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Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs

Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs
Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs
Use Python & Keras to do 24 Projects - Recognition of Emotions, Age, Gender, Object Detection, Segmentation, Face Aging+
Created by Rajeev Ratan
  • English
  • English [Auto-generated]
  • Includes
  • 14 hours on-demand video
  • 4 articles
  • 12 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
Preview This Course - GET COUPON CODE

What you'll learn
  • Learn by completing 18 advanced computer vision projects including Emotion, Age & Gender Classification, London Underground Sign Detection, Monkey Breed, Flowers, Fruits , Simpsons Characters and many more!
  • Learn Advanced Deep Learning Computer Vision Techniques such as Transfer Learning and using pre-trained models (VGG, MobileNet, InceptionV3, ResNet50) on ImageNet and re-create popular CNNs such as AlexNet, LeNet, VGG and U-Net.
  • Understand how Neural Networks, Convolutional Neural Networks, R-CNNs , SSDs, YOLO & GANs with my easy to follow explanations
  • Become familiar with other frameworks (PyTorch, Caffe, MXNET, CV APIs), Cloud GPUs and get an overview of the Computer Vision World
  • Learn how to use the Python library Keras to build complex Deep Learning Networks (using Tensorflow backend)
  • Learn how to do Neural Style Transfer, DeepDream and use GANs to Age Faces up to 60+
  • Learn how to create, label, annotate, train your own Image Datasets, perfect for University Projects and Startups
  • Learn how to use CNNs like U-Net to perform Image Segmentation which is extremely useful in Medical Imaging application
  • Learn how to use OpenCV with a FREE Optional course with almost 4 hours of video
  • Learn how to use TensorFlow's Object Detection API and Create A Custom Object Detector in YOLO
Requirements
  • Basic programming knowledge is a plus but not a requirement
  • High school level math, College level would be a bonus
  • Atleast 20GB storage space for Virtual Machine and Datasets
  • A Windows, MacOS or Linux OS

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