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mobilenets-tensorflow

models

Models and examples built with TensorFlow

modl

Randomized online matrix factorization

motif-based-pagerank

mscelebfaceextract

Code for extracting images from MsCelebV1-Faces-Aligned.tsv

mtcnn-ncnn

mtcnn-pytorch

Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks

mtcnn_face_detection_alignment

Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural Networks

multinet

Real-time Joint Semantic Reasoning for Autonomous Driving

multisensory

Code for the paper: Audio-Visual Scene Analysis with Self-Supervised Multisensory Features

multiviewlrssc

murphy-machine-learning-a-probabilistic-perspective-errata-and-notes-4th-printing

Murphy's Machine Learning: A Probabilistic Perspective Errata (4th and later printings)

mv3d

Multi-View 3D Object Detection Network for Autonomous Driving

mvdata

Data sets for multi-view learning.

mvision

机器人视觉 无人驾驶 视觉SLAM ORB LSD SVO DSO 深度学习目标检测yolov3 行为检测 opencv PCL 双目视觉

mx-maskrcnn

An MXNet implementation of Mask R-CNN

mxnet-mtcnn

mtcnn mxnet C++ implementation

mxnet-oneclick

use mxnet to train your own data with just oneclick

narrativeqa

ncappzoo

Contains examples for the Movidius Neural Compute Stick.

ncnn

ncnn is a high-performance neural network inference framework optimized for the mobile platform

netmf

Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec

netron

Visualizer for deep learning and machine learning models

netsmf

NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization

network_enhancement

neural-networks-on-silicon

This is a collection of works on neural networks and neural accelerators.

neural-style-transfer-papers

:pencil2: Neural Style Transfer: A Review

nf-cce

Network Fusion for Composite Community Extraction

nimbro_picking

nlp-architect

NLP Architect by Intel AI Lab: A Python library for exploring the state-of-the-art deep learning topologies and techniques for natural language processing and natural language understanding