Overview

Welcome to Modern Computer Vision Tensorflow, Keras & PyTorch! AI and Deep Learning are transforming industries and one of the most intriguing parts of this AI revolution is in Computer Vision!Update for 2024: Modern Computer Vision CourseWe're excited to bring you the latest updates for our 2024 modern computer vision course. Dive into an enriched curriculum covering the most advanced and relevant topics in the field:YOLOv8: Cutting-edge Object RecognitionDINO-GPT4V: Next-Gen Vision ModelsMeta CLIP for Enhanced Image AnalysisDetectron2 for Object DetectionSegment AnythingFace Recognition TechnologiesGenerative AI Networks for Creative ImagingTransformers in Computer VisionDeploying & Productionizing Vision ModelsDiffusion Models for Image ProcessingImage Generation and Its ApplicationsAnnotation Strategy for Efficient LearningRetrieval Augmented Generation (RAG)Zero-Shot Classifiers for Versatile ApplicationsUsing Roboflow: Streamlining Vision WorkflowsWhat is Computer Vision?But what exactly is Computer Vision and why is it so exciting? Well, what if Computers could understand what they're seeing through cameras or in images? The applications for such technology are endless from medical imaging, military, self-driving cars, security monitoring, analysis, safety, farming, industry, and manufacturing! The list is endless. Job demand for Computer Vision workers are skyrocketing and it's common that experts in the field are making USD $200,000 and more salaries. However, getting started in this field isn't easy. There's an overload of information, many of which is outdated, and a plethora of tutorials that neglect to teach the foundations. Beginners thus have no idea where to start.This course aims to solve all of that!Taught using Google Colab Notebooks (no messy installs, all code works straight away)27+ Hours of up-to-date and relevant Computer Vision theory with example codeTaught using both PyTorch and Tensorflow Keras! In this course, you will learn the essential very foundations of Computer Vision, Classical Computer Vision (using OpenCV) I then move on to Deep Learning where we build our foundational knowledge of CNNs and learn all about the following topics:Computer vision applications involving Deep Learning are booming!Having Machines that can see will change our world and revolutionize almost every industry out there. Machines or robots that can see will be able to:Perform surgery and accurately analyze and diagnose you from medical scans. Enable self-driving carsRadically change robots allowing us to build robots that can cook, clean, and assist us with almost any taskUnderstand what's being seen in CCTV surveillance videos thus performing security, traffic management, and a host of other servicesCreate Art with amazing Neural Style Transfers and other innovative types of image generationSimulate many tasks such as Aging faces, modifying live video feeds, and realistically replacing actors in films Detailed OpenCV Guide covering:Image Operations and ManipulationsContours and SegmentationSimple Object Detection and TrackingFacial Landmarks, Recognition and Face SwapsOpenCV implementations of Neural Style Transfer, YOLOv3, SSDs and a black and white image colorizer Working with Video and Video StreamsOur Comprehensive Deep Learning Syllabus includes: Classification with CNNsDetailed overview of CNN Analysis, Visualizing performance, Advanced CNNs techniques Transfer Learning and Fine TuningGenerative Adversarial Networks - CycleGAN, ArcaneGAN, SuperResolution, StyleGANAutoencoders Neural Style Transfer and Google DeepDreamModern CNN Architectures including Vision Transformers (ResNets, DenseNets, MobileNET, VGG19, InceptionV3, EfficientNET and ViTs)Siamese Networks for image similarityFacial Recognition (Age, Gender, Emotion, Ethnicity)PyTorch Lightning Object Detection with YOLOv5 and v4, EfficientDetect, SSDs, Faster R-CNNs, Deep Segmentation - MaskCNN, U-NET, SegNET, and DeepLabV3Tracking with DeepSORTDeep Fake Generation Video ClassificationOptical