機器學習相關資源
Machine Learning of Computer Vision
Published in
4 min readNov 19, 2016
這些是我研究所時期搜集、整理的資源，雖然網路上已經有很多類似這樣的文章，不過資源再多都不嫌多，如果這篇文章能夠幫到正在從事相關研究或者單純有興趣的人，是我的榮幸！
Last updated Jan 29 2019
New found at that time
- 神經網路的設計模式：Deep Convolutional Neural Network Design Patterns、解析深度卷积神经网络的14种设计模式
- 2017 iT 邦幫忙鐵人賽：tensorflow 學習筆記
- 我們從李飛飛斯坦福CS231n課程講義裏，扒來了最全的計算機視覺術語表
- 动手学深度学习
- YOLO: Real-Time Object Detection
- Leonardo Araujo Dos Santos — Artificial Intelligence (Gitbook, Github)👍👍
- The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3)(Github)👍👍
- CS231n Convolutional Neural Networks for Visual Recognition
- Github repo: Code for the Make Your Own Neural Network book
- Rapid Tables — Mathematical Symbols
- Blog — Mr.Opengate(AI — Ch16.5 類神經網路實作 — TensorFlow 介紹與入門教學)
Frameworks
- Keras — Deep Learning library for Theano and TensorFlow(Github)
- Tensorflow (TFLearn) (playground) — 天蛇流XD
- Caffe2 — A New Lightweight, Modular, and Scalable Deep Learning Framework
- OpenFace — Free and open source face recognition with deep neural networks
- OpenCV3.1 — documentations
Research institutes
- Research at Google — DeepMind、Brain、Teachable Machine (Github)
- OpenAI
- Uber AI Lab— Michelangelo (TechOrange)
- Apple Machine Learning Journal
- Facebook Research (FAIR)
- NIPS Processings
- arXiv.org — Computer Vision and Pattern Recognition
- Distill — Machine Learning ResearchShould Be Clear, Dynamic and Vivid.Distill Is Here to Help.
Articles
- PyImageSearch — LeNet implementation
- PyImageSearch — OpenCV3 installation tutorial
- 從AWS搭建一個GPU運算環境來玩Tensorflow
- Series — Machine Learning is Fun!
Machine Learning is Fun! Part 2
Using Machine Learning to generate Super Mario Maker levels
medium.com
- 深度學習資料大全：從基礎到各種網絡模型
- 乾貨| 谷歌機器學習應用的四十三條經驗法則
- Perceptrons — the most basic form of a neural network
- Gradient Descent — Problem of Hiking Down a Mountain
- Why Momentum Really Works — Distill
- 机器学习敲门砖：任何人都能看懂的TensorFlow介绍 — 機器之心👍👍
- 2017年度盘点：15个最流行的GitHub机器学习项目 — 機器之心👍👍
Online Services
History of Machine Learning
- Geoffrey E. Hinton — Father of deep learning
- 人造特徵 — SIFT, HOG, Gabor
- 機器學習的衰頹興盛：從類神經網路到淺層學習
- 類神經網路的復興：深度學習簡史
- 神經網路的復興：重回風口的深度學習
Methodologies & Papers
- FaceNet(Google)
- DeepFace(Facebook)
- AlexNet — Imagenet classification with deep convolutional neural networks (Hinton)
- Reducing the dimensionality of data with neural networks(Hinton)
- Deep Convolutional GAN(Generative Adversarial Networks)
- CNN — RCNN — Fast-RCNN — Faster-RCNN
- LeNet(卷积神经网络Lenet-5实现)
- AlexNet (ILSVRC 2012 winner)(閱讀筆記、我看AlexNet、AlexNet論文翻譯、AlexNet模型)
- ZF Net (2013 winner)
- GoogLeNet (2014 winner)
- VGG model (2014 runner-up)
- ResNet (2015 winner)
- 機器學習算法常用指標總結
- LSTM(Long-short term memory)
- GAN/Deep Convolutional GAN/Conditional Gan
E-books
- NNDesign(Dropbox)
- Bishop — Pattern Recognition And Machine Learning(Dropbox)
- Python Machine Learning(Dropbox)
- Computer Vision: Algorithms and Applications(electronic drift)
- Neural networks and Deep Learning(online book)
- Digital video introduction(Github)
- Deep Learning: An MIT press book(Ian Goodfellow)(Dropbox)
Tutorials
Youtube
- 李宏毅(website, courses) — NTUEE ML 2016 👍👍👍
- 周莫煩(python, machine learing, deep learning, keras, CNN…)
- 林軒田 — 機器學習基石
- 林軒田 — 機器學習技法
- DeepLearning. TV
- Nervana — Convolutional Neural Networks
- Tutorial on Convolutional Neural Networks(CNNs) for image recognition
- How Convolutional Neural Networks work(Brandon Rohrer)👍
Udacity
Coursera
Others
- 孫民(Sun Min) — Research, Publications, awesomeCVpapers, 2017-toturials-pdf
- 深度學習深度學習 — DSC2016一天搞懂深度學習, Neural Networks and Deep Learning, Deep Learning(An MIT Press Book)
- THE STAR ALSO RISES — LeNet實作團
Mathematics
- 數學菜鳥的AI學習攻略：數學符號輕鬆入門
- 數值分析(政大MOOCS)
- KhanAcademy — Calculus
Conferences
- 谷歌云首席科学家李飞飞：人工智能 极客公园 2017 年 大会演讲
- Fei-Fei Li— How we’re teaching computers to understand pictures(TED)
- 李开复 未来十年人工智能趋势的崛起
- Python讓你的眼睛聽得見(PyCon APAC 2015)
- This app knows how you feel — from the look on your face(TED, Emotion recognition, Affectiva)
- GTC2016 — 以深度學習加速語音及影像辨識應用發展
- GTC2016 — 圖形處理器於腦部核磁共振影像處理應用
- GTC2016 — Learning from Dashcam Videos(Faster-RCNN, Min Sun Publications)
Blogs
- Marc Talk
- Alex’s Work
- Colah’s blog
- EasyAPI關注AI科技
- TensorFlow+Keras深度學習人工智慧實務應用
- wenjun’s blog
- Daniil’s blog — Machine Learning and Computer Vision artisan👍👍👍
- CH.Tseng👍👍👍
News
Others
- CS 143 Introduction to Computer Vision(Brown University)
- PyImageSearch — Computer Vision & OpenCV tutorials
- Quora — What are the best resources for learning computer vision?
- Hidden Markov Model(HMM)
- Formosa 5 是由國網中心自行建置的GPU運算平台主機，以叢集架構來提供GPU運算服務
