Practical Deep Learning For Computer Vision With Python by David Landup

Implement Object detection, Image Segmentation, Image Classification, Image Generation & People Counting from scratch!

ConvNets, ResNet, Inception, Faster R-CNN, SSD, TensorFlow Object Detection, YOLOv4. Train your own data.

New updated version of the previous course. Contains new SOTAs such as ViT and YOLOv7

Deep Learning review, CNNs, VGG-16, Faster R-CNN, TensorFlow Object Detection API, YOLO. Train your own data.

Discover deep learning and computer vision with SAS!
Deep Learning for Computer Vision with SAS®: An Introduction introduces the pivotal components of deep learning. Readers will gain an in-depth understanding of how to build deep feedforward and convolutional neural networks, as well as variants of denoising autoencoders. Transfer learning is covered to help readers learn about this emerging field. Containing a mix of theory and application, this book will also briefly cover methods for customizing deep learning models to solve novel business problems or answer research questions. SAS programs and data are included to reinforce key concepts and allow readers to follow along with included demonstrations.

Image Semantic Segmentation for Computer Vision with PyTorch & Python to Train & Deploy YOUR own Models (UNet, SAM)