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Antonio Rueda-Toicen
computer vision
machine learning
deep learning
artificial intelligence
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