Blog Topics Advertise Join Newsletter Training a Neural Network to Write Like Lovecraft In this post, the author attempts to train a neural network to generate Lovecraft-esque prose, known to be awkward and irregular at best. Did it end in success? If not, any suggestions on how it might have? Read on to find out. By Luciano Strika , MercadoLibre on July 11, 2019 in Keras , LSTM , Natural Language Generation , Neural Networks , Python , TensorFlow --> comments LSTM Neural Networks have seen a lot of use in

Machine Learning # Common architectures in convolutional neural networks. #### Jeremy Jordan 19 Apr 2018 • 9 min read In this post, I'll discuss commonly used architectures for convolutional networks. As you'll see, almost all CNN architectures follow the same general design principles of successively applying convolutional layers to the input, periodically downsampling the spatial dimensions while increasing the number of feature maps. While the classic network architectures were comprised simply of s

A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks CalibrationYuhang Li, Shikuang Deng, Xin Dong, Ruihao Gong, Sh

The transformer neural network was first proposed in a 2017 paper to solve some of the issues of a simple RNN. This guide will introduce you to its operations

An activation function transforms weighted input values to form the output from neurons. It is mainly of two types: Linear and Non-linear activation functions and is used in Hidden and Output layers in ANN. An activation function should have properties like differentiability, continuity, monotonic, non-linear, boundedness, crossing origin and computationally cheaper, which we have discussed in detail.

Recursive neural network Not to be confused with Recurrent neural network . A recursive neural network (RNN) is a kind of deep neural network created by applying the same set of weights recursively over a structure, to produce a structured prediction over variable-length input, or a scalar prediction on it, by traversing a given structure in topological order . RNNs have been successful in learning sequence and tree structures in natural language processing , mainly phrase and sentence continuous representa

Your All-in-One Learning Portal: GeeksforGeeks is a comprehensive educational platform that empowers learners across domains-spanning computer science and programming, school education, upskilling, commerce, software tools, competitive exams, and more.

Understand GNNs that operate on graph-structured data, learning through message passing between nodes and edges.

Abstract page for arXiv paper 2307.13609: Dendritic Integration Based Quadratic Neural Networks Outperform Traditional Aritificial Ones

Session-based recommendations are highly relevant in many modern on-line services (e.g. e-commerce, video streaming) and recommendation settings. Recently, Recurrent Neural Networks have been shown