The RAG Experiment Accelerator is a versatile tool designed to expedite and facilitate the process of conducting experiments and evaluations using Azure Cognitive Search and RAG pattern.
-
Updated
May 25, 2026 - Python
The RAG Experiment Accelerator is a versatile tool designed to expedite and facilitate the process of conducting experiments and evaluations using Azure Cognitive Search and RAG pattern.
[ACCV22] Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removal, https://arxiv.org/abs/2210.03061
Efficient matrix representations for working with tabular data
Implementation of Artificial Neural Networks using NumPy
Intrusion Detection System - IDS example using Dense, Conv1d and Lstm layers in Keras / TensorFlow
CP-APR Tensor Decomposition with PyTorch backend. pyCP_APR can perform non-negative Poisson Tensor Factorization on GPU, and includes an interface for anomaly detection using the extracted latent patterns.
Hand Gesture Recognition using CNNs and Perceptrons in realtime (OpenCV)
A Fast, Extensible Trainer and Extensions for Pytorch
colbert for dense retrieval, including multi view version, dureader-retrieval as an example
A simple code for creating your own custom layer in TensorFlow using Keras API. Here I have created the Dense layer same as the regular dense layer available in the Keras API.
Building Convolution Neural Networks from Scratch
Keras implementation of Mixed-scale Dense Net (MS-D Net) object segmentation
This Project is based on Neural Network to classify between Dogs and Cats.
A systematic study of ultra-tiny language models
Add a description, image, and links to the dense topic page so that developers can more easily learn about it.
To associate your repository with the dense topic, visit your repo's landing page and select "manage topics."