Backpropagation Algorithm – An important mathematical tool for making better and high accuracy predictions in machine learning. This algorithm uses supervised learning methods for training Artificial Neural Networks. The whole idea of training multi-layer perceptrons is to compute the derivatives of the error function or gradient descent with respect to weights using the backpropagation algorithm. This algorithm … Continue reading Deep Learning – Backpropagation Algorithm Basics
Generative Adversarial Networks (GANs) – A very young family member of Deep Neural Networks Architecture. Introduce by Ian Goodfellow and his team at the University of Montreal in 2014. GANs are class of unsupervised machine learning algorithm. Adversarial training “The most interesting idea in the last 10 years in the field of Machine Learning.” … Continue reading Deep Learning – Introduction to Generative Adversarial Neural Networks (GANs)
Recurrent Neural Networks – Main use of RNNs are when using google or facebook these interfaces are able to predict next word what you are about to type. RNNs have loops to allow information to persist. RNN’s are considered to be fairly good for modeling sequence data. Recurrent neural networks are linear architectural variant of recursive networks. … Continue reading Deep Learning – Introduction to Recurrent Neural Networks
Convolutional Neural Network – In this article, we will explore our intuitive explanation of convolutional neural networks (CNN’s) on high level. CNN’s are inspired by the structure of the brain but our focus will not be on neural science in here as we do not specialise in any biological aspect. We are going artificial in this … Continue reading Deep Learning – Introduction to Convolutional Neural Networks
Neural network based on radial basis function with can be used for strategic reasons. There are several other models of neural network including what we have mentioned above. For an introduction to neural network and their working model
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