You might haven’t thought of it, but a great variety of tasks is successfully resolved with machine learning (ML). To name a few:
How is it actually done? Unlike under algorithmic approach when we program a machine on certain actions directly, when we perform ML, we teach a computer to get the desired output, and it generates this output. Experts opt for machine learning implementation in case programming a machine on certain actions doesn’t seem to be easy or reasonable.
ML is mostly held with neural networks algorithm. A neural network (NN) resembles a network of neurons in our brain to some extent.
How does it operate? As an input, some kind of sample is shown to a NN. As an output, a NN should produce a result that possesses the characteristics of the shown sample. By comparing the result received and the result that is wanted, a network is “taught”.
The most common task for NNs is data classification. Having a large data set as an input assures better results. Email filtering is one of classification samples.
One more popular task is object recognition. Examples include recognition of faces, handwriting, speech, etc.
Many programmers write algorithms for machine learning in Python. Useful libraries are written in this language – Tensorflow, Theano, Scikit-learn are among them.
Java can also a good fit for projects involving ML. There are Java based libraries for such purposes. To name a few: Weka, Deeplearning4, ELKI, Massive Online Analysis (MOA).
A NN can be implemented without libraries as well. However, our experts would say it is much better to work with reliable libraries. Torch library is one more useful instrument for this.
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If you have challenging tasks that involve machine learning implementation in Python, Java, or any other suitable language, reach out to us! Together we’ll find a solution.