Sigmoid

Binary classification NN is used with the sigmoid activation function on its final layer together with BCE loss. The final layer size should be 1.

Binary Classification: Understanding Activation and Loss Functions with a PyTorch Example

The brain of a human child is spectacularly amazing. Even in any previously unknown situation, the brain makes a decision based on its primal knowledge. Depending on the outcome, it learns and remembers the most optimal choices to be taken in that particular scenario. On a high level, this process of learning can be understood as a ’trial and error’ process, where the brain tries to maximise the occurrence of positive outcomes.

Reinforcement Learning: A Brief Introduction to Rules and Applications

In this post, we will talk about the Universal approximation theorem and we will also prove the theorem graphically. This is a follow-up post of my previous post on <a href="https://towardsdatascience.com/sigmoid-neuron-deep-neural-networks-a4cd35b629d7" target="_blank">Sigmoid Neuron</a>.

Illustrative Proof of Universal Approximation Theorem

Animate your web applications with a customizable ease-out.

Ease Out: The Half Sigmoid

It’s very common for animations to be specified as ease-in-out. It’s a very pleasing sensation to witness an object speed up, cruise, and slow to a halt. Most easings specify one of a small number of easing curves: easeInOutQuad, easeInOutSine, easeInOutCubic, etc. However, the sharpness of that curve is not configurable. Here I show how to create a configurable ease-in-out function that will work for animating any property you desire.

Ease In, Out: The Sigmoid Factory


ADA boosts regression models' robustness through data augmentation, improving performance across real-world and synthetic data with varying hyperparameters.

Testing ADA on Synthetic and Real-World Data

This paper extends ADA’s evaluation to nonlinear regression on the California and Boston housing datasets, comparing its performance against C-Mixup.

ADA vs C-Mixup: Performance on California and Boston Housing Datasets

Learn to build a model for predicting new links in a social graph, demonstrating the use of Graph Neural Networks and DGL for link prediction.

Predicting Links in Graphs with Graph Neural Networks and DGL.ai

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