An introduction to deep artificial neural networks and deep learning. | Continue reading
The differences between super-human intelligence and general-purpose machine modeling. | Continue reading
Application of graph theory in machine and deep learning. | Continue reading
A beginner's reference for Restricted Boltzmann Machines (RBMs), invented by Geoffrey Hinton. | Continue reading
Beginner's guide to troubleshooting neural networks. | Continue reading
A list of great tools for data science, machine learning and AI in Clojure. | Continue reading
Machine Learning Platform for Enterprises on JVM Stack | Continue reading
A simple algorithm originally intended to perform binary classification. | Continue reading
Spiking is a way to encode digital communications over a long distance. | Continue reading
Our interactive learning scenarios provide you with a pre-configured SKIL instance, accessible from your browser without any downloads or configuration. Use it to experiment, learn SKIL and see how we can help solve real-world machine learning problems. | Continue reading
Denoising autoencoders attempt to address identity-function risk by randomly corrupting input (i.e. introducing noise) that the autoencoder must then reconstruct. | Continue reading
A comparison of various deep learning and machine learning frameworks including Deeplearning4j, PyTorch, TensorFlow, Caffe, Keras, MxNet, Gluon & CNTK. | Continue reading
Reinforcement learning refers to goal-oriented algorithms, which learn how to attain a complex objective (goal) or maximize along a particular dimension over many steps. | Continue reading