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Not the parent, but NNs typically work better when you can't linearize your data. For classification, that means a space in which hyperplanes separate classes, and for regression a space in which a linear approximation is good. For example, take the circle dataset here: https://playground.tensorflow.org That doesn't look immediately linearly separable, but since it is 2D we have the insight that parameterizing by... - Source: Hacker News / 2 months ago
For visualisation and some fun: http://playground.tensorflow.org/. - Source: dev.to / 4 months ago
Https://seeing-theory.brown.edu/ https://www.3blue1brown.com/ https://playground.tensorflow.org/. - Source: Hacker News / 8 months ago
There’s an interactive neural network you can train here, which can give some intuition on wider vs larger networks: https://mlu-explain.github.io/neural-networks/ See also here: http://playground.tensorflow.org/. - Source: Hacker News / 10 months ago
This site is worth playing around with to get a feel for neural networks, and somewhat about ML in general. There are lots of strategies for statistical learning, and neural nets are only one of them, but they essentially always boil down into figuring out how to build a “classifier”, to try to classify data points into whatever category they best belong in. Source: 11 months ago
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Neuronify - An educational neural network app.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Netron - Open-source visualizer for neural network, deep learning and machine learning models.
NumPy - NumPy is the fundamental package for scientific computing with Python
GoldSim - GoldSim is the premier Monte Carlo simulation software solution for dynamically modeling complex systems in business, engineering and science.