astorfi/Deep-Learning-Roadmapmaster 1 Branch Tags Code Folders and filesLatest commit astorfi Update README.rst bdb0add· Apr 21, 2020 History76 Commits .github Create FUNDING.yml Mar 3, 2020 _img/mainpage Add files via upload May 16, 2019...
:satellite: All You Need to Know About Deep Learning - A kick-starter - instillai/deep-learning-roadmap
路线图在Github上,地址是: GitHub - songrotek/Deep-Learning-Papers-Reading-Roadmap: Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech! 以下是截图: 希望对大家有所帮助!
Deep Learning Papers Reading Roadmap 地址: https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap?utm_source=tuicool&utm_medium=referral 参考: http://www.csdn.net/article/2015-06-01/2824811 1.1 survey Deep Learning 摘要 深度学习:允许拥有多个处理层的计算模型去学习拥有多层抽象的数据表示。
代码地址: https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap download.py的内容如下 from __future__ import print_function import os import re from six.moves.urllib.error importHTTPErrorimport shutil import argparse import mistune ...
Deep Learning综述[上] 最近打算深入了解一下深度学习,github上找到一个关于新手入门深度学习建议阅读论文的路线图。小白决定开始沿着路线图将paper逐个攻克,顺便写下自己的一些笔记,方便回顾。 Deep-Learning-Papers-Reading-Roadmap: [1] LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." (2015...
GitHub:fchollet/keras 5、songrotek/Deep-Learning-Papers-Reading-Roadmap Introduction Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech! GitHub:songrotek/Deep-Learning-Papers-Reading-Roadmap 6、Microsoft/CNTK ...
Loss Functions (StanfordCS231n) http://cs231n.github.io/neural-networks-2/#losses L1 vs. L2 Lossfunction (rishy.github.io) http://rishy.github.io/ml/2015/07/28/l1-vs-l2-loss/ The cross-entropy costfunction (neuralnetworksanddeeplearning.com) http://neuralnetworksanddeeplearning.com/chap...
Roadways: https://www.openstreetmap.org; population density: https://www.worldpop.org/. The sample data can be accessed at https://github.com/phygeograph/phygeographdata. Code availability Pytorch Geometric libraries (https://pytorch-geometric.readthedocs.io) were used to develop the graph deep...
DRL-based navigation has the advantages of solid learning ability and low dependence on sensor accuracy. The DRL algorithm, when integrating with a given agent, can replace the localization and map construction module, being able to move to the destination point, avoiding static or dynamic ...