{"id":390,"date":"2017-09-12T21:17:37","date_gmt":"2017-09-12T21:17:37","guid":{"rendered":"http:\/\/eic.cefet-rj.br\/~ebezerra\/?page_id=390"},"modified":"2017-10-25T08:39:53","modified_gmt":"2017-10-25T08:39:53","slug":"am-complementar","status":"publish","type":"page","link":"https:\/\/eic.cefet-rj.br\/~ebezerra\/am-complementar\/","title":{"rendered":"Aprendizado de M\u00e1quina &#8211; material complementar"},"content":{"rendered":"<h3>Aula 01 &#8211; Introdu\u00e7\u00e3o ao Aprendizado de M\u00e1quina; Regress\u00e3o Linear (uma vari\u00e1vel)<\/h3>\n<ul>\n<li><a href=\"https:\/\/www.technologyreview.com\/s\/603944\/microsoft-ai-isnt-yet-adaptable-enough-to-help-businesses\/\">Microsoft: AI Isn\u2019t Yet Adaptable Enough to Help Businesses<\/a><br \/>\n<a href=\"https:\/\/www.bloomberg.com\/news\/articles\/2016-11-10\/hedge-funds-beware-most-machine-learning-talk-is-really-hokum\">Why Machines Still Can\u2019t Learn So Good<\/a><\/li>\n<li><a href=\"https:\/\/www.projectarm.com\/image-recognition-what-it-is\/\">Image Recognition: A Short History and All You Need to Know About It<\/a><\/li>\n<li>Informa\u00e7\u00f5es sobre Arthur Samuel: <a href=\"https:\/\/history-computer.com\/ModernComputer\/thinkers\/Samuel.html\">aqui<\/a> e <a href=\"http:\/\/sumnerandscott.com\/arthur-samuel\/\">aqui<\/a><\/li>\n<li><a href=\"http:\/\/www.cs.cmu.edu\/~tom\/\">Informa\u00e7\u00f5es sobre Tom Mitchell<\/a><\/li>\n<li><a href=\"http:\/\/www.artificialbrain.xyz\/whoever-controls-machine-learning-controls-the-future\/\">Whoever Controls Machine Learning Controls The Future<\/a><\/li>\n<li><a href=\"https:\/\/www.technologyreview.com\/s\/608911\/is-ai-riding-a-one-trick-pony\/\">Is AI Riding a One-Trick Pony? (September 29, 2017)<\/a><\/li>\n<\/ul>\n<h3>Aula 02 &#8211;\u00a0Regress\u00e3o Linear (v\u00e1rias vari\u00e1veis); Regress\u00e3o Log\u00edstica<\/h3>\n<ul>\n<li><a href=\"http:\/\/eic.cefet-rj.br\/~ebezerra\/wp-content\/uploads\/2017\/09\/derivacao-reg-log.pdf\">Deriva\u00e7\u00e3o apresentada em aula do fun\u00e7\u00e3o hip\u00f3tese para a regress\u00e3o log\u00edstica<\/a><\/li>\n<li><a href=\"https:\/\/stats.stackexchange.com\/questions\/278771\/how-is-the-cost-function-from-logistic-regression-derivated\">Deriva\u00e7\u00e3o da fun\u00e7\u00e3o de custo para a regress\u00e3o log\u00edstica<\/a><\/li>\n<li><a href=\"https:\/\/math.stackexchange.com\/questions\/477207\/derivative-of-cost-function-for-logistic-regression\">Outra vers\u00e3o da deriva\u00e7\u00e3o da fun\u00e7\u00e3o de custo para a regress\u00e3o log\u00edstica<\/a><\/li>\n<li><a href=\"http:\/\/adit.io\/posts\/2016-03-13-Logistic-Regression.html\">Texto introdut\u00f3rio sobre regress\u00e3o log\u00edstica<\/a><\/li>\n<li><a href=\"http:\/\/eic.cefet-rj.br\/~ebezerra\/wp-content\/uploads\/2017\/07\/minicurso-python.zip\">Tutorial: Python<\/a><\/li>\n<\/ul>\n<h3>Aula 03 &#8211; Regulariza\u00e7\u00e3o;\u00a0Refinamento de Algoritmos de Aprendizado de M\u00e1quina<\/h3>\n<ul>\n<li><a href=\"http:\/\/shookrun.com\/documents\/stupidmining.pdf\">Stupid Data Miner Tricks (texto com descri\u00e7\u00e3o acerca do sobreajuste)<\/a><\/li>\n<li><a href=\"https:\/\/www.quora.com\/What-is-regularization-in-machine-learning\">Quora: What is Regularization in ML?