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Hi, I'm a physicist, my interests lie in computational applications to physics and mathematics, physics engines, data analysis, machine learning, neural networks and much more.

My go-to programming languages are Python, C, Rust, Mathematica.

I have knowledges of HTML, CSS and Javascript because I like making things easily accessible for everyone with web implementations.

My favourite IDEs are Jupyter Lab and Visual Studio Code, plus I like versioning just about everything I work on with Git and Github.

VIM > Nano.

Feel free to contact me for anything interesting.

Check out my Web Portfolio with nothing in it.

Alessandro Michele Ferrara's Projects

ai-logistic-regression-on-molecular-dynamics icon ai-logistic-regression-on-molecular-dynamics

A logistic regression (Gradient descent) algorithm to perform image classification to visually dinstinguish between molecular simulation of Argon at the solid state vs gas+liquid state.

coursera-deep-learning-specialization icon coursera-deep-learning-specialization

Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks;

engine-sim icon engine-sim

Combustion engine simulator that generates realistic audio.

fisica-statistica icon fisica-statistica

This code base is composed of the python programs I wrote for my course of Statistical Physics

llama-gpt icon llama-gpt

A self-hosted, offline, ChatGPT-like chatbot. Powered by Llama 2. 100% private, with no data leaving your device. New: Code Llama support!

neural-network-core-periphery icon neural-network-core-periphery

AI: Neural Network application in Complex Networks, find a core-periphery structure from data coming from Erdős–Rényi, preferential attachment (Barabasi-Albert), Core Periphery (Borgatti-Everett) and Small World (Watts-Strogatz) networks.

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