pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

Conventional Neural Networks can approximate simple arithmetic operations, but fail to generalize beyond the range of numbers that were seen during training. Neural Arithmetic Units aim to overcome this difficulty, but current arithmetic units are either limited to operate on positive numbers or can only represent a subset of arithmetic operations. We introduce the Neural Power Unit (NPU) that operates on the full domain of real numbers and is capable of learning arbitrary power functions in a single layer

Neural Nets tutorial with code example and explaination

Stand Out Publishing . Home Page . Neural Networks

Credit scoring is the practice of analysing a persons background and credit application in order to assess the creditworthiness of the person. One can take numerous approaches on analysing this creditworthiness. In the end it basically comes down to first selecting the correct independent variables (e.g. income, age, gender) that lead to a given level of creditworthiness. In other words: creditworthiness=f(income, age, gender, ...). A creditscoring system can be represented by linear regression, logistic re

Thanks to Dan Roberts and Sho Yaida for comments on a draft of this post. …

Without activation functions, a neural network is just a linear regression no matter how deep. This guide explains what activation functions do, the most important ones, and how to choose the right one for your architecture

In this guide, we discuss what a Convolutional Neural Network (CNN) is, how they work, and discuss various different applications of CNNs in computer vision models

http://gizmodo.com/this-is-what-happens-when-you-let-a-neural-network-desi-1755137713

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