Pytorch

Implement basic Deep Learning models and advanced real-life applications with Pytorch

Pytorch

A complete Weights and Biases tutorial

Learn about the Weights and Biases library with a hands-on tutorial on the different features and visualizations.

Pytorch · Attention and Transformers · Computer Vision

A complete Hugging Face tutorial: how to build and train a vision transformer

Learn about the Hugging Face ecosystem with a hands-on tutorial on the datasets and transformers library. Explore how to fine tune a Vision Transformer (ViT)

Convolutional Neural Networks · Medical · Computer Vision · Pytorch

An overview of Unet architectures for semantic segmentation and biomedical image segmentation

Learn everything about one of the most famous convolutional neural network architectures that is widely used on image segmentation.

Software · Tensorflow · Pytorch · Autoencoders

JAX vs Tensorflow vs Pytorch: Building a Variational Autoencoder (VAE)

A side-by-side comparison of JAX, Tensorflow and Pytorch while developing and training a Variational Autoencoder from scratch

Attention and Transformers · Pytorch

How Positional Embeddings work in Self-Attention (code in Pytorch)

Understand how positional embeddings emerged and how we use the inside self-attention to model highly structured data such as images

Attention and Transformers · Pytorch

Understanding einsum for Deep learning: implement a transformer with multi-head self-attention from scratch

Learn about the einsum notation and einops by coding a custom multi-head self-attention unit and a transformer block

Attention and Transformers · Computer Vision · Pytorch

How the Vision Transformer (ViT) works in 10 minutes: an image is worth 16x16 words

In this article you will learn how the vision transformer works for image classification problems. We distill all the important details you need to grasp along with reasons it can work very well given enough data for pretraining.

Convolutional Neural Networks · Computer Vision · Pytorch

Best deep CNN architectures and their principles: from AlexNet to EfficientNet

How convolutional neural networks work? What are the principles behind designing one CNN architecture? How did we go from AlexNet to EfficientNet?

Recurrent Neural Networks · Pytorch

Recurrent Neural Networks: building GRU cells VS LSTM cells in Pytorch

What are the advantages of RNN’s over transformers? When to use GRU’s over LSTM? What are the equations of GRU really mean? How to build a GRU cell in Pytorch?

Recurrent Neural Networks · Pytorch

Recurrent neural networks: building a custom LSTM cell

Are you interested to see how recurrent networks process sequences under the hood? That’s what this article is all about. We are going to inspect and build our own custom LSTM model. Moreover, we make some comparisons between recurrent and convolutional modules, to maximize our understanding.

Medical · Computer Vision · Pytorch

Deep learning in medical imaging - 3D medical image segmentation with PyTorch

The basic MRI foundations are presented for tensor representation, as well as the basic components to apply a deep learning method that handles the task-specific problems(class imbalance, limited data). Moreover, we present some features of the open source medical image segmentation library. Finally, we discuss our preliminary experimental results and provide sources to find medical imaging data.

Autoencoders · Unsupervised Learning · Generative Learning · Pytorch

How to Generate Images using Autoencoders

Learn what autoencoders are and build one to generate new images