Disclaimer:
These pages about different languages / apis / best practices were mostly jotted down quckily and rarely corrected afterwards.
The languages / apis / best practices may have changed over time (e.g. the facebook api being a prime example),
so what was documented as a good way to do something at the time might be outdated when you read it (some pages here are over 15 years old).
Just as a reminder.
Machine LearningKmeans clusteringClustering intro How to find how many clusters (if you don know what you want)
No easy way to get it.
You might know in advance, e.g. cluster by digits 09 means you want 10 clusters.
Could run algorithm for differnt values of K, K=2,3,...
And compare variance (how many are in each cluster?), and pick the one with the smallest variance.
Problem: is ideal when K=n (i.e. one in each cluster)
In general, the more clusters you add, the lower the variance is going to be.
Look at it visually in scree plot. Pick the K where the mountain ends and the rubble begins, i.e. less drastic changes:
maximize 2nd derivate of V: point where rate of decline changes the most.
Meanshift
Meanshift, can be used to track objects between frames
Neural networksGoogle Tech Talks  November, 29 2007 How convolutional neural networks see the world Training and investigating Residual Nets Neural networks for computer vision The Back Propagation Algorithm A Beginner's Guide To Understanding Convolutional Neural Networks Trained image classification models for Keras Neural Network Architectures
Toolsscikitlearn: Machine Learning in Python scikit tutorial PyHubs is a machine learning library developed in Python. It contains implementations of hubnessaware machine learning algorithms together with some useful tools for machine learning experiments. Curated list of machine learning frameworks, libraries and software
TensorflowTensorFlow http://playground.tensorflow.org/ KMeans Clustering with TensorFlow Improvement on that Kmeans example Other kmeans implmenetation Tensorflow sample code TensorFlow Implementation of Deep Convolutional Generative Adversarial Networks CS224D Lecture 7  Introduction to TensorFlow (19th Apr 2016) https://github.com/aymericdamien/TensorFlowExamples RNNS IN TENSORFLOW, A PRACTICAL GUIDE AND UNDOCUMENTED FEATURES
Videos/LecturesCaltech machine learning 2012 Microsoft Research: Deep Learning 7 December 2015 Deep Learning course by Google Neural Networks Demystified Lecture Lecture python numpy cs231n Andrej Karpathy 2016 UC Berkeley CS188 Intro to AI cs189 cs294112 berkeley deeplearning
tSNEVisualizing data using tSNE http://scikitlearn.org/stable/auto_examples/manifold/plot_lle_digits.html
word2vec and similarImplementing Conceptual Search in Solr using LSA and Word2Vec: Presented by Simon Hughes, Dice.com (Oct 2015) http://multithreaded.stitchfix.com/blog/2015/03/11/wordisworthathousandvectors/ http://eng.kifi.com/fromword2vectodoc2vecanapproachdrivenbychineserestaurantprocess/ https://github.com/dav/word2vec This is an implementation of the LexVec word embedding model (similar to word2vec and GloVe) that achieves state of the art results in multiple NLP tasks
ClusteringFuzzy kmeans in python Fuzzifying clustering algorithms: The case study of MajorClust http://pythonhosted.org/scikitfuzzy/auto_examples/plot_cmeans.html http://stackoverflow.com/questions/6736347/isafuzzycmeansalgorithmavailableforpython https://pypi.python.org/pypi/scikitfuzzy Joint Unsupervised Learning of Deep Representations and Image Clusters (2016) https://github.com/jwyang/jointunsupervisedlearning
Image classificationhttps://github.com/jcjohnson/densecap alternative to opencv, looks good, c++
Face detectionhttps://github.com/cmusatyalab/openface https://bamos.github.io/2016/01/19/openface0.2.0/ Where are they looking?
NLPhttps://github.com/oxfordcsdeepnlp2017/lectures cs224n NLP 2017 Python NLP package
GeneralTensortalk, just links to AI stuff in a hackernews/reddit manner Understanding Aesthetics with Deep Learning Netflix: extracting interesting regions/text, how they define simularity Starting points for deep learning and RNN https://github.com/kjw0612/awesomernn Approaching (Almost) Any Machine Learning Problem (2016) DeepWarp: Photorealistic Image Resynthesis for Gaze Manipulation (in ECCV'16) http://www.efimovml.com https://blog.insightdatascience.com/graphbasedmachinelearning6e2bd8926a0 http://news.efinancialcareers.com/uken/285249/machinelearningandbigdatajpmorgan/
General AI booksSecond machine age
Fooling networkshttp://www.evolvingai.org/fooling
TheanoIntro to Deep Learning with Theano and OpenDeep by Markus Beissinger (2015)
Pytorchhttp://blog.outcome.io/pytorchquickstartclassifyinganimage/
Nearest neigbhor packagesOverview of theory and pros/cons of some packages https://github.com/lyst/rpforest https://github.com/spotify/annoy http://www.cs.ubc.ca/research/flann/ https://github.com/primetang/pyflann https://github.com/mariusmuja/flann https://github.com/falconnlib/falconn https://github.com/yahoo/lopq http://www.kgraph.org/index.php?n=Main.Home https://github.com/hdidx/hdidx http://mmp2.github.io/megaman/ https://arxiv.org/pdf/1603.02763.pdf https://github.com/kayzhu/LSHash http://ryanrhymes.github.io/panns/
Installing cunn:
luarocks install cunn
libcunnx.so malformed object
http://stackoverflow.com/questions/26822010/installnametoolmalformedobjectloadcommand23cmdsizeiszeromacosx
th e "require 'cutorch'; print(cutorch)"
luajit l libcutorch
problem loading torch/install/lib/lua/5.1/libcutorch.so:
https://github.com/torch/cutorch/issues/243
http://stackoverflow.com/questions/36312018/unabletoimportrequirecutorchintorch
http://ubuntuforums.org/showthread.php?t=2264359
https://github.com/torch/cutorch/issues/244
https://github.com/torch/cutorch/issues/126
https://tryolabs.com/blog/2013/03/25/whyaccuracyalonebadmeasureclassificationtasksandwhatwecandoaboutit/
accuracy = "how high percentage of data set did it get right"
precision = "how relevant was the result returned, i.e. were there false positives"
recall = "how high percentage of the relevant result did it return, ignore any false positives"
Accuracy = (true positives + true negatives) / (total examples)
Precision = (true positives) / (true positives + false positives)
Recall = (true positives) / (true positives + false negatives)
F1 score = (2 * precision * recall) / (precision + recall)
Misclassification Rate = (FP + FN) / total
(microsoft definition)
TPR = TP / (TP + FP)
FPR = FN / (TN + FN)
For multi classification:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
where TN is defined as "all items that should have been classified in all other classes MINUS False Positives")
Precision (micro) = sum(all TP) / (sum(all TP) + sum(all TN))
Precision (macro) = sum(precision for each class) / numclasses
F1 micro/macro in the same way
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