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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.

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Unlimited Duration

Last Updated

July 29, 2021

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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.

Course Curriculum

    • Getting the Data 00:00:00
    • Submit Your Work 00:00:00
    • Introduction to Convolutional Neural Networks 00:00:00
    • Convolutional Neural Networks (CNNs) 00:00:00
    • Structuring Deep Learning Projects and Hyperparameters Tuning 00:00:00
    • Submit Your Work 00:00:00
    • Object Detection 00:00:00
    • Object Detection with R-CNN, SSD, and YOLO 00:00:00
    • Submit Your Work 00:00:00
    • Human Keypoint Estimation—Model Training 00:00:00
    • Human Keypoint Estimation—Transfer Learning 00:00:00
    • ResNet 00:00:00
    • Transfer Learning 00:00:00
    • Submit Your Work 00:00:00
    • Model Deployment/Inference Demo Using the Webcam 00:00:00
    • Submit Your Work 00:00:00

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