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2016 Dec 17
Hyperparameter optimization for Neural Networks
This article explains different hyperparameter algorithms that can be used for neural networks. It covers simple algorithms like Grid Search, Random Search and more complicated algorithms like Gaussian Process and Tree-structured Parzen Estimators (TPE).
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2016 Nov 12
Image classification, MNIST digits
This short tutorial shows how to design and train simple network for digit classification in NeuPy.
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2015 Jul 04
Predict prices for houses in the area of Boston
Boston house prices is a classical dataset for regression. This article shows how to make a simple data processing and train neural network for house price prediction.
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2015 Jul 04
Visualize Algorithms based on the backpropagation
Typical neural networks have mullions of parameters and it's quite difficult to visualize the process. In the article, we visualize training of the network that has only 2 parameters. It allows us to explore different training algorithms and see how it behaves during the training
These type of visualizations can provide us with useful insights about the training process.