<\/a><\/li>\n<li><a href=\"https:\/\/machinelearningmastery.com\/overfitting-and-underfitting-with-machine-learning-algorithms\/\">Overfitting and Underfitting With Machine Learning Algorithms<\/a><\/li>\n<li><a href=\"http:\/\/blog.lokad.com\/journal\/2009\/4\/22\/overfitting-when-accuracy-measure-goes-wrong.html\">Overfitting: when accuracy measure goes wrong<\/a><\/li>\n<li><a href=\"https:\/\/stats.stackexchange.com\/questions\/153605\/no-regularisation-term-for-bias-unit-in-neural-network\">Explica\u00e7\u00e3o acerca de n\u00e3o ser necess\u00e1ria a regulariza\u00e7\u00e3o de theta_0<\/a><\/li>\n<li><a href=\"http:\/\/scott.fortmann-roe.com\/docs\/BiasVariance.html\">Understanding the Bias-Variance Tradeoff<\/a><\/li>\n<li><a href=\"http:\/\/scott.fortmann-roe.com\/docs\/MeasuringError.html\">Accurately Measuring Model Prediction Error<\/a><\/li>\n<li><a href=\"https:\/\/stats.stackexchange.com\/questions\/187335\/validation-error-less-than-training-error\">Validation Error less than training error?<\/a><\/li>\n<li><a href=\"https:\/\/sebastianraschka.com\/blog\/2016\/model-evaluation-selection-part1.html\">Model evaluation, model selection, and algorithm selection in machine learning<\/a><\/li>\n<li><a href=\"https:\/\/florianhartl.com\/thoughts-on-machine-learning-dealing-with-skewed-classes.html\">Thoughts on Machine Learning \u2013 Dealing with Skewed Classes<\/a><\/li>\n<li><a href=\"http:\/\/eic.cefet-rj.br\/~ebezerra\/wp-content\/uploads\/2017\/09\/Python2BBasics2BWith2BNumpy2Bv3.html\">Tutorial: NumPy<\/a><\/li>\n<\/ul>\n<h3>Aula 04 &#8211; Agrupamento (k-means); Redu\u00e7\u00e3o de Dimensionalidade (PCA)<\/h3>\n<ul>\n<li><a href=\"https:\/\/stats.stackexchange.com\/questions\/2691\/making-sense-of-principal-component-analysis-eigenvectors-eigenvalues\">Making sense of principal component analysis, eigenvectors &amp; eigenvalues<\/a><\/li>\n<li><a href=\"https:\/\/www.quora.com\/What-is-an-eigenvector-of-a-covariance-matrix\">What is an eigenvector of a covariance matrix<\/a><\/li>\n<li><a href=\"https:\/\/stackoverflow.com\/questions\/26444525\/how-do-i-calculate-the-covariance-matrix-without-any-built-in-functions-or-loops\">How do I calculate the covariance matrix without any built-in functions or loops in MATLAB?<\/a><\/li>\n<li><a href=\"http:\/\/www.cse.psu.edu\/~rtc12\/CSE586Spring2010\/lectures\/pcaLectureShort_6pp.pdf\">Principal Components Analysis (lecture)<\/a><\/li>\n<li><a href=\"http:\/\/cs229.stanford.edu\/notes\/cs229-notes10.pdf\">CS229: Principal components analysis<\/a><\/li>\n<li><a href=\"https:\/\/stackoverflow.com\/questions\/15317822\/calculating-covariance-with-python-and-numpy\">Calculating Covariance with Python and Numpy<\/a><\/li>\n<li><a href=\"http:\/\/www.itl.nist.gov\/div898\/handbook\/pmc\/section5\/pmc541.htm\">Mean Vector and Covariance Matrix<\/a><\/li>\n<li><a href=\"https:\/\/mubaris.com\/2017-10-01\/kmeans-clustering-in-python\">K-Means Clustering in Python<\/a><\/li>\n<\/ul>\n<h3>Aula 05 &#8211; Detec\u00e7\u00e3o de Anomalias<\/h3>\n<ul>\n<li><a href=\"https:\/\/stats.stackexchange.com\/questions\/246418\/can-anomaly-detection-work-without-the-assumption-of-normal-distribution-of-the\">Can anomaly detection work without the assumption of Normal Distribution of the underlying data?