U-NET para segmentación semántica, explicación del paper.

U-NET Code Implementation U-NET Model in PyTorch:    • Segmentación Semántica - U-NET desde cero ...   Dataset and Dataloader:    • PyTorch DATASET & DATALOADER con U-NET des...   Evaluation Metrics and Results    • Entrenamiento y métricas IoU y Coeficiente...   In this video, I explain in detail the U-NET paper, a revolutionary architecture with a major impact on semantic segmentation problems. It allows for the classification of pixels in an image using a fully convolutional end-to-end model that is much more efficient than previous models using sliding windows. In the next video, I will implement this architecture in PyTorch from scratch! Reference to the original paper: Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation (arXiv:1505.04597). arXiv. https://doi.org/10.48550/arXiv.1505.0... About the Deep Learning Fundamentals with Python and PyTorch series: In this video series, I explain what neural networks are, what machine learning is, and what deep learning is. We start with the fundamental mathematical principles and work through their implementation in code. To do this, we will first use Python and Numpy to understand the principles of neural network programming, including the backpropagation algorithm. With this foundation, we will introduce the PyTorch framework and build more complex models such as convolutional neural networks (CNNs). About the video series: In this video series I will explain what Neural Networks are, and how Deep Neural Networks work, from the mathematical principles to their implementation in code. Firstly, we will use pure Python and Numpy to understand the fundamentals including backpropagation for a simple Fully Connected Network, and from there we will build on to Convolutional Neural Networks (CNN) using PyTorch. I will be uploading at least one new video every week until we reach different architectures of CNNs. Then, depending on the response and interest in the series I may cover newer models using Generative Adversarial Networks (GANs), and Recurrent Neural Networks.