Code samples are modified for Michael Nielsen's book Neural Networks and Deep Learning by python 3.x
Code samples for my book "Neural Networks and Deep Learning"
This is my assignment on Andrew Ng's course “neural networks and deep learning”
Pytorch Unofficial implement of paper "All optical machine learning using diffractive deep neural networks" .
Code from Michael Nielsen book Neural Networks and Deep Learning ported to C++.
A probabilistic programming library for Bayesian deep learning, generative models, based on Tensorflow
Tutorial for understanding CNN basics, video at http://online.codingblocks.com Machine Learning/Deep Learning course.
This is a personal project to demonstrate my own programming skills, and learn the inner workings of machine learning models
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.