<\/a><br \/>\n<a href=\"https:\/\/www.statlect.com\/fundamentals-of-statistics\/normal-distribution-maximum-likelihood\">Normal distribution &#8211; Maximum Likelihood Estimation<\/a><\/li>\n<li><a href=\"https:\/\/anomaly.io\/anomaly-detection-normal-distribution\/\">Anomaly detection with the normal distribution<\/a><\/li>\n<li><a href=\"http:\/\/www.sigmamagic.com\/forum\/archives\/297\">Transform Data to Normal Distribution<\/a><\/li>\n<li><a href=\"https:\/\/docs.scipy.org\/doc\/scipy\/reference\/generated\/scipy.stats.boxcox.html\">scipy.stats.boxcox<\/a><\/li>\n<li><a href=\"https:\/\/docs.scipy.org\/doc\/numpy\/reference\/generated\/numpy.linalg.det.html\">Como computar o determinante de uma matriz com numpy<\/a><\/li>\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/Power_transform#Box.E2.80.93Cox_transformation\">Power transform (Wikpedia)<\/a><\/li>\n<li><a href=\"https:\/\/www.isixsigma.com\/tools-templates\/normality\/tips-recognizing-and-transforming-non-normal-data\/\">Tips for Recognizing and Transforming Non-normal Data<\/a><\/li>\n<li><a href=\"http:\/\/shahramabyari.com\/2015\/12\/21\/data-preparation-for-predictive-modeling-resolving-skewness\/\">Data Preparation for Predictive Modeling: Resolving Skewness<\/a><\/li>\n<li><a href=\"https:\/\/onlinecourses.science.psu.edu\/stat414\/node\/191\">MLE<\/a><\/li>\n<li><a href=\"http:\/\/eic.cefet-rj.br\/~ebezerra\/wp-content\/uploads\/2017\/09\/mle-mu-sigma.pdf\">Prova relativa \u00e0 estima\u00e7\u00e3o por m\u00e1xima verossimilhan\u00e7a apresentada em aula<\/a><\/li>\n<\/ul>\n<h3>Aula 06 &#8211; Sistemas de Recomenda\u00e7\u00e3o; AM &amp; Big Data<\/h3>\n<ul>\n<li><a href=\"http:\/\/dataconomy.com\/2015\/03\/an-introduction-to-recommendation-engines\/\">An Introduction to Recommendation Systems<\/a><\/li>\n<li><a href=\"https:\/\/am207.github.io\/2017\/wiki\/gradientdescent.html\">Gradient Descent and SGD<\/a><\/li>\n<li><a href=\"https:\/\/www.bonaccorso.eu\/2017\/10\/03\/a-brief-and-comprehensive-guide-to-stochastic-gradient-descent-algorithms\/\">A Brief (and Comprehensive) Guide to Stochastic Gradient Descent Algorithms<\/a><\/li>\n<\/ul>\n<h3>Aula 07 &#8211; Redes Neurais &#8211; representa\u00e7\u00e3o<\/h3>\n<ul>\n<li><a href=\"https:\/\/medium.com\/towards-data-science\/what-the-hell-is-perceptron-626217814f53\">What the Hell is Perceptron?<\/a><\/li>\n<li><a href=\"https:\/\/neurophysics.ucsd.edu\/courses\/physics_171\/annurev.neuro.28.061604.135703.pdf\">Dendritic Computation (Michael London and Michael Hausser)<\/a><\/li>\n<li><a href=\"http:\/\/dataconomy.com\/2015\/03\/an-introduction-to-recommendation-engines\/\">An Introduction to Recommendation Systems<\/a><\/li>\n<li><a href=\"https:\/\/jack-clark.net\/\">Import AI<\/a><\/li>\n<li><a href=\"http:\/\/www.emergentmind.com\/neural-network\">This Emergent Mind project implements a JavaScript-based neural network with back-propagation that can learn various logical operators<\/a>.