This blogpost is about starting learning pytorch with a hands on tutorial on image classification.
Learn how distributed training works in pytorch: data parallel, distributed data parallel and automatic mixed precision. Train your deep learning models with massive speedups.
Implement a UNETR to perform 3D medical image segmentation on the BRATS dataset
Learn about the Weights and Biases library with a hands-on tutorial on the different features and visualizations.
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)
Learn everything about one of the most famous convolutional neural network architectures that is widely used on image segmentation.
A side-by-side comparison of JAX, Tensorflow and Pytorch while developing and training a Variational Autoencoder from scratch
Understand how positional embeddings emerged and how we use the inside self-attention to model highly structured data such as images
Learn about the einsum notation and einops by coding a custom multi-head self-attention unit and a transformer block
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.
How convolutional neural networks work? What are the principles behind designing one CNN architecture? How did we go from AlexNet to EfficientNet?
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?
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.
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.
Learn what autoencoders are and build one to generate new images