Tarea 7: Clasificador de imágenes de ocho formas con GoogLeNet
Implementación y evaluación de un clasificador de ocho formas geométricas mediante Transfer Learning con GoogLeNet (Inception v1) recortada y adaptada a escala de grises en PyTorch.
Andrés Cruz Chipol
Master's Student in Computer Science at CINVESTAV
Passionate about AI, Machine Learning, Deep Learning, and Computer Vision research.
Recent Assignments
Coursework assignments and technical reports (written in Spanish per CINVESTAV academic requirements).
Tarea 6: Autoencoder para extraer características
Reproducción en PyTorch de la extracción de características con Autoencoders para potenciar clasificadores supervisados.
Tarea 5: Clasificador con redes de picos
Diseño y evaluación de una Red Neuronal de Picos (SNN) para clasificar la calidad del vino blanco.
M.Sc. in Computer Science
Passionate about AI, algorithms, and technology. Having completed all coursework, currently dedicated to Master's thesis research in Deep Learning at Cinvestav IPN.
Read full biography & research background →Current Courses & Research
Thesis Seminar
Master's thesis research and development focused on Deep Learning.
Previous 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.
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.