https://github.com/tensorflow/lucid
Lucid is a collection of infrastructure and tools for research in neural network interpretability. #Framework
https://github.com/greentfrapp/lucent
Lucid is a collection of infrastructure and tools for research in neural network interpretability. #Framework
https://github.com/greentfrapp/lucent
GitHub
GitHub - tensorflow/lucid: A collection of infrastructure and tools for research in neural network interpretability.
A collection of infrastructure and tools for research in neural network interpretability. - tensorflow/lucid
https://umap-learn.readthedocs.io/en/latest/ Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualisation similarly to t-SNE, but also for general non-linear dimension reduction. #Framework #Tools
https://shap.readthedocs.io/en/latest/index.html
SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions.
#Framework
SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions.
#Framework
https://docs.determined.ai/latest/index.html#
#Framework distributed training, hyperparameter tuning
https://www.determined.ai/blog/data-version-control-determined
#Framework distributed training, hyperparameter tuning
https://www.determined.ai/blog/data-version-control-determined
Determined AI
Managing ML Training Data with DVC and Determined
Tracking machine learning data sets made easy with Data Version Control (DVC) and Determined.
https://keras.io/keras_tuner/
#Framework KerasTuner is an easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search.
#Framework KerasTuner is an easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search.
keras.io
Keras documentation: KerasTuner
❤1
https://www.microsoft.com/en-us/research/project/document-ai/
Microsoft Document AI (Intelligent Document Processing) #Framework
Microsoft Document AI (Intelligent Document Processing) #Framework
https://github.com/yzhao062/pyod outlier/anomaly detection - many methods in one library #framework #library
GitHub
GitHub - yzhao062/pyod: A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques
A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques - yzhao062/pyod
Deep Learning
Dino/Dino v2 explained: Self-distillation with no labels & etc. #FYI #Tips #Explained #Tutorial 1. https://medium.com/@anuj.dutt9/emerging-properties-in-self-supervised-vision-transformers-dino-paper-summary-4c7a6ed68161 Original Dino 2. https://encord.com/blog/dinov2…
https://www.samarkhanna.com/ExPLoRA/ Parameter-Efficient Extended Pre-training to Adapt Vision Transformers under Domain Shifts
#Paper #Framework
#Paper #Framework
Samarkhanna
ExPLoRA
ExPLoRA: Parameter-Efficient Extended Pre-training to Adapt Vision Transformers under Domain Shifts