Explore various regularization methods used in neural networks, how they work, and why we need them
The advancement of convolutional neural networks (CNNs) on various vision applications has attracted lots of attention. Yet the majority of CNNs are unable to satisfy the strict requirement for
Discover the secrets of neural networks, the building blocks of AI. Explore their structure, function, types, applications, and limitations
Abstract page for arXiv paper 1910.00019: Non-Gaussian processes and neural networks at finite widths
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Understand what you never did before: artificial neural networks – how they are structured, and how they function in the grand scheme of all things AI
Neural networks are one of the most powerful machine learning algorithm. However, its background might confuse brains because of complex mathematical calculations. In this post, math behind the neural network learning algorithm and state of the art are mentioned
Two weeks ago, we began our machine study in earnest by constructing a full neural network. But this network was still quite simple by deep learning standards. In this article, we’re going to tackle a much more difficult problem: image recognition. Of course, we’ll still be using a well known data set with well-known results, so this is only the tip of the iceberg. We’ll be using the <a href="http://yann.lecun.com/exdb/mnist/" target="_blank">MNIST data set</a>. This set classifies images of
An overview of gradient descent in the context of neural networks. This is a method used widely throughout machine learning for optimizing how a computer performs on certain tasks
Do neural networks need explicit symbolic architecture to compose learned concepts, or can scaling alone enable compositional generalization? This asks whether compositionality is an architectural feature or an emergent property of scale