Tarea 2: Clasificador de calidad de vinos en PyTorch
Implementación de una red neuronal para clasificar la calidad de vinos utilizando diferentes optimizadores: Adam, SGD y SGD con momento Nesterov.
Hello, I'm
Andrés Cruz Chipol
Master's Student in Computer Science at CINVESTAV. Passionate about AI, Machine Learning, and Computer Vision.
Recent Assignments
Tarea 1: Clasificación de dígitos manuscritos con MNIST en PyTorch
Implementación y análisis de una red neuronal feedforward (MLP) utilizando PyTorch para clasificar imágenes de dígitos manuscritos del dataset MNIST.
Tarea 8: Clasificación Bayesiana de formas geométricas
Pipeline completo de visión por computadora: captura manual de imágenes, extracción de características geométricas, entrenamiento de un clasificador Naive Bayes Gaussiano y predicción en tiempo real con cámara.
M.Sc. in Computer Science
Passionate about AI, algorithms, and technology. Currently focused on Machine Learning and Deep Learning at Cinvestav IPN.
Read full bio →Current Courses
Deep Learning
Advanced study of deep neural networks, covering modern architectures and applications in complex data.
Computational Complexity
In-depth analysis of computational problems, complexity classes (P, NP, NP-Complete), and limits of algorithmic efficiency.
Generative AI
Exploration of modern generative models and architectures for synthesizing text, images, and complex systems.
Collaborative Systems
Principles and architectures for Computer-Supported Cooperative Work (CSCW) and distributed collaborative applications.
Previous Courses
Machine Learning
Supervised learning, neural networks, ensemble methods, and fundamental ML pipelines.
Computer Architecture
Processor architecture, primarily focusing on RISC-V and RISC0, assembly programming, and performance optimization.
Theory of Computation
Fundamental concepts including automata theory, formal languages, computability, and complexity theory.
Game Theory
Strategic decision-making analysis. Competitive scenarios, Nash equilibrium, and applications in CS.
Algorithm Analysis
Efficiency, data structures, and computational complexity.
Artificial Intelligence
Intro to AI principles, search, logic, and intelligent agents.
Advanced Programming
Software engineering, object-oriented design, and design patterns.
Discrete Mathematics
Logic, combinatorics, graph theory, and mathematical proofs.