A self-complete guide for understanding biology concepts that are necessary for applying deep learning in biology and bioinformatics focused on protein folding and alphafold2 related stuff
Explore the most popular deep learning architecture to perform automatic speech recognition (ASR). From recurrent neural networks to convolutional and transformers.
A general perspective on understanding self-supervised representation learning methods.
Learn about the Weights and Biases library with a hands-on tutorial on the different features and visualizations.
A curated list of the best courses, books and blog to learn computer vision with deep learning methods
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)
Discover what is regularization, why it is necessary in deep neural networks and explore the most frequently used strategies: L1, L2, dropout, stohastic depth, early stopping and more
Learn about the SOTA recommender system models. From collaborative filtering and factorization machines to DCN and DLRM
Explore the most popular deep learning models to perform text to speech (TTS) synthesis