# Tag: Backpropagation (15)

**The Backpropagation Algorithm Demystified**- Jan 2, 2019.

A crucial aspect of machine learning is its ability to recognize error margins and to interpret data more precisely as rising numbers of datasets are fed through its neural network. Commonly referred to as backpropagation, it is a process that isn’t as complex as you might think.**Deep Learning in H2O using R**- Jan 22, 2018.

This article is about implementing Deep Learning (DL) using the H2O package in R. We start with a background on DL, followed by some features of H2O's DL framework, followed by an implementation using R.**Top KDnuggets tweets, Dec 06-12: Top #DataScience and #MachineLearning Methods Used in 2017; Geoff Hinton Capsule Networks – a new way for machines to see**- Dec 13, 2017.

Also The first international #beauty contest decided by #AI #algorithm sparked controversy; 4 Common #Data Fallacies That You Need To Know; Using #DeepLearning to Solve Real World Problems; Best Online Masters in #DataScience and #Analytics.**The 10 Deep Learning Methods AI Practitioners Need to Apply**- Dec 13, 2017.

Deep learning emerged from that decade’s explosive computational growth as a serious contender in the field, winning many important machine learning competitions. The interest has not cooled as of 2017; today, we see deep learning mentioned in every corner of machine learning.**Neural Network Foundations, Explained: Updating Weights with Gradient Descent & Backpropagation**- Oct 25, 2017.

In neural networks, connection weights are adjusted in order to help reconcile the differences between the actual and predicted outcomes for subsequent forward passes. But how, exactly, do these weights get adjusted?**A Quick Introduction to Neural Networks**- Nov 9, 2016.

This article provides a beginner level introduction to multilayer perceptron and backpropagation.**Deep Learning Key Terms, Explained**- Oct 12, 2016.

Gain a beginner's perspective on artificial neural networks and deep learning with this set of 14 straight-to-the-point related key concept definitions, including Biological Neuron, Multilayer Perceptron (MLP), Feedforward Neural Network, and Recurrent Neural Network.

**Top KDnuggets tweets, Jun 15-21: Predicting UEFA Euro2016; Visual Explanation of Backprop for Neural Nets**- Jun 22, 2016.

Building statistical model to predict UEFA #Euro2016; A Visual Explanation of Back Propagation Algorithm for #NeuralNetworks; Scala is the new golden child for coding and #DataScience.**KDnuggets™ News 16:n22, Jun 22: Data Science Blog Contest; Free Machine Learning Ebook; Master SQL for Data Science**- Jun 22, 2016.

Data Science Blog Contest; New Free Andrew Ng Machine Learning Book Under Construction; 7 Steps to Mastering SQL for Data Science; A Visual Explanation of the Back Propagation Algorithm; Mining Twitter Data with Python Part 1: Collecting Data**A Visual Explanation of the Back Propagation Algorithm for Neural Networks**- Jun 17, 2016.

A concise explanation of backpropagation for neural networks is presented in elementary terms, along with explanatory visualization.**Learning to Code Neural Networks**- Jan 22, 2016.

Learn how to code a neural network, by taking advantage of someone else's experiences learning how to code a neural network.**How do Neural Networks Learn?**- Dec 2, 2015.

Neural networks are generating a lot of excitement, while simultaneously posing challenges to people trying to understand how they work. Visualize how neural nets work from the experience of implementing a real world project.**A Neural Network in 11 lines of Python**- Oct 30, 2015.

A bare bones neural network implementation to describe the inner workings of back-propagation.**YCML Machine Learning library on Github**- Aug 24, 2015.

YCML is a new Machine Learning library available on Github as an Open Source (GPLv3) project. It can be used in iOS and OS X applications, and includes Machine Learning and optimization algorithms.**Geoff Hinton AMA: Neural Networks, the Brain, and Machine Learning**- Dec 9, 2014.

In a wide-ranging Q&A, Geoff Hinton addresses the future of deep learning, its biological inspirations, and his research philosophy.