Tagged deep-learning

Study the Deep Learning textbook chapter by chapter with a 2026 update layer

Discover conserved quantities and symmetries from trajectory data with neural nets

Train physics-informed neural networks with DeepXDE for forward and inverse PDEs

Train graph neural networks on node, link and molecular graph tasks with PyG

Build graph neural networks for molecules and proteins with TorchDrug

Patch TileGym GPU kernels into Hugging Face transformers models before loading

Match T cell receptors to their cognate epitopes with deep learning models

Polish Nanopore assemblies and call variants with medaka neural networks

Call germline variants with the DeepVariant deep learning caller

Predict molecular ADMET properties and generate molecules with graph neural networks

Forecast long time series with GRU neural networks

Build graph neural networks for molecules and proteins in PyTorch

Design, train and audit deep learning pipelines for imaging with proper validation

Model base-level ATAC-seq accessibility and variant effects with chromBPNet

Train base-resolution deep learning models for ChIP-seq and CUT&RUN profiles with BPNet

Predict RNA-binding protein sites from sequence with deep learning models

Train, evaluate and export neural networks in MATLAB with the trainnet API

Write and optimize ESP32-P4 SIMD assembly for neural network operators

Implement, test and optimize neural network operators in the ESP-DL framework

Detect promoter regions in DNA sequences with a hosted transformer model

Build spiking neural network simulations with neuron models, connectivity and plasticity

Build ML pipelines with preprocessing, tuning, evaluation and model explainability

Run HuggingFace transformer models for classification, NER and embeddings