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Jose Guillen's Projects

clustering-data icon clustering-data

This project clusters customers using Python's scikit-learn library and the k-means algorithm, then visualizes the results to identify patterns and make business decisions.

deep-learning-bishop-example-codes icon deep-learning-bishop-example-codes

Deep Learning Bishop Example Codes: This repository contains example codes for deep learning concepts as described in 'Pattern Recognition and Machine Learning' by Christopher M. Bishop. It includes practical implementations of the topics covered in the book.

hash-table-collision-handling icon hash-table-collision-handling

This repository implements open addressing technique to handle collisions in a hash table and describes the techniques used to calculate the sequence of probed positions, including linear probing, quadratic probing, and double hashing.

javacprocesssynchronization icon javacprocesssynchronization

This project is a Java/C implementation simulating people crossing a pedestrian bridge, demonstrating the concepts of thread synchronization and semaphores in Operating Systems.

openwakeword icon openwakeword

An open-source audio wake word (or phrase) detection framework with a focus on performance and simplicity.

process-creation-and-communication icon process-creation-and-communication

This repository contains the implementation of a C program for creating and communicating processes using shared memory. It calculates the value of Pi using two different series - Gregory-Leibniz and Nilakantha - to demonstrate the inter-process communication and shared memory management in a Linux environment.

quadtree-image-compression icon quadtree-image-compression

Quad-Tree image compression algorithm implemented in C using stb_image.h and stb_image_write.h libraries. Efficiently reduces image size while maintaining quality.

rnn-text-generation icon rnn-text-generation

Text generation using a Recurrent Neural Network (RNN) in PyTorch. This project demonstrates how to train an RNN model to learn and generate text based on a training corpus, allowing for the creation of text sequences in the style of the source dataset.

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