Tutorials are too technical and theoretical However, learning computer vision with Deep Learning is hard! Huge technology companies such as Facebook, Google, Microsoft, Apple, Amazon, and Tesla are all heavily devoting billions to computer vision research.Īs a result, the demand for computer vision expertise is growing exponentially! Simulate many tasks such as Aging faces, modifying live video feeds and realistically replace actors in films Understand what's being seen in CCTV surveillance videos thus performing security, traffic management and a host of other servicesĬreate Art with amazing Neural Style Transfers and other innovative types of image generation Radically change robots allowing us to build robots that can cook, clean and assist us with almost any task Perform surgery and accurately analyze and diagnose you from medical scans. Machines or robots that can see will be able to: Having Machines that can 'see' will change our world and revolutionize almost every industry out there. Take a picture of a Credit Card, extract and identify the numbers on that card!Ĭomputer vision applications involving Deep Learning are booming! Recognize multiple persons using your webcamįacial Recognition on the Friends TV Show Characters Newly added Facial Recognition & Credit Card Number Reader Projects How to set up a Cloud GPU on PaperSpace and Train a CIFAR10 AlexNet CNN almost 100 times faster!īuild a Computer Vision API and Web App and host it on AWS using an EC2 Instance! If you want to learn all the latest 2019 concepts in applying Deep Learning to Computer Vision, look no further - this is the course for you! You'll get hands the following Deep Learning frameworks in Python:Īll in an easy to use virtual machine, with all libraries pre-installed! Windows install guide for TensorFlow2.0 (with Keras), OpenCV4 and Dlibĭeep Learning Computer Vision™ Use Python & Keras to implement CNNs, YOLO, TFOD, R-CNNs, SSDs & GANs + A Free Introduction to OpenCV. High school level math, College level would be a bonusĪtleast 20GB storage space for Virtual Machine and Datasets Use Cloud GPUs on PaperSpace for 100X Speed Increase vs CPUīuild a Computer Vision API and Web App and host it on AWS using an EC2 Instanceīasic programming knowledge is a plus but not a requirement How to use TensorFlow's Object Detection API and Create A Custom Object Detector in YOLO How to use CNNs like U-Net to perform Image Segmentation which is extremely useful in Medical Imaging application How to use OpenCV with a FREE Optional course with almost 4 hours of video How to create, label, annotate, train your own Image Datasets, perfect for University Projects and Startups How to do Neural Style Transfer, DeepDream and use GANs to Age Faces up to 60+ How to use the Python library Keras to build complex Deep Learning Networks (using Tensorflow backend) Understand how Neural Networks, Convolutional Neural Networks, R-CNNs, SSDs, YOLO & GANs with my easy to follow explanationsīecome familiar with other frameworks (PyTorch, Caffe, MXNET, CV APIs), Cloud GPUs and get an overview of the Computer Vision World Learn by completing 26 advanced computer vision projects including Emotion, Age & Gender Classification, London Underground Sign Detection, Monkey Breed, Flowers, Fruits, Simpsons Characters and many more!Īdvanced 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. Language: English | VTT | Size: 8.79 GB | Duration: 14h 43m
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