Antonio Rueda-Toicen

Image Embeddings

Building a Feedforward Network for Classification in PyTorch

Introduction to Neural Networks

Image Generation with Diffusion Models

Deep Learning Approaches to Image Segmentation

Approaches to Object Detection

Contrastive Language-Image Pretraining (CLIP)

Vision Transformers

Transfer Learning and Fine-tuning Pretrained Models

Image Data Augmentation

Interpretability with Class Activation Mapping

Usage of Binary Cross Entropy Loss

Regularization with Dropout and Batch Normalization

Skip Connections in Neural Networks

Normalizing Input and Inference with Pretrained Models

Upsampling and Channel Mixing with Convolutions

Pooling in Neural Networks

Fundamentals of Convolutions

PyTorch's Dataset and DataLoader

Metrics for Classification and Experiment Tracking

Matrix Multiplication and Activation Functions

Building an MLP for Image-based Regression in PyTorch

Image Tensors in PyTorch

Digital Images in PIL and NumPy

Common Tasks in Computer Vision

MedRegistryChain - Bluecode 2018 - Demo

MedRegistryChain - The Chain - Bluecode Challenge 2018

Algorithmic Information Dynamics MOOC - intro video