Solve captcha without manually labeling a training set
- keras
- gan
- neural-network
- generative-adversarial-network
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Solve captcha without manually labeling a training set
Genomic Pretrained Network - GPN, GPN-MSA, PhyloGPN, GPN-Star
Fusing Histology and Genomics via Deep Learning - IEEE TMI
Usiigaci: stain-free cell tracking in phase contrast microscopy enabled by supervised machine learning
This repository contains notebooks that show the usage of TensorFlow Lite for quantizing deep neural networks.
🏃 The go-to single-cell Foundation Model
Optimising chemical reactions using machine learning
InferPy: Deep Probabilistic Modeling with Tensorflow Made Easy
TensorFlow Lite models for MIRNet for low-light image enhancement.
General Modeling Network Specification
Cyber Defence Monitoring Course Suite :: Suricata, Arkime (and others in the past)
PRnet is a flexible and scalable perturbation-conditioned generative model predicting transcriptional responses to unseen complex perturbations at bulk and single-cell levels.
Classification and Gradient-based Localization of Chest Radiographs using PyTorch.
High-Dimensional Gene Expression and Morphology Profiles of Cells across 28,000 Genetic and Chemical Perturbations
The repository host gives no description.
Deep learning model for single-cell inference of multi-omic profiles from a single input modality.
Sample code for Core ML using ResNet50 provided by Apple and a custom model generated by coremltools.
The repository host gives no description.
Deciphering cell-cell communication from spatially resolved transcriptomic data at single-cell resolution with subgraph-based attentional graph neural network
Sources for the Network Science Cheatsheets
American Sign Language Detection is a deep learning end to end project where we can detect American Sign Language.
Implementation of "Adversarial Frontier Stitching for Remote Neural Network Watermarking" in TensorFlow.
A GTFS-based data transit network data standard suitable for dynamic transit modeling.
Attempt at reproducing a SGNN's projection layer, but with word n-grams instead of skip-grams. Paper and more: http://aclweb.org/anthology/D18-1105