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Active Noise Cancelling Algorithms implementation
Implementation of Griffin from the paper: "Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models"
能動騒音制御(Active Noise Control)の説明資料
An implementation of various metaheuristics adapted to train neural networks
PointHop: An Explainable Machine Learning Method for Point Cloud Classification
TensorFlow implementation of CNN fast neural style transfer ⚡️ 🎨 🌌
Implementation of the model "Hedgehog" from the paper: "The Hedgehog & the Porcupine: Expressive Linear Attentions with Softmax Mimicry"
Zeta implemantion of "Rethinking Attention: Exploring Shallow Feed-Forward Neural Networks as an Alternative to Attention Layers in Transformers"
Build our neural networks from scratch
Animation Tweening of 3D vertex data using a Feed-Forward Neural Network.
Machine Learning Exercises from Online Course (Coursera)
Detection of handwritten digits with neural networks
Implementação simples da rede neural Perceptron Multicamadas em Javascript.
This is a complete repository of any code done throughout the 2024-2025 VEX High Stakes Season.
C++ neuron-based neural network library
A minimal, modular Python implementation of feed-forward neural networks (binary & multi-class) with customizable layers, activations, optimizers (GD/Adam), learning-rate schedules, and L2 regularization. Ideal for learning and experimentation.
Matlab toolbox for model stable inversion based on least squares
A simple, from-scratch C++ Feedforward Neural Network with zero dependencies, built to demystify the fundamentals of deep learning.
CNN to classify animal pictures from the Animal-10 dataset
This is a application that though a feed forward neural network allows a car to learn to drive
Implement the multiple layer neural network simulator using C#.
A highly modular design and implementation of fully-connected feedforward neural network structured on NumPy matrices
Implementation of research paper "Lateral Control of an Autonomous Vehicle." A controller is designed to control the lateral movement of autonomous vehicles on straight and curved roads using the principle of feedforward, backstepping, and preview point.
Implementing Neural Networks from scratch
🧠 Neural Network From-Scratch Implementations while taking Facultad de Ciencias Course from UNAM.
A feed forward neural network that learns to produce boxes.
A Multilayer Perceptron built in TypeScript from scratch.
🖼️🔢 A TensorFlow/Keras-based Fully Connected Neural Network for classifying 32×32 color images into 10 categories using the CIFAR-10 dataset.
A feed forward neural network that learns to produce boxes.
Letters and numbers classifier for chess games using computer vision and neural networks.
MATLAB pipeline for detecting EMG onset/offset and vertical ground reaction force (Fz) contact during landing tasks. Includes feedforward (pre-activation) and feedback (post-activation) timing analysis.
Multilayer Perceptron implementation using feedforward, backpropagation, and gradient descent from scratch.
Implementation of a fully connected feedforward neural network from scratch in Python using NumPy. Includes forward propagation, backpropagation, and application to the Titanic dataset for survival prediction. Demonstrates hands-on understanding of activation functions, weight updates, and performance evaluation.
A simple & efficient AI framework written in C.