jdc08161063 Goto Github PK

memeproject

mentornet

Code for MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks

meta

A Modern C++ Data Sciences Toolkit

meta-learning-lstm

This repo contains the source code accompanying a scientific paper with the same name.

meta-learning-lstm-pytorch

pytorch implementation of Optimization as a Model for Few-shot Learning

metabev

MetaBEV: Solving Sensor Failures for BEV Detection and Map Segmentation

metacar

A reinforcement learning environment for self-driving cars in the browser.

metatron-discovery

Powerful & Easy way for big data discovery

meteornet

MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences (ICCV 2019 Oral)

metric-transfer.pytorch

Deep Metric Transfer for Label Propagation with Limited Annotated Data

mgc-django

Machine learning approach to classify music based on genres

mgcnn

Multi-Graph Convolutional Neural Networks

midas

Anomaly Detection on Dynamic (time-evolving) Graphs in Real-time and Streaming manner. Detecting intrusions (DoS and DDoS attacks), frauds, fake rating anomalies.

midlevel-reps

Code for the paper: Mid-Level Visual Representations Improve Generalization and Sample Efficiency for Learning Visuomotor Policies. More info on the website!

minerva

Meandering In Networks of Entities to Reach Verisimilar Answers

miniflow

Minimal implementation of TensorFlow

mish

Mish Deep Learning Activation Function for PyTorch / FastAI

mittens

A fast implementation of GloVe, with optional retrofitting

mix-and-match

Self-supervised Semantic Segmentation

ml-agents

Unity Machine Learning Agents Toolkit

ml-coursera-python-assignments

Python assignments for the machine learning class by andrew ng on coursera with complete submission for grading capability and re-written instructions.

ml-demos

Python code examples for the feedly Machine Learning blog (https://blog.feedly.com/category/all/Machine-Learning/)

ml-desnapify

ml-from-scratch

Machine Learning From Scratch. Bare bones Python implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from data mining to deep learning.

ml-nlp

此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。

ml-notes

notes about machine learning

ml-resources

ml-sound-classifier

Machine Learning Sound Classifier

ml-starter-pack

A collection of Machine Learning algorithms written from sctrach.

ml4a-guides

practical guides, tutorials, and code samples for ml4a