Deep Learning for Computer Vision with Tensorflow 2.X

This new course is the updated version of the previous course Deep Learning for Computer Vision with Tensorflow2. X. It contains new classes explaining in detail many state of the art algorithms for image classification and object detection.

The course was entirely written using Google Colaboratory(Colab) in order to help students that don't have a GPU card in your local system, however you can follow the course easily if you have one. This time the course starts explaining in detail the building blocks from ConvNets which are the base for image classification and the base for the feature extractors in the latest object detection algorithms. We're going to study in detail the following concepts and algorithms:- Image Fundamentals in Computer Vision,- Load images in Generators with TensorFlow,- Convolution Operation,- Sparsity Connections and parameter sharing,- Depthwise separable convolution,- Padding,- Conv2D layer with Tensorflow,- Pooling layer,- Fully connected layer,- Batch Normalization,- ReLU activation and other functions,- Number of training parameters calculation,- Image Augmentation, etc- Different ConvNets architectures such as: * LeNet5, * AlexNet, * VGG-16, * ResNet, * Inception, * The lastest state of art Vision Transformer (ViT)- Many practical applications using famous datasets and sources such as: * Covid19 on X-Ray images, * CIFAR10, * Fashion MNIST, * BCCD, * COCO dataset, * Open Images Dataset V6 through Voxel FiftyOne, * ROBOFLOWIn the Object Detection chapter we'll learn the theory and the application behind the main object detection algorithms doing a journey since the beginnings to the latest state of the art algorithms.

You'll be able to use the main algorithms of object detection to develop practical applications. Some of the content in this Chapter is the following:- Object detection milestones since Selective Search algorithm,- Object detection metrics,- Theoretical background for R-CNN, Fast R-CNN and Faster R-CNN,- Detect blood cells using Faster R-CNN application,- Theoretical background for Single Shot Detector (SSD),- Train your customs datasets using different models with TensorFlow Object Detection API- Object Detection on images and videos,- YOLOv2 and YOLOv3 background. - Object detection from COCO dataset application using YOLOv4 model.

- YOLOv4 theoretical class- Practical application for detecting Robots using a custom dataset (R2D2 and C3PO robots dataset) and YOLOv4 model- Practical application for License Plate recognition converting the plates images in raw text format (OCR) with Yolov4, OpenCV and ConvNets-Object detection with the latest state of the art YOLOv7. -Face Mask detection application with YOLOv7I have updated the course with a new chapter for Image Segmentation:- I review the theory behind U-Net for image segmentation- We develop an application for detecting brain tumors from MRI images using U-Net. - We train models with U-Net and U-Net with attention mechanism.