Mish As Neural Networks Activation Function

Recently, Mish activation function is announced in deep learning world. Researchers report that it overperforms than both regular ReLU and Swish. The … More

5 Facts about Deep Learning and Neural Networks

Marketing staff are much more successful than engineers for things to be adopted. Even for engineering marvels. People working in … More

The Insider’s Guide to Adam Optimization Algorithm for Deep Learning

Adam is the super star optimization algorithm of Deep Learning. Optimization algorithms aim to find optimum weights, minimize error and … More

Random Initialization in Neural Networks

Neural networks requires to apply several state-of-the-art techniques such as choice of activation function, or network design to push their … More

How Vectorization Saves Life in Neural Networks

Developers tend to handle problems with conditional statements and loops. This is the number one topic of developers and data … More

Convolutional Autoencoder: Clustering Images with Neural Networks

Previously, we’ve applied conventional autoencoder to handwritten digit database (MNIST). That approach was pretty. We can apply same model to … More

Autoencoder: Neural Networks For Unsupervised Learning

Neural networks are like swiss army knifes. They can solve both classification and regression problems. Surprisingly, they can also contribute … More

Handling Overfitting with Dropout in Neural Networks

Overfitting is trouble maker for neural networks. Designing too complex neural networks structure could cause overfitting. So, dropout is introduced to … More

Facial Expression Recognition with Keras

Kaggle announced facial expression recognition challenge in 2013. Researchers are expected to create models to detect 7 different emotions from human … More

Logarithm of Sigmoid As a Neural Networks Activation Function

Previously, we’ve reviewed sigmoid function as activation function for neural networks. Logarithm of sigmoid states it modified version. Unlike to … More