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In this video I do a little educational explanation of how Neural Networks work. How they take in input and choose what to output.
TSNE (T-Distributed Stochastic Neighbor Embedding) is a popular unsupervised dimensionality reduction algorithm that finds uses as varied as neurology, image similarity, and visualizing neural networks.
Suppose you have just developed a spiffy new machine learning model (using Flux.jl of course) and now want to start deploying it for your users. How do you go about doing that? Probably the simplest thing would be to just ship your model to your users and let them run it locally on their data.
Data is truly considered a resource in today’s world. As per the World Economic Forum, by 2025 we will be generating about 463 exabytes of data globally per day! But is all this data fit enough to be used by machine learning algorithms? How do we decide that?
This article will provide the reader with a brief overview for a number of different Linux commands. A special emphasis will be placed on explaining how each command can be used in the context of performing data science tasks. The goal will be to convince the reader that each of these commands can be extremely useful, and to allow them to understand what role each command can play when manipulating or analyzing data.