AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP
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May 9, 2021 - Python
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AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP
YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
Experience, Learn and Code the latest breakthrough innovations with Microsoft AI
Go package for computer vision using OpenCV 4 and beyond.
pytorch-kaldi is a project for developing state-of-the-art DNN/RNN hybrid speech recognition systems. The DNN part is managed by pytorch, while feature extraction, label computation, and decoding are performed with the kaldi toolkit.
Automatic Speech Recognition (ASR), Speaker Verification, Speech Synthesis, Text-to-Speech (TTS), Language Modelling, Singing Voice Synthesis (SVS), Voice Conversion (VC)
GUI for marking bounded boxes of objects in images for training neural network Yolo v3 and v2
NLP DNN Toolkit - Building Your NLP DNN Models Like Playing Lego
GNES is Generic Neural Elastic Search, a cloud-native semantic search system based on deep neural network.
DNN (formerly DotNetNuke) is the leading open source web content management platform (CMS) in the Microsoft ecosystem.
Voice activity detection (VAD) toolkit including DNN, bDNN, LSTM and ACAM based VAD. We also provide our directly recorded dataset.
Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving (ICCV, 2019)
RMDL: Random Multimodel Deep Learning for Classification
仅使用numpy从头开始实现神经网络,包括反向传播公式推导过程; numpy构建全连接层、卷积层、池化层、Flatten层;以及图像分类案例及精调网络案例等,持续更新中... ...
Optimized (for size and speed) Caffe lib for iOS and Android with out-of-the-box demo APP.
An Open Source Modular Framework From Face to FACS Based Avatar Animation (Unity3D / Blender)
Heterogeneous Run Time version of Caffe. Added heterogeneous capabilities to the Caffe, uses heterogeneous computing infrastructure framework to speed up Deep Learning on Arm-based heterogeneous embedded platform. It also retains all the features of the original Caffe architecture which users deploy their applications seamlessly.
HLS based Deep Neural Network Accelerator Library for Xilinx Ultrascale+ MPSoCs
Neural network-based singing voice synthesis library for research
LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks
Deep-Learning based CTR models implemented by PyTorch
MONeT framework for reducing memory consumption of DNN training
machine learning algorithm
An Open Source Deep Learning Inference Engine Based on FPGA
Open Toolkit for Painless Object Detection
Deep Neural Network for Speaker Count Estimation
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New Operator
Describe the operator
Why is this operator necessary? What does it accomplish?
This is a frequently used operator in
tensorflow/kerasCan this operator be constructed using existing onnx operators?
If so, why not add it as a function?
I don't know.
Is this operator used by any model currently? Which one?
Are you willing to contribute it?