<\/li>\n<li><a href=\"https:\/\/machinelearningmastery.com\/gentle-introduction-mini-batch-gradient-descent-configure-batch-size\/\">A Gentle Introduction to Mini-Batch Gradient Descent and How to Configure Batch Size<\/a><\/li>\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/Coordinate_descent\">Wikipedia: Coordinate descent<\/a><\/li>\n<li><a href=\"https:\/\/pastebin.com\/vK7cE2qV\">Implementa\u00e7\u00e3o de uma RNA para o problema XOR<\/a><\/li>\n<li><a href=\"https:\/\/www.youtube.com\/watch?v=IHZwWFHWa-w\">[3Blue1Brown] Gradient descent, how neural networks learn | Deep learning, part 2<\/a><\/li>\n<li><a href=\"http:\/\/[Khan Academy] Multivariable functions | Multivariable calculus | Khan Academy https:\/\/www.youtube.com\/watch?v=TrcCbdWwCBc&amp;list=PLSQl0a2vh4HC5feHa6Rc5c0wbRTx56nF7\">[Khan Academy] Multivariable functions | Multivariable calculus | Khan Academy<\/a><\/li>\n<\/ul>\n<h3>Aula 08 &#8211; Redes Neurais &#8211; aprendizado<\/h3>\n<ul>\n<li><a href=\"https:\/\/prateekvjoshi.com\/2016\/03\/29\/understanding-xavier-initialization-in-deep-neural-networks\/\">Understanding Xavier Initialization In Deep Neural Networks<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/pdf\/1710.05941.pdf\">SWISH: A SELF-GATED ACTIVATION FUNCTION<\/a><\/li>\n<li><a href=\"https:\/\/mattmazur.com\/2015\/03\/17\/a-step-by-step-backpropagation-example\/\">A Step by Step Backpropagation Example<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/cthorey\/CS231\/tree\/master\/assignment1\">Algumas solu\u00e7\u00f5es para cs231n<\/a><\/li>\n<li><a href=\"http:\/\/www.emergentmind.com\/neural-network\">JavaScript-based neural network with back-propagation that can learn various logical operators<\/a><\/li>\n<li><a href=\"http:\/\/www.cedar.buffalo.edu\/~srihari\/CSE676\/11.5%20Debugging.pdf\">Debugging Strategies<\/a><\/li>\n<li><a href=\"https:\/\/betterexplained.com\/articles\/how-to-develop-a-mindset-for-math\/\">How to Develop a Mindset for Math<\/a><\/li>\n<li><a href=\"http:\/\/www.cs.ubbcluj.ro\/~gabis\/ml\/ml-books\/McGrawHill%20-%20Machine%20Learning%20-Tom%20Mitchell.pdf\">Machine Learning &#8211; Tom Mitchell<\/a><\/li>\n<li><a href=\"https:\/\/medium.com\/@karpathy\/yes-you-should-understand-backprop-e2f06eab496b\">Yes you should understand backprop<\/a><\/li>\n<li><a href=\"http:\/\/adventuresinmachinelearning.com\/improve-neural-networks-part-1\/\">Improve your neural networks \u2013 Part 1 [TIPS AND TRICKS]<\/a><\/li>\n<li><a href=\"https:\/\/blog.slavv.com\/37-reasons-why-your-neural-network-is-not-working-4020854bd607\">37 Reasons why your Neural Network is not working<\/a><\/li>\n<li><a href=\"https:\/\/machinelearningmastery.com\/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset\/\">8 Tactics to Combat Imbalanced Classes in Your Machine Learning Dataset<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/1609.04836\">On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima<\/a><\/li>\n<li><a href=\"http:\/\/theorangeduck.com\/page\/neural-network-not-working\">My Neural Network isn&#8217;t working! What should I do? (Aug. 19, 2017)<\/a><\/li>\n<li><a href=\"https:\/\/medium.com\/machine-learning-world\/how-to-debug-neural-networks-manual-dc2a200f10f2\">How to debug neural networks.