Which is your favorite Machine Learning Algorithm?

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Developed back in the 50s by Rosenblatt and colleagues, this extremely simple algorithm can be viewed as the foundation for some of the most successful classifiers today, including suport vector machines and logistic regression, solved using stochastic gradient descent. The convergence proof for the Perceptron algorithm is one of the most elegant pieces of math I've seen in ML. Most useful: Boosting, especially boosted decision trees. This intuitive approach allows you to build highly accurate ML models, by combining many simple ones. Boosting is one of the most practical methods in ML, it's widely used in industry, can handle a wide variety of data types, and can be implemented at scale.


Maximum Entropy Learning with Deep Belief Networks

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Understanding how a nervous system computes requires determining the input, the output, and the transformations necessary to convert the input into the desired output [1]. Artificial neural networks are a conceptual framework that provide insight into how these transformations are carried out, and have also played a crucial factor in the success of many pattern recognition tasks such as for handwriting [2] and object [3] detection. An important feature of neural networks is their ability to capture the underlying regularities in a task domain by representing the input with multiple layers of active neurons. This distributed representation of the input is based on the hierarchal processing and information flow of biological systems [4,5]. In a multi-layered network, complex internal representations can also be constructed by repeatedly adjusting the weights of the connections in order to ensure that the output is close to the desired output [6].


When Does Deep Learning Work Better Than SVMs or Random Forests?

@machinelearnbot

Guest blog by Sebastian Raschka, originally posted here. If we tackle a supervised learning problem, my advice is to start with the simplest hypothesis space first. I.e., try a linear model such as logistic regression. If this doesn't work "well" (i.e., it doesn't meet our expectation or performance criterion that we defined earlier), I would move on to the next experiment. I would say that random forests are probably THE "worry-free" approach - if such a thing exists in ML: There are no real hyperparameters to tune (maybe except for the number of trees; typically, the more trees we have the better).


IBM Watson steps into real-world cybersecurity

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MoneyLion secures $22.5M to bring fresh talent to AI finance management Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time. We won't share your personal information with anyone.


Apple Reportedly Acquires AI-Based Facial Recognition Startup RealFace

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In a bid to boost its prospects in the world of artificial intelligence (AI), Apple has acquired Israel-based startup RealFace that develops deep learning-based face authentication technology, media reported on Monday. Reported by Calcalist, the acquisition is to be worth roughly $2 million (roughly Rs. 13.39 crores). A Times of Israel report cites Startup Nation Central to note RealFace had raised $1 million in funding thus far, employed about 10 people, and had sales operations China, Europe, Israel, and the US. Set up in 2014 by Adi Eckhouse Barzilai and Aviv Mader, RealFace has developed a facial recognition software that offers users a smart biometric login, aiming to make passwords redundant when accessing mobile devices or PCs. The firm's first app - Pickeez - selects the best photos from the user's album.