Relevant Readable Links
|
Name |
Interesting topic |
Comment |
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Edwin Chen |
非参贝叶斯 |
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徐亦达老板 |
Dirichlet Process |
学习目标：Dirichlet Process, HDP, HDP-HMM, IBP, CRM |
|
Alex Kendall |
Geometry and Uncertainty in Deep Learning for Computer Vision |
语义分割 |
|
colah's blog |
Feature Visualization |
|
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Jason Yosinski |
Understanding Neural Networks Through Deep Visualization |
|
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田渊栋 |
general CV | |
|
alexisbcook |
Global Average Pooling Layers for Object Localization |
目标定位 |
|
Tombone |
http://www.computervisionblog.com/ |
DL, CV and the algorithms that are shaping the future of AI. |
Others：
http://www.cnblogs.com/tornadomeet/archive/2012/06/24/2560261.html【理论总结挺好】
http://www.cnblogs.com/charlotte77/【统计机器学习，可能实用】
http://blog.csdn.net/zhangjunhit/article/list/1【论文阅读笔记不错】
专注于数据分析之Kaggle and 图像处理之AR on phone
How to be a Top AR Full-Stack Developer
正如该链接中所言，学习了哪些知识，计算机视觉才算入门？
计算机视觉涉及面甚广，找到一类问题好好研究并实践就好，这类问题在本博客就指AR问题。
Ref: 计算机视觉入门书？
列出现代计算机视觉体系的主要科目（知识点）及其递进关系。
一个单元代表一门course (12 weeks)或者一本book (600 pages)的学习量，亲测。
循序渐进很重要，后辈务必去掉大跃进的念头。
| 人工智能之计算机视觉 - 学术体系 | ||||
| 第四层 | 计算机视觉：模型，学习，推理 | |||
| 第三层 | 统计机器学习 | 深度学习 | ||
| 第二层 | 机器学习入门 | 计算机视觉入门 | ||
| 第一层 | 统计推断 | 贝叶斯分析 | 多元线性分析 | 凸优化 |
编程是基本功，无须赘述。
| 人工智能之计算机视觉 - 软件工程 | ||||
| 第四层 | 实践！实践！实践！ | |||
| 第三层 | Android API, RN, OpenCV, Scikit-learning, ARToolkit, Unity | |||
| 第二层 | 软件架构，设计模式，代码管理，单元测试 | |||
| 第一层 | C/C++, Python, Java, Kotlin, Javascript, SQL | |||
如上，乃基本的学习路线，仅是参考，仍可细分，但基本上具备了AR全栈开发者的潜力。
Phones with ARCore support, Feb, 2018
Indoor navigation app: you'll never be lost again
Inside Navigation【好东西，但时机不对】
My Hierarchy of AI Knowledge
实践阶段
如果你想要一个能走到冰箱面前而不撞到墙壁的机器人，那就使用 SLAM。
如果你想要一个能识别冰箱中各种物品的机器人，那就使用 Deep Learning。
基本上，这算一个风口；仅指路，不领路，需深耕。
增强现实 - Deep Learning 识别
综述：
[Object Tracking] Overview of Object Tracking
[Object Tracking] Overview of algorithms for Object Tracking
轮廓识别：
[Object Tracking] Active contour model - Snake Model
[Object Tracking] Deep Boundary detection Tech
[Object Tracking] Contour Detection through Tensorflow running on smartphone
[Object Tracking] Contour Detection through OpenCV
目标定位：
[OpenCV] Real-time object detection with dnn module in OpenCV 3.3
[Localization] SSD - Single Shot MultiBoxDetector
[Localization] MobileNet with SSD
[Tensorflow] Android Meets TF in TensorFlow Dev Summit 2017
[Tensorflow] Object Detection API - prepare your training data
[Tensorflow] Object Detection API - build your training environment
[Tensorflow] Object Detection API - predict through your exclusive model
[Tensorflow] Object Detection API - retrain mobileNet
[Tensorflow] Object Detection API - mobileNet_v1.py
[Object Tracking] Identify and Track Specific Object
[Object Tracking] MeanShift
增强现实 - SLAM 跟踪
[SLAM] 01. "Simultaneous Localization and Mapping"
[SLAM] 02. Some basic algorithms of 3D reconstruction
[SLAM] 03. ORB-SLAM2
[SLAM] AR Tracking based on which tools?