<\/a><\/li>\n<li><a href=\"http:\/\/speech.ee.ntu.edu.tw\/~tlkagk\/talk.html\">Hung-yi Lee<\/a><\/li>\n<li><a href=\"https:\/\/www.youtube.com\/watch?v=cWzi38-vDbE&amp;feature=youtu.be&amp;app=desktop\">Yann LeCun &#8211; How does the brain learn so much so quickly? (CCN 2017)<\/a><\/li>\n<li><a href=\"https:\/\/medium.com\/towards-data-science\/epoch-vs-iterations-vs-batch-size-4dfb9c7ce9c9\">Epoch vs Batch Size vs Iterations<\/a><\/li>\n<li><a href=\"https:\/\/medium.com\/towards-data-science\/activation-functions-neural-networks-1cbd9f8d91d6\">Activation Functions: Neural Networks<\/a><\/li>\n<\/ul>\n<h3>Aula 09 &#8211; Redes Profundas; Introdu\u00e7\u00e3o ao Keras<\/h3>\n<ul>\n<li><a href=\"https:\/\/apiumhub.com\/tech-blog-barcelona\/deep-learning-startups\/\">DEEP LEARNING STARTUPS, USE CASES &amp; BOOKS<\/a><\/li>\n<li><a href=\"https:\/\/phys.org\/news\/2017-09-deep-ocean.html\">Using deep learning to forecast ocean waves<\/a><\/li>\n<li><a href=\"http:\/\/adventuresinmachinelearning.com\/neural-networks-tutorial\/\">Neural Networks Tutorial \u2013 A Pathway to Deep Learning<\/a><\/li>\n<li><a href=\"https:\/\/www.youtube.com\/playlist?list=PLJOzdkh8T5kqCNV_v1w2tapvtJDZYiohW\">Deep Learning Video Tutorials<\/a><\/li>\n<li>Jeff Dean\u2019s Talk on Large-Scale Deep Learning: <a href=\"https:\/\/becominghuman.ai\/jeff-deans-talk-on-large-scale-deep-learning-171fb8c8ac57\">aqui<\/a> e <a href=\"http:\/\/pavel.surmenok.com\/2017\/09\/23\/jeff-deans-talk-on-large-scale-deep-learning\/\">aqui<\/a>.<\/li>\n<\/ul>\n<h3>Aula 10 &#8211; Redes de Convolu\u00e7\u00e3o<\/h3>\n<ul>\n<li><a href=\"http:\/\/colah.github.io\/posts\/2014-07-Understanding-Convolutions\/\">Understanding Convolutions<\/a><\/li>\n<li><a href=\"https:\/\/stats.stackexchange.com\/questions\/232754\/reference-to-learn-how-to-interpret-learning-curves-of-deep-convolutional-neural\">Reference to learn how to interpret learning curves of deep convolutional neural networks<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/pdf\/1501.00092.pdf\">Image Super-Resolution Using Deep Convolutional Networks<\/a><\/li>\n<li><a href=\"https:\/\/deeplearning4j.org\/lstm.html#long\">A Beginner\u2019s Guide to Recurrent Networks and LSTMs<\/a><\/li>\n<\/ul>\n<h3>Aula 11 &#8211; Redes Recorrentes<\/h3>\n<ul>\n<li><a href=\"https:\/\/github.com\/nicodjimenez\/lstm\">A basic LSTM network written in Python<\/a>.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Aula 01 &#8211; Introdu\u00e7\u00e3o ao Aprendizado de M\u00e1quina; Regress\u00e3o Linear (uma vari\u00e1vel) Microsoft: AI Isn\u2019t Yet Adaptable Enough to Help Businesses Why Machines Still Can\u2019t Learn So Good Image Recognition: A Short History and All You Need to Know About It Informa\u00e7\u00f5es sobre Arthur Samuel: aqui e aqui Informa\u00e7\u00f5es sobre Tom Mitchell Whoever Controls Machine [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-390","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-json\/wp\/v2\/pages\/390","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-json\/wp\/v2\/comments?post=390"}],"version-history":[{"count":14,"href":"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-json\/wp\/v2\/pages\/390\/revisions"}],"predecessor-version":[{"id":523,"href":"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-json\/wp\/v2\/pages\/390\/revisions\/523"}],"wp:attachment":[{"href":"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-json\/wp\/v2\/media?parent=390"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}