In this tutorial, you will learn about convolution and cross-correlation in neural networks. These concepts are important to deep learning for a variety of reasons
Skip to content Twitter Youtube GitHub Linkedin Facebook Instagram RSS Mail Sefik Ilkin Serengil Code wins arguments Menu Hyperbolic Secant As Neural Networks Activation Function Sefik Serengil August 29, 2018February 2, 2020 Machine Learning , Math Post navigation Previous Next Hyperbolic functions are common activation functions in neural networks. Previously, we have mentioned hyperbolic tangent as activation function. Now, we will mention a less common one. Hyperbolic secant or as its acronym sech(x) ap
Discover the power of Deep Neural Networks for Machine Learning and how they can be used to improve your predictive modeling
Here is a very good video about understanding neural networks if you know a little digital signal processing / wavelets. https://youtu.be/nHXO43BqeQw I was also able to find a couple of papers about decision regions in
Regularization techniques help improve a neural network’s generalization ability by reducing overfitting. They do this by minimizing needless complexity and exposing the network to more diverse data
Skip to content Clearly and Simply # Tag: Artificial Neural Networks ## Where the rubber meets the road ### A self organizing feature map for Travelling Salesman Problems implemented in Microsoft Excel In the recent post we discussed the question whether Microsoft Excel is a viable platform for developing and testing models and algorithms for complex combinatorial optimization problems. The Travelling Salesman Problem (TSP) is probably one of the most popular challenges in combinatorial optimization a
How to build fully convolutional neural networks that can model the complex structure of piano music with striking success
Learn how to harness the power of recurrent neural networks for text classification in this comprehensive guide
The most intuitive explanation of the mathematical prowess that are neural networks
Let us see how random forest competes with neural networks for solving a real world business problem