[ARCORE, Continue...]
冲刺阶段
已看到收敛趋势，查缺补漏，攻克难点疑点。
融会贯通方可运用自如，解决新问题。
生成式网络 - Conv & Deconv
[Paper] Before GAN: sparse coding
Continue...
深度学习概念 - UFLDL
[UFLDL] Basic Concept
[UFLDL] Linear Regression & Classification
[UFLDL] Dimensionality Reduction
[UFLDL] Generative Model
[UFLDL] Sparse Representation
[UFLDL] ConvNet
[UFLDL] Train and Optimize
深度学习理论 - Stats 385
[Stats385] Lecture 01-02, warm up with some questions
[Stats385] Lecture 03, Harmonic Analysis of Deep CNN
[Stats385] Lecture 04: Convnets from Probabilistic Perspective
[Stats385] Lecture 05: Avoid the curse of dimensionality
【暂时不实用，点到为止】
统计机器学习 - PRML
| 9. [Bayesian] “我是bayesian我怕谁”系列 - Gaussian Process |
| 8. [Bayesian] “我是bayesian我怕谁”系列 - Variational Autoencoders |
| 7. [Bayesian] “我是bayesian我怕谁”系列 - Boltzmann Distribution |
| 6. [Bayesian] “我是bayesian我怕谁”系列 - Markov and Hidden Markov Models |
| 5. [Bayesian] “我是bayesian我怕谁”系列 - Continuous Latent Variables |
| 4. [Bayesian] “我是bayesian我怕谁”系列 - Variational Inference |
| 3. [Bayesian] “我是bayesian我怕谁”系列 - Latent Variables |
| 2. [Bayesian] “我是bayesian我怕谁”系列 - Exact Inference |
| 1. [Bayesian] “我是bayesian我怕谁”系列 - Naive Bayes with Prior |
混沌阶段
打地基，处于强化学习初期的不稳定阶段，感谢马尔科夫收敛的性质，目标已收敛；自下向上，基本遵循循序渐进的学习过程，夯实知识体系。
了解领域内的疑难点，认识技术细节的价值，为下一阶段做准备。
内容多为早年整理，倾向于参考价值。
Bayesian Analysis
R与采样方法：
[Bayes] What is Sampling
[Bayes] Point --> Line: Estimate "π" by R
[Bayes] Point --> Hist: Estimate "π" by R
[Bayes] qgamma & rgamma: Central Credible Interval
[Bayes] Hist & line: Reject Sampling and Importance Sampling
[Bayes] runif: Inversion Sampling
[Bayes] dchisq: Metropolis-Hastings Algorithm
[Bayes] prod: M-H: Independence Sampler for Posterior Sampling
[Bayes] Metroplis Algorithm --> Gibbs Sampling
[Bayes] Parameter estimation by Sampling
[Bayes] openBUGS: this is not the annoying bugs in programming
PGM基础：
[PGM] What is Probabalistic Graphical Models
[PGM] Bayes Network and Conditional Independence
[PGM] Temporal Models
[PGM] Markov Networks
[PGM] Variable elimination and Belief propagation
[PGM] Exact Inference for calculating marginal distribution
贝叶斯基础：
[BOOK] Applied Math and Machine Learning Basics
[Bayes] Multinomials and Dirichlet distribution
[Bayes] Understanding Bayes: A Look at the Likelihood
[Bayes] Understanding Bayes: Updating priors via the likelihood
[Bayes] Understanding Bayes: Visualization of the Bayes Factor
[Bayes] Why we prefer Gaussian Distribution
[Bayes] Improve HMM step by step
[Math] Unconstrained & Constrained Optimization
[Bayes] KL Divergence & Evidence Lower Bound
[Bayes] Variational Inference for Bayesian GMMs
[Bayes] Latent Gaussian Process Models
学习指南：
[Math] A love of late toward Mathematics - how to learn it?
