Deep Learning / neural network machine learning

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Code samples are modified for Michael Nielsen's book Neural Networks and Deep Learning by python 3.x

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Code samples for my book "Neural Networks and Deep Learning"

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This is my assignment on Andrew Ng's course β€œneural networks and deep learning”

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Pytorch Unofficial implement of paper "All optical machine learning using diffractive deep neural networks" .

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Code from Michael Nielsen book Neural Networks and Deep Learning ported to C++.

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zhusuan πŸ‚
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A probabilistic programming library for Bayesian deep learning, generative models, based on Tensorflow

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Tutorial for understanding CNN basics, video at http://online.codingblocks.com Machine Learning/Deep Learning course.

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This is a personal project to demonstrate my own programming skills, and learn the inner workings of machine learning models

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Some of the most successful deep learning methods involve artificial neural networks. Artificial neural networks are inspired by the 1959 biological model proposed by Nobel laureates David H. Hubel & Torsten Wiesel, who found two types of cells in the primary visual cortex: simple cells and complex cells. Deep learning (deep structured learning or hierarchical learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using model architectures. cs231n.github.io, theano tutorial and ufldl.stanford.edu has a reference.

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"Lithium-Ion Battery Life Prediction Based on Initial Stage-Cycles Using Machine Learning"--Deep Neural Model

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