
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the facebook api being a prime example), so what was documented as a good way to do something at the time might be outdated when you read it (some pages here are over 15 years old).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 introHow 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 2007How 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 Pythonscikit 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 TensorflowTensorFlowhttp://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 2012Microsoft 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 tSNEhttp://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 pythonFuzzifying 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/densecapalternative to opencv, looks good, c++ Face detectionhttps://github.com/cmusatyalab/openfacehttps://bamos.github.io/2016/01/19/openface0.2.0/ Where are they looking? NLPhttps://github.com/oxfordcsdeepnlp2017/lecturescs224n NLP 2017 Python NLP package GeneralTensortalk, just links to AI stuff in a hackernews/reddit mannerUnderstanding 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 ageFooling networkshttp://www.evolvingai.org/foolingTheanoIntro 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 packageshttps://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 KerasCheck if using gpufrom tensorflow.python.client import device_lib print(device_lib.list_local_devices())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  OS X cd More programming related pages 
