Quetzalcoatl29
MSC AI // No clever manipulation of data can improve the Inferences that can be made from the data~
Pinned Repositories
CursoAprendizajeAutomatizado
Diapositivas, tareas, código de ejemplo y página del curso de aprendizaje automatizado impartido en el PCIC de la UNAM
AeroPython
Classical Aerodynamics of potential flow using Python and Jupyter Notebooks
amazon-sagemaker-examples
Example notebooks that show how to apply machine learning, deep learning and reinforcement learning in Amazon SageMaker
cs231n.github.io
NNST_lectures
deeplearning-models
A collection of various deep learning architectures, models, and tips
dslp
The Data Science Lifecycle Process is a process for taking data science teams from Idea to Value repeatedly and sustainably. The process is documented in this repo.
Quetzalcoatl29's Repositories
Quetzalcoatl29/cs231n.github.io
NNST_lectures
Quetzalcoatl29/ml-engineer-test
Repositorio base para desarrollo de la prueba practica que forma parte del proceso de contratación para ML Engineer
Quetzalcoatl29/dslp
The Data Science