Learn about the role of activation functions in neural networks, including the different types of activation functions and how they work

Using Apple’s new BNNS framework to make a basic neural network

Neural networks are machine learning algorithms that are very good at solving tough problems - they’re used for language translation, facial recognition, and financial management. I, however, have been training them on silly datasets. Here are some of my favorite experiments from the last year

Discover the differences and commonalities of artificial intelligence, machine learning, deep learning and neural networks

课程地址：https://www.coursera.org/learn/neural-networks 老师主页：http://www.cs.toronto.edu/~hinton 备注：笔记内容和图片均参考老师课件。 Lecture2主要介绍了神经网络的几种主流类型以及感知机，下面回顾一下

Let us see how random forest competes with neural networks for solving a real world business problem

Currently, our largest neural networks have only billions of parameters, while the brain has trillions of synapses. To this date, we are not able to scale up artificial neural networks to trillions of parameters by performing the usual dense matrix multiplication. We introduce Modular Networks, a way to make neural networks modular, similar to what has been observed in the brain. In our approach, we execute only some of these modules, conditioned on the input to the network. Furthermore, we look at future p

Skip to main content Toggle navigation Lipman’s Artificial Intelligence Directory Category: Neural Networks Introduction to Activation Maximization and implementation in Tensorflow June 18, 2018June 18, 2018 Juan Miguel Valverde Leave a comment Introduction The goal of this entry is to learn about activation maximization and to prove two basic well-known facts about Deep Learning. The way a NN learns the representation of something does not neet to be in a significant way for humans. In fact, most of the

Discorver how to formulate and train Spiking Neural Networks (SNNs) using the LIF model, and how to encode data so that it can be processed by SNNs

Sinc function is a sinusoidal activation function in neural networks. In contrast to other common activation functions, it has rises and falls. However, the function saturated and its output converges to zero for large positive and negative inputs