DeepLearning List

Overview

• 2017-The Deep Learning Roadmap.

• 2017-深度学习简史：一部深度学习的简史，虽然不那么波澜壮阔，但其发展也是一波三折，艰难而行。随着 AlphaGo 掀起的人工智能新浪潮，人工智能的春天才刚刚开始。希望本文为大家了解深度学习的历史，理解深度学习的本质带来帮助。

• 我如何理解深度学习？

• 有趣的机器学习概念纵览：从多元拟合，神经网络到深度学习，给每个感兴趣的人

• 深层学习为何要 “Deep”(上)

• 程序员的深度学习入门指南: 来自费良宏在 2016QCon 全球软件开发大会(上海)上的演讲。

• 2017- 深度神经网络全面概述：从基本概念到实际模型和硬件基础：深度神经网络(DNN )所代表的人工智能技术被认为是这一次技术变革的基石(之一)。近日，由 IEEE Fellow Joel Emer 领导的一个团队发布了一篇题为《深度神经网络的有效处理：教程和调研(Efficient Processing of Deep Neural Networks: A Tutorial and Survey )》的综述论文，从算法、模型、硬件和架构等多个角度对深度神经网络进行了较为全面的梳理和总结。。

• Deep Learning 101 #Series#: The Deep Learning 101 series is a companion piece to a talk given as part of the Department of Biomedical Informatics @ Harvard Medical School ‘Open Insights’ series. Slides for the talk are available here

• 深度学习：2016 年的进展综述及 2017 年的预测

• sjchoi86-深度学习指导: 一系列对于深度学习入门指导的 PPT

• 【直观梳理深度学习关键概念】优化算法、调参基本思路、正则化方式等

• 2018-什么是深度学习？: 45 分钟理解深度神经网络和深度学习。

• 2019-The Decade of Deep Learning: This post is an overview of some the most influential Deep Learning papers of the last decade. My hope is to provide a jumping-off point into many disparate areas of Deep Learning by providing succinct and dense summaries that go slightly deeper than a surface level exposition, with many references to the relevant resources.

Review

• 2016-Deep Learning 2016: The Year in Review

• The limitations of deep learning: This post is targeted at people who already have significant experience with deep learning (e.g. people who have read chapters 1 through 8 of the book). We assume a lot of pre-existing knowledge.

• 2017-Deep Learning Achievements Over the Past Year: Great developments in text, voice, and computer vision technologies.

• 2019-Deep Learning:State of the Art (2019): Breakthrough Developments in 2017 & 2018

Case Study

• 2017-The-Terrible-Deep-Learning-List: 15 working examples to get you started with Deep Learning without learning any of the math.

• Miles Deep - AI Porn Video Editor

Resource

Collection

• Deep Learning for Noobs [Part 1]

• 126 篇殿堂级深度学习论文分类整理-从入门到应用：昵称为 songrotek 的学霸在 GitHub 发布了他整理的深度学习路线图，分门别类梳理了新入门者最需要学习的 DL 论文，又按重要程度给每篇论文打上星星。

• Theories of Deep Learning (STATS 385) Stanford University, Fall 2017

Others: 其他

Tutorial

• 李宏毅: Deep Learning Tutorial:300 多页的 PPT 深入浅出地介绍了深度学习的基本概念。

• 2019-The Matrix Calculus You Need For Deep Learning: This paper is an attempt to explain all the matrix calculus you need in order to understand the training of deep neural networks.