Popular GitHub Repositories for Elastic Loss

Daytona is a Secure and Elastic Infrastructure for Running AI-Generated Code

An open source implementation of CLIP.

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

File Parser optimised for LLM Ingestion with no loss ? Parse PDFs, Docx, PPTx in a format that is ideal for LLMs.

Chinese version of CLIP which achieves Chinese cross-modal retrieval and representation generation.

Context aware, pluggable and customizable data protection and de-identification SDK for text and images

Siamese and triplet networks with online pair/triplet mining in PyTorch

MiniMax-01 is a simple implementation of the MiniMax algorithm, a widely used strategy for decision-making in two-player turn-based games like Tic-Tac-Toe. The algorithm aims to minimize the maximum possible loss for the player, making it a popular choice for developing AI opponents in various game scenarios.

??? 专注于YOLO11，YOLOv8、TYOLOv12、YOLOv10、RT-DETR、YOLOv7、YOLOv5改进模型，Support to improve backbone, neck, head, loss, IoU, NMS and other modules?

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PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations

label-smooth, amsoftmax, partial-fc, focal-loss, triplet-loss, lovasz-softmax. Maybe useful

Chinese NER(Named Entity Recognition) using BERT(Softmax, CRF, Span)

Fluid, elastic data abstraction and acceleration for BigData/AI applications in cloud. (Project under CNCF)

PyTorch implementation of various methods for continual learning (XdG, EWC, SI, LwF, FROMP, DGR, BI-R, ER, A-GEM, iCaRL, Generative Classifier) in three different scenarios.

:dart: Task-oriented embedding tuning for BERT, CLIP, etc.

Code for the Lovász-Softmax loss (CVPR 2018)

Live training loss plot in Jupyter Notebook for Keras, PyTorch and others

GNES is Generic Neural Elastic Search, a cloud-native semantic search system based on deep neural network.

Implementation of triplet loss in TensorFlow