Character Recognition (OCR)Image Captioning3D Computer Vision using Point Cloud DataMedical Imaging - X-Ray analysis and CT-ScansDepth EstimationMaking a Computer Vision API with FlaskAnd so much moreThis is a comprehensive course, is broken up into two (2) main sections. This first is a detailed OpenCV (Classical Computer Vision tutorial) and the second is a detailed Deep Learning This course is filled with fun and cool projects including these Classical Computer Vision Projects:Sorting contours by size, location, using them for shape matchingFinding WaldoPerspective Transforms (CamScanner)Image Similarity K-Means clustering for image colorsMotion tracking with MeanShift and CAMShiftOptical FlowFacial Landmark Detection with DlibFace SwapsQR Code and Barcode ReachingBackground removalText DetectionOCR with PyTesseract and EasyOCRColourize Black and White PhotosComputational Photography with inpainting and Noise RemovalCreate a Sketch of yourself using Edge DetectionRTSP and IP StreamsCapturing Screenshots as videoImport Youtube videos directlyDeep Learning Computer Vision Projects:PyTorch & Keras CNN Tutorial MNIST PyTorch & Keras Misclassifications and Model Performance AnalysisPyTorch & Keras Fashion-MNIST with and without Regularisation CNN Visualisation - Filter and Filter Activation VisualisationCNN Visualisation Filter and Class MaximisationCNN Visualisation GradCAM GradCAMplusplus and FasterScoreCAMReplicating LeNet and AlexNet in Tensorflow2.0 using KerasPyTorch & Keras Pretrained Models - 1 - VGG16, ResNet, Inceptionv3, MobileNetv2, SqueezeNet, WideResNet, DenseNet201, MobileMNASNet, EfficientNet and MNASNetRank-1 and Rank-5 AccuracyPyTorch and Keras Cats vs Dogs PyTorch - Train with your own dataPyTorch Lightning Tutorial - Batch and LR Selection, Tensorboards, Callbacks, mGPU, TPU and morePyTorch Lightning - Transfer LearningPyTorch and Keras Transfer Learning and Fine TuningPyTorch & Keras Using CNN's as a Feature ExtractorPyTorch & Keras - Google Deep DreamPyTorch Keras - Neural Style Transfer + TF-HUB ModelsPyTorch & Keras Autoencoders using the Fashion-MNIST DatasetPyTorch & Keras - Generative Adversarial Networks - DCGAN - MNISTKeras - Super Resolution SRGANProject - Generate_Anime_with_StyleGANCycleGAN - Turn Horses into ZebrasArcaneGAN inferencePyTorch & Keras Siamese Networks Facial Recognition with VGGFace in KerasPyTorch Facial Similarity with FaceNetDeepFace - Age, Gender, Expression, Headpose and RecognitionObject Detection - Gun, Pistol Detector - Scaled-YOLOv4 Object Detection - Mask Detection - TensorFlow Object Detection - MobileNetV2 SSDObject Detection - Sign Language Detection - TFODAPI - EfficientDetD0-D7Object Detection - Pot Hole Detection with TinyYOLOv4Object Detection - Mushroom Type Object Detection - Detectron 2Object Detection - Website Screenshot Region Detection - YOLOv4-DarknetObject Detection - Drone Maritime Detector - Tensorflow Object Detection Faster R-CNNObject Detection - Chess Pieces Detection - YOLOv3 PyTorchObject Detection - Hardhat Detection for Construction sites - EfficientDet-v2Object DetectionBlood Cell Object Detection - YOLOv5Object DetectionPlant Doctor Object Detection - YOLOv5Image Segmentation - Keras, U-Net and SegNetDeepLabV3 - PyTorch_Vision_Deeplabv3Mask R-CNN DemoDetectron2 - Mask R-CNNTrain a Mask R-CNN - ShapesYolov5 DeepSort Pytorch tutorialDeepFakes - first-order-model-demoVision Transformer Tutorial PyTorchVision Transformer Classifier in KerasImage Classification using BigTransfer (BiT)Depth Estimation with KerasImage Similarity Search using Metric Learning with KerasImage Captioning with KerasVideo Classification with a CNN-RNN Architecture with KerasVideo Classification with Transformers with KerasPoint Cloud Classification - PointNetPoint Cloud Segmentation with PointNet3D Image Classification CT-ScanX-ray Pneumonia Classification using TPUsLow Light Image Enhancement using MIRNetCaptcha OCR CrackerFlask Rest API - Server and Flask Web AppDetectron2 - BodyPose

Skills

Reviews