[Bayes ML] This is Bayesian Machine Learning 【原文总结得相当好】
Deep Learning
理论：
[BOOK] Applied Math and Machine Learning Basics 【DL书基础，1至5章笔记】
[Hinton] Neural Networks for Machine Learning - Basic
[Hinton] Neural Networks for Machine Learning - Converage
[Hinton] Neural Networks for Machine Learning - RNN
[Hinton] Neural Networks for Machine Learning - Bayesian
[Hinton] Neural Networks for Machine Learning - Hopfield Nets and Boltzmann Machine
编程：
[Tensorflow] Architecture - Computational Graphs 【TF 框架】
[Tensorflow] Practice - The Tensorflow Way 【相对基础】
[Tensorflow] Cookbook - The Tensorflow Way 【前者的 Detail】
[Tensorflow] Cookbook - Neural Network 【代码基础写法】
[Tensorflow] Cookbook - CNN 【卷积网络专题】
[Tensorflow] Cookbook - Object Classification based on CIFAR-10
[Tensorflow] Cookbook - Retraining Existing CNNs models - Inception Model
[Tensorflow] RNN - 01. Spam Prediction with BasicRNNCell
[Tensorflow] RNN - 02. Movie Review Sentiment Prediction with LSTM
[Tensorflow] RNN - 03. MultiRNNCell for Digit Prediction
[Tensorflow] RNN - 04. Work with CNN for Text Classification
[TensorBoard] Cookbook - Tensorboard
[TensorBoard] Train and Test accuracy simultaneous tracking
[TensorBoard] Name & Variable scope
训练：
[Converge] Gradient Descent - Several solvers
[Converge] Weight Initialiser
[Converge] Backpropagation Algorithm 【BP实现细节】
[Converge] Feature Selection in training of Deep Learning 【特性相关性的影响】
[Converge] Training Neural Networks 【cs231n-lec5&6，推荐】
[Converge] Batch Normalisation
卷积：
[CNN] What is Convolutional Neural Network 【导论】
[CNN] Understanding Convolution 【图像角度理解】
[CNN] Tool - Deep Visualization
模型：
[Model] LeNet-5 by Keras
[Model] AlexNet
[Model] VGG16
[Model] GoogLeNet
[Model] ResNet
[Localization] R-CNN series for Localization and Detection
[Localization] YOLO: Real-Time Object Detection
[Localization] SSD - Single Shot MultiBoxDetector
[Localization] MobileNet with SSD
其他：
[GPU] CUDA for Deep Learning, why?
[GPU] DIY for Deep Learning Workstation
[Keras] Install and environment setting
[Keras] Develop Neural Network With Keras Step-By-Step
[GAN] *What is Generative networks 【导论，”生成式模型“有哪些，与”判别式模型“同级】
[GAN] How to use GAN - Meow Generator
[DQN] What is Deep Reinforcement Learning 【导论：此方向优先级低】
[Understanding] Compressive Sensing and Deep Model 【感知压缩，暂且不懂】
[DL] *Deep Learning for Industry - Wang Yi 【课外阅读】
Machine Learning
/* ML文件夹待整理 */
IR & NLP基础
检索：
[IR] Boolean retrieval
[IR] Index Construction
[IR] Compression
[IR] Tolerant Retrieval & Spelling Correction & Language Model
[IR] Probabilistic Model
[IR] Link Analysis
[IR] Ranking - top k
[IR] Evaluation
[IR] Information Extraction
[IR] Open Source Search Engines
[IR] Search Server - Sphinx
[IR] Concept Search and LSI
[IR] Concept Search and PLSA
[IR] Concept Search and LDA
压缩：
[IR] What is XML
[IR] XML Compression
[IR] Advanced XML Compression - ISX
[IR] Advanced XML Compression - XBW
[IR] XPath for Search Query
[IR] Graph Compression
[IR] Bigtable: A Distributed Storage System for Semi-Structured Data
