Theory, Books, Papers of Deep Learning DL

Theory, Books, Papers of Deep Learning DL

2017-03-10. Category & Tags: Deep Learning, DL, Theory

See also:

  • /ml-books (Learning Machine Learning, ML Books & Codes)
  • /ml-understand: ML Understandability / Interpratation / Comprehensibility & Causality
  • /dl-do-it: Deep Learning Hands On

如何理解神经网络 #

Courses #

// DataCamp.com 优点：一点一点学，一步一步带着做

  • 深度学习初级 ( w/ pyTorch )：https://app.datacamp.com/learn/courses/introduction-to-deep-learning-with-pytorch
  • 深度学习中级 ( w/ pyTorch )：https://app.datacamp.com/learn/courses/intermediate-deep-learning-with-pytorch
  • deeplearning.ai+ 网易云课堂公开课： https://mooc.study.163.com/smartSpec/detail/1001319001.htm （包括以下 5 个部分，建议按照顺序学）
    1. NN & DL 神经网络和深度学习
    2. Improving DL Nets 改善深层神经网络：超参数调试、正则化以及优化
    3. Structuring Machine Learning Projects 结构化机器学习项目（构建和运用 ML 项目的经验，少走弯路）
    4. CNN 卷积神经网络
    5. Sequence Models 序列模型

MIXED TUTORIAL + TRIKCS #

DEEP LEARNING BOOKS ( & CODES ) #

  • Yoshua Bengio 和 GAN 之父 Ian Goodfellow 等人合著的 Deep Learning: 中文版 on github (pan.baidu), 英文版 github (pan.baidu).
  • DL With Python, by Francois Chollet (also the author of Keras). Detailed introduction of CNN & RNN on CV & text analysis in Keras.
  • DL for CV with Python [//trilogy], by Adrian Rosebrock.
  • Hands on ML with SL and TF [//genius for clarity], by Aurélien Géron.
  • DL - A Practioner’s Approach, [java], by Adam Gibson & Josh Patterson.
  • DL with Python, by Jason Brownlee. [//with code, easy to understand]
  • Practical Python and Open CV, by Adrian Rosebrock. [// fast into CV]
  • DL with TF, by Giancarlo Zaccone, [// DL 101].

ref(cn)

PAPERS, TERMS & DEFINATIONS #

ToC: 一 ~ 三、概述, 背景, 人脑视觉机理 四、关于特征: 特征表示的粒度; 初级（浅层）特征表示; 结构性特征表示; 需要有多少个特征？ 五 ~ 七、Deep Learning 的基本思想 (vs. Shallow Learning), Neural Network 八、Deep learning 训练过程: 传统神经网络 vs. deep learning 九、Deep Learning 的常用模型或者方法: AutoEncoder 自动编码器; Sparse Coding 稀疏编码; Restricted Boltzmann Machine(RBM) 受限波尔兹曼机; Deep BeliefNetworks 深信度网络; Convolutional Neural Networks 卷积神经网络 十、总结与展望 十一、参考文献和 Deep Learning 学习资源

EBOOK #

dive into deep learning (pyTorch), 动手学深度学习（PyTorch 版）中文版 #

by 阿斯顿·张（Aston Zhang），扎卡里·C.立顿（Zachary C. Lipton）， 李沐（Mu Li），亚历山大·J.斯莫拉（Alexander J. Smola），人民邮电出版社，ISBN 9787115600820，2026-02。 image.png.

Deep Learning, 深度学习 中文版 #

by Ian Goodfellow (伊恩·古德费洛)、Yoshua Bengio (约书亚·本吉奥)、Aaron Courville (亚伦·库维尔). Deep Learning 中文版 深度学习, “AI 圣经”, ISBN 978-7115461476, 人民邮电出版社， 2017-01-01. Github 开源. image.png.

邱锡鹏 教授 2020 神经网络与深度学习 #

Keywords: CNN, RNN, Attention, Gaussian Mix., RBM, DBN, GAN, RL, TF, PyTorch: equation, pseudocode, exercise … PDF, PPT, code, exercise & solution, etc.

python data science handbook - 2017 #

[easy to find common examples] Jupyter Notebooks for the Python Data Science Handbook

stanford deep learning tutorial [mooc] #

This tutorial will teach you the main ideas of Unsupervised Feature Learning and Deep Learning. By working through it, you will also get to implement several feature learning/deep learning algorithms, get to see them work for yourself, and learn how to apply/adapt these ideas to new problems.

