artifical-neural-network

ADAS is short for Adaptive Step Size, it's an optimizer that unlike other optimizers that just normalize the derivative, it fine-tunes the step size, truly making step size scheduling obsolete, achieving state-of-the-art training performance

Awesome tutorials, papers, projects and tools for Reservoir Computing techniques like Echo State Networks (ESN).

Hands-on implementations of advanced deep learning architectures and techniques. Focused on real-world applications, optimization, and research-level concepts.

Huge-scale, high-performance flow cytometry clustering in Julia

Identify circular trajectories in scRNA-seq data using an autoencoder with sinusoidal activations

Case Studies and Projects in Machine Learning/EDA/DL

Innervator: Hardware Acceleration for Neural Networks

The implementation of evolvable-substrate HyperNEAT algorithm in GO language. ES-HyperNEAT is an extension of the original HyperNEAT method for evolving large-scale artificial neural networks.

Option pricing and Delta hedging performance comparison between Black and Scholes vs Artificial Neural Network

Recognize handwritten digits using back-propagation algorithm on MNIST data-set

(Machine) Learning to Do More with Less

Wave-Mask/Mix: Exploring Wavelet-Based Augmentations for Time Series Forecasting

White Blood Cell Classification is a deep learning project built with Python, TensorFlow, and Keras that classifies five types of WBCs from microscopic images using a CNN model. With advanced image preprocessing, data augmentation, and a robust architecture, it achieves up to 95% test accuracy.

A project to filter SQL Injection and XSS attacks using ANN -- in Ruby

Fruit Classifier with ANN

ANN detecting signal from internal variability

Intrusion Detection System for MQTT Enabled IoT.

building artificial neuron networks from scratch