A game theoretic approach to explain the output of any machine learning model.
- interpretability
- machine-learning
- deep-learning
- gradient-boosting
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A game theoretic approach to explain the output of any machine learning model.
stable diffusion webui colab
Debugging, monitoring and visualization for Python Machine Learning and Data Science
Synthetic data generators for tabular and time-series data
Koç University deep learning framework.
TorchXRayVision: A library of chest X-ray datasets and models. Classifiers, segmentation, and autoencoders.
A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)
Neural question generation using transformers
Foundation Models for Genomics & Transcriptomics
LLVIP: A Visible-infrared Paired Dataset for Low-light Vision
ZeroCostDL4Mic: A Google Colab based no-cost toolbox to explore Deep-Learning in Microscopy
food image to recipe with deep convolutional neural networks.
Reference mapping for single-cell genomics
A TensorFlow implementation of the Mnemonic Descent Method.
tutorials made for biologists to learn deep learning
Code repository for The Automation Ahead series, showcasing practical examples for GenAI-driven automation in investments. Each installment dives into specific tools, techniques, and use cases to help professionals assess and apply GenAI effectively.
Peax is a tool for interactive visual pattern search and exploration in epigenomic data based on unsupervised representation learning with autoencoders
Classification and Gradient-based Localization of Chest Radiographs using PyTorch.
Mobile Deep Learning Projects, published by Packt
Sample code for Core ML using ResNet50 provided by Apple and a custom model generated by coremltools.
An Icon classifier made with TFLite Model Maker and deployed to Android with ML Model Binding.