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100-days-of-ml-code

100 Days of ML Coding

100-days-of-ml-code-1

100-Days-Of-ML-Code中文版

acme

A library of reinforcement learning components and agents

amc

[ECCV 2018] AMC: AutoML for Model Compression and Acceleration on Mobile Devices

awesome-emdl

Embedded and mobile deep learning research resources

awesome-model-compression-and-acceleration

a list of awesome papers on deep model ompression and acceleration

awesome-python

A curated list of awesome Python frameworks, libraries, software and resources

coursera-ml-andrewng-notes

吴恩达老师的机器学习课程个人笔记

cross-edge-computation-offloading

A python package of Cross-edge Computation Offloading (CCO) algorithm and its distributed version, Decor for Mobile Edge Computing (MEC).

cs231nassignment

Stanford CS231n assignment in 2019 spring

d2l-pytorch

This project reproduces the book Dive Into Deep Learning (www.d2l.ai), adapting the code from MXNet into PyTorch.

data-science-notes

数据科学的笔记以及资料搜集

dbhot

git实验

ddlo

Distributed Deep Learning-based Offloading for Mobile Edge Computing Networks

ddnn

deep-compression-alexnet

Deep Compression on AlexNet

deep-learning-21-examples

《21个项目玩转深度学习———基于TensorFlow的实践详解》配套代码

deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

deep-reinforcement-learning-hands-on

Hands-on Deep Reinforcement Learning, published by Packt

deepcompression-pytorch

Learning both Weights and Connections for Efficient Neural Networks https://arxiv.org/abs/1506.02626

deeplearning

deeplearning_ai_books

deeplearning.ai（吴恩达老师的深度学习课程笔记及资源）

dimensionality_reduction_alo_codes

PCA、LDA、MDS、LLE、TSNE等降维算法的python实现

distiller

Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research. https://intellabs.github.io/distiller

dive-into-dl-pytorch

本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。

droo

Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks

edge-intelligence

随着移动云计算和边缘计算的快速发展，以及人工智能的广泛应用，产生了边缘智能(Edge Intelligence)的概念。深度神经网络(例如CNN)已被广泛应用于移动智能应用程序中，但是移动设备有限的存储和计算资源无法满足深度神经网络计算的需求。神经网络压缩与加速技术可以加速神经网络的计算，例如剪枝、量化、卷积核分解等。但是这些技术在实际应用非常复杂，并且可能导致模型精度的下降。在移动云计算或边缘计算中，任务卸载技术可以突破移动终端的资源限制，减轻移动设备的计算负载并提高任务处理效率。通过任务卸载技术优化深度神经网络成为边缘智能研究中的新方向。Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge这篇文章提出了协同推断的**，将深度神经网络进行分区，一部分层在移动端计算，而另一部分在云端计算。根据硬件平台、无线网络以及服务器负载等因素实现动态分区，降低时延以及能耗。本项目给出了边缘智能方面的相关论文，并且给出了一个Python语言实现的卷积神经网络协同推断实验平台。关键词：边缘智能(Edge Intelligence)，计算卸载(Computing Offloading)，CNN模型分区(CNN Partition)，协同推断(Collaborative Inference)，移动云计算(Mobile Cloud Computing)

fate

An Industrial Level Federated Learning Framework

federated

A framework for implementing federated learning

federated-learning

Federated Learning - PyTorch