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Deep Grid. Informative posts on machine learning, deep learning, statistics, algorithms and related topics.
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Vanishing And Exploding Gradient Problems
Introduction Two of the common problems associated with training of deep neural networks using gradient-based learning methods and backpropagation include the vanishing gradients and that of the exploding gradients. In this article we explore how these pr
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Factorization Machines
Introduction Factorization machines were first introduced by Steffen Rendle 1 in 2010. The idea behind FMs is to model interactions between features (explanatory variables) using factorized parameters. The FM model has the ability to the estimate all inte
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Backpropagation In Convolutional Neural Networks
Introduction Convolutional neural networks (CNNs) are a biologically-inspired variation of the multilayer perceptrons (MLPs). Neurons in CNNs share weights unlike in MLPs where each neuron has a separate weight vector. This sharing of weights ends up redu
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Formulating The ReLu
Rectified Linear Function (ReL): In this article we take a critical review of the rectified linear activation function and its formulation as derived from the sigmoid activation function. ReL Definition The Rectified Linear Function (ReL) is a max functio
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Initialization Of Deep Networks Case of Rectifiers
Mathematics Behind Neural Network Weights Initialization - Part Three: In this third of a three part series of posts, we will attempt to go through the weight initialization algorithms as developed by various researchers taking into account influences der
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