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:  * Le Net5,  * Alex Net,  * VGG-16,  * Res Net,  * 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 Fifty