend & recommended skills

  • This course is for intermediate Python programmers familiar with machine learning who want to build a human pose estimation algorithm based on convolutional neural networks. Knowledge of PyTorch and NumPy will be helpful.
  • Skill-level: Foundation-level Deep Neural Networks skills for Intermediate skilled team members. This is not a basic class.
  • PIL: Basic: (1-2 years’ experience)
  • JSON: Basic (1-2 years’ experience)
  • Matplotlib: Basic (1-2 years’ experience)
  • Matrix and vector operations: Basic (1-2 years’ experience)
  • Machine Learning concepts such as classification and regression: Basic (1-2 years’ experience)
  • PyTorch: Intermediate (3-5 years’ experience)
  • NumPy: Intermediate (3-5 years’ experience)

About this course

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.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Deep Neural Networks expert instructor, students will learn about and explore:
  • Getting the Data
  • Introduction to Convolutional Neural Networks
  • Object Detection
  • Human Key point Estimation
  • Model Deployment/Inference Demo Using the Webcam
  • Building a human pose estimation algorithm based on convolutional neural networks.
  • Using an object detector to detect a person in an image, and then building and training 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

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