[IR] Huffman Coding
[IR] Arithmetic Coding
[IR] Dictionary Coding
[IR] BWT+MTF+AC
[IR] String Matching
[IR] Suffix Trees and Suffix Arrays
[IR] Time and Space Efficiencies Analysis of Full-Text Index Techniques
[IR] Extraction-based Text Summarization
其他：
[IR] Word Embeddings
【以上内容需随recommended system一起再过一遍，完善体系】
AR基础
[Artoolkit] Marker Training
[Artoolkit] ARToolKit's SDK Structure on Android
[Artoolkit] Framework Analysis of nftSimple
[Artoolkit] kpmMatching & Tracking of nftSimple
[Artoolkit] Android Sample of nftSimple
[Artoolkit] Can I Use LGPL code for commercial application
[Artoolkit] Marker of nftSimple
[Artoolkit] ARSimpleNativeCarsProj for Multi Markers Tracking
[Unity3D] 01 - Try Unity3D
[Unity3D] 02 - ** Editor Scripting, Community Posts, Project Architecture
[Unity3D] 03 - Component of UI
[Unity3D] 04 - Event Manager
[Unity3D] 05 - Access to DB or AWS
【简单涉及3D建模知识点，非重点】
CV基础
概念：
[OpenCV] Install openCV in Qt Creator
[OpenCV] Basic data types - Matrix
[OpenCV] IplImage and Operation
[OpenCV] HighGUI
[OpenCV] Image Processing - Image Elementary Knowledge
[OpenCV] Image Processing - Grayscale Transform
[OpenCV] Image Processing - Frequency Domain Filtering
[OpenCV] Image Processing - Spatial Filtering
[OpenCV] Image Processing - Fuzzy Set
[OpenCV] Feature Extraction
[OpenCV] Feature Matching
[SLAM] Little about SLAM
[SLAM] Camera math knowledge
[Tango] Basic Knowledge
实践：
// 内容将合并，重新整理
[OpenCV] Samples 01: drawing【几何图案、文字等】
[OpenCV] Samples 02: [ML] kmeans【聚类算法】
[OpenCV] Samples 03: cout_mat【Mat计算能力】
[OpenCV] Samples 04: contours2【二值图案找轮廓】
[OpenCV] Samples 05: convexhull【散点的凸包轮廓】
[OpenCV] Samples 06: [ML] logistic regression【线性二分类】
[OpenCV] Samples 07: create_mask【鼠标圈图】
[OpenCV] Samples 08: edge【边缘检测】
[OpenCV] Samples 09: plImage <==> Mat【色域通道分离】
[OpenCV] Samples 10: imagelist_creator【图片地址list参数】
[OpenCV] Samples 11: image sequence【视频流提取】
[OpenCV] Samples 12: laplace【视频流处理】
[OpenCV] Samples 13: opencv_version【版本信息显示】
[OpenCV] Samples 14: kalman filter【预测下一个状态】
[OpenCV] Samples 15: Background Subtraction and Gaussian mixture models【背景差分】
[OpenCV] Samples 16: Decompose and Analyse RGB channels【色域通道分离】
[OpenCV] Samples 17: Floodfill【聚类算法】
[OpenCV] Samples 18: Load image and check its attributes【图片属性】
扩展：
[CNN] Face Detection
[Android Studio] Using Java to call OpenCV
[Android Studio] Using NDK to call OpenCV
[OpenCV] Install OpenCV 3.3 with DNN
[OpenCV] Install OpenCV 3.4 with DNN
趣码收集：
[Link] Face Swap Collection
[Link] Face Swap without DLIB【代码可用】
算法基础
[Algorithm] Deferred Acceptance Algorithm
[Algorithm] Beating the Binary Search algorithm – Interpolation Search, Galloping Search
[Algorithm] Warm-up puzzles
[Algorithm] Asymptotic Growth Rate
[Algorithm] Polynomial and FFT
[Algorithm] Maximum Flow
[Algorithm] String Matching and Hashing
[Optimization] Greedy method
[Optimization] Dynamic programming
[Optimization] Advanced Dynamic programming
Everything here starts from 2016
