Innervator: Hardware Acceleration for Neural Networks.
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Updated
Jul 10, 2024 - VHDL
Innervator: Hardware Acceleration for Neural Networks.
Case Studies and Projects in Machine Learning/EDA/DL
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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.
In this project, I used the RAVDESS dataset with eight emotions, each at two intensities. I built a model extracting five features from speech signals to classify emotions, providing valuable insights.
Using Hopfield Neural Networks to recognise digits 0 - 9. Testing the Hebbian Training Method vs the Storkey Training Method.
This repository contains projects i completed throughout the "Machine Learning and Deep Learning Projects in Python" course by S. Emadedin Hashemi on Udemy
Intrusion Detection System for MQTT Enabled IoT.
Fruit Classifier with ANN
Performed data analysis with tensorflow and keras.
I am an Artificial Intelligence Engineer with expertise in Machine Learning and Computer Vision.
Huge-scale, high-performance flow cytometry clustering in Julia
Using the Minisom library, an implementation of the Self-Organizing Maps, to classify defected software.
I created an ANN model to predict customer churn using a dataset from Kaggle (https://www.kaggle.com/datasets/puja19/telcom-customer-churn). The model achieved an accuracy of 92 out of 100. The code is fully explained in the accompanying notebook.
Hecho con la tecnología de Windows Forms en el lenguaje C#. Un perceptrón simple que simula resultados mediante entradas
Predicting wether customers will leave a bank or not using Artficial Neural Network training
An exploration on SOM algorithm to solve traveling salesman problem
artifical neural network (ann) supervised learning javascript library and 2d game example
IST 5535 Machine Learning Algorithms and Applications in R Project, where we developed a predictive model to predict the UPDRS score for Parkinsons Disease based on different dysphonia (noise) measures.
This predicts the future energy demand by using a LSTM (Long Short Term Memory) Model i.e. (a kind of Recurrent Neural Network) based only on time series(sequence-to-sequence).
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