Wolfram mathematica 7 mathid
Mathematica's strengths lay with it's expressive, compact, language, strong visualisation capabilities and easy parallelization. This limits significantly the potential for creating novel methods within the existing machine learning framework. These latter general ensemble methods allow you to leverage the power of a base classifier, either built-in or, of greater utility when exploring performance improvements, of your own design. Whilst it does offer one ensemble method, RandomForest, it lacks both Bagging and any flavour of boosting, such as Adaboost. Mathematica, as of version 10, supports the following classifiers: "LogisticRegression", "Markov", "NaiveBayes", "NearestNeighbors", "NeuralNetwork", "RandomForest", "SupportVectorMachine". The few algorithms that Mathematica does support are not particularly well exposed for the type of tweaking needed to win Kaggle competitions. Julia has much more limited algorithm support but does exhibit a good turn of speed. Mathematica doesn't have the depth of algorithm support that is present in R or Python.
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The folks at Wolfram Research are often anticipating needs, and that has led to their curated databases, free-form and semantic input, and so on, so perhaps they'll make some optimized deep learning code (running in the cloud?) that will make calling from a standard Mathematica session easy. So at this moment, if I were to start a project in deep learning where the goal was a neural network with dozens of layers and hundreds of thousands of nodes ("neurons"), I'd likely turn to a language other than Mathematica. All these steps can be done in Mathematica, of course, but I haven't seen that it is superior to other languages in this realm. On the other hand, a late stage of machine learning work can involve implementing algorithms on a massive scale, with large datasets, where careful control of data type, bit resolution, parallel threading and such may become more important. I'm not aware of any other language that comes close to its power and ease in such tasks. I really like Mathematica's symbolics for statistical analyses, Probability, and so on. One of the many great benefits of Mathematica is its seamless integration between different functionality, so it is easy to use statistical learning with graph-theoretic methods, and pre-processing of images, sound, financial data, etc., without the need to load libraries of special-purpose functions. In pattern recognition and machine learning, the early stages are ones of experimentation and exploration-trying different algorithms, feature pre-processing, and such, as well as integrating different functions and visualizing preliminary results. Here, as in most disciplines, the "best" language depends upon what you seek to accomplish. As such, I feel I'm fairly well positioned to answer this question.
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I have worked in pattern classification and machine learning for decades, taught the subject in a number of elite academic departments, am writing the third edition of Pattern classification by Duda, Hart and Stork as well as its companion computer manual in Mathematica, and am an expert Mathematica programmer, a solid Matlab programmer, but very weak in R and Python and have no experience with Julia.