mathematics for computer science – mit 麻省理工的英文原版教材 #

see also /math

tensorflow cook book #

Nick McClure - TensorFlow Machine Learning Cookbook: code, (similar simple cn cases)

datascience from scratch, by Joel Grus, 2014 #

（见 (/ml-books)[/ml-books]）

周志华 机器学习 2016 西瓜书 #

（见 (/ml-books)[/ml-books]）

the deep learning textbook - 2015 #

It helps students and practitioners enter the field of machine learning in general and deep learning in particular.

refs #

机器学习资源 Machine learning Resources | MachineLearning, (bak)

TOOLS #

VISDOM: DATA VISUALIZATION #

For Torch/Numpy, by Facebook. github A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Torch and Numpy.'

TALKS & PRESENTATIONS #

< machine learning: testing and error metrics > #

model selection & improvement. by Luis Serrano Udacity @ youtube

weight Initialization in deep learning #

by 夏飞@google LeiPhone

GAN FAQ #

Generative Adversarial Networks (GANs), Some Open Questions/problems by Sanjeev Arora. See their next blog for solutions.

See sections below for GAN details.

多源数据融合与时空数据挖掘 #

微软研究院资深主任研究员郑宇 （一） (evernote)

Practical Deep Learning For Coders Part 1 #

Taught by Jeremy Howard (Kaggle’s #1 competitor 2 years running, and founder of Enlitic). fast.ai

CNN #

CNN, ImageNet: CNN 浅析和历年 ImageNet 冠军模型解析 How do Convolutional Neural Networks work? cn Insights into AlexNet CNN explained, with python, analyticsvidhya 2016 CNN 图像分割简史：从 R-CNN 到 Mask R-CNN

cnn visualization/comprehensibility #

一位中国博士把整个 CNN 都给可视化了，可交互有细节，每次卷积 ReLU 池化都清清楚楚 来源：量子位 2020-05 Netscope CNN Analyzer 2017-11: A web-based tool for visualizing and analyzing convolutional neural network architectures (or technically, any directed acyclic graph). Currently supports Caffe’s prototxt format. 深度学习调参入门，有哪些技巧？ AI 科技评论 2017-03 TensorBoard in “Insights into AlexNet”

OTHER KEYWORDS in GITHUB #

“awesome deep learning” “awesom deep learning papers” " 图解 TensorFlow 架构与设计 "

邓仰东专栏｜机器学习的那些事儿 #

目录 1.绪论 1.1.概述 1.2 机器学习简史 1.3 机器学习改变世界：基于 GPU 的机器学习实例 1.3.1 基于深度神经网络的视觉识别 1.3.2 AlphaGO 1.3.3 IBM Waston 1.4 机器学习方法分类和本书组织 1.3 机器学习改变世界：基于 GPU 的机器学习实例 机器学习技术正在不断取得举世瞩目的成就，这一节会介绍三个机器学习的成功案例，让大家体会机器学习技术怎样解决极度挑战性的实际问题。 1.3.1 基于深度神经网络的视觉识别

GAN #

GANs - the story so far, 2019-06, (bak) (cn, (cn bak)): GAN, DCGAN, CGAN, CycleGAN, CoGAN, ProGAN, WGAN, SAGAN, BigGAN, StyleGAN.

yipansansha 一盘散沙的工作间, slack：DeepThinkInPy, papers

一文帮你发现各种出色的 GAN 变体

生成式对抗网络资料荟萃（原理/教程/报告/论文/实战/资料库） (bak)

IMAGE CONTENT SEARCH #

TensorFlow TF + Elasticsearch 实现全文的图片搜索（附源代码）

从 R-CNN 到 RFBNet，目标检测架构 5 年演进全盘点

RNN #

TF voice 使用 TensorFlow 构建、训练和改进循环神经网络

Using Fast Weights to Attend to the Recent 亮点：在 Short-Term Memory、Long-Term Memory 以及 LSTM 之外，有什么更好的 Attention 机制呢？ 多伦多大学。

GENERAL GREAT #

MIT Efficient Processing of Deep Neural Networks: A Tutorial and Survey

如何为你的深度学习任务挑选最合适的 GPU?

RESIDUAL NET #

Residual Networks Behave Like Ensembles of Relatively Shallow Network + 康奈尔

OTHERS #

Deep learning mooc open course @ Hugo Larochelle DL 小钢炮攒机心得 | 帮你踩坑 control keras in WeChat

HARDWARE #

Colab 提供了免费 TPU，机器之心帮你试了试