Deeplearning4j Documentation

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O site de um pacote java para deeplearing com montes de info. sobre redes neuronais e afins.


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The Many Faces of ROC Analysis

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Bom tutorial sobre curvas ROC

Receiver Operating Characteristics (ROC) Analysis originated from signal detection theory, as a model of how well a receiver is able to detect a signal in the presence of noise. Its key feature is the distinction between hit rate (or true positive rate) and false alarm rate (or false positive rate) as two separate performance measures. ROC analysis has also widely been used in medical data analysis to study the effect of varying the threshold on the numerical outcome of a diagnostic test. It has been introduced to machine learning relatively recently, in response to classification tasks with varying class distributions or misclassification costs (hereafter referred to as skew). ROC analysis is set to cause a paradigm shift in machine learning. Separating performance on classes is almost always a good idea from an analytical perspective. For instance, it can help us to

  • understand the behaviour and skew-sensitivity of many machine learning metrics, including rule learning heuristics and decision tree splitting criteria, by plotting their isometrics in ROC space;
  • develop new metrics specifically designed to improve the Area Under the ROC Curve (AUC) of a model;
  • understand fundamental algorithms such as the separate-and-conquer or sequential covering rule learning algorithm, by tracing its trajectory through a sequence of ROC spaces.

The goal of this tutorial is to develop the ROC perspective in a systematic way, demonstrating the many faces of ROC analysis in machine learning.

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Best Data Science Learning podcasts


Muito bons podcasts tem temas introdutórios

We present the top 12 Data Science & Machine Learning related Podcasts by popularity on iTunes. Check out latest episodes to stay up-to-date & become a part of the data conversations!

By Bhavya Geethika Peddibhotla.

Learn Data science the new way by listening to these compelling story tellers, interviewers, educators and experts in the field. Data suggests that podcasting about Data Science is only growing!

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Theoretical Motivations for Deep Learning

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Uma boa introdução ao Deep Learning uma nova técnica de machine learning.

This post is based on the lecture “Deep Learning: Theoretical Motivations” given by Dr. Yoshua Bengio at Deep Learning Summer School, Montreal 2015. I highly recommend the lecture for a deeper understanding of the topic.

Deep learning is a branch of machine learning algorithms based on learning multiple levels of representation. The multiple levels of representation corresponds to multiple levels of abstraction. This post explores the idea that if we can successfully learn multiple levels of representation then we can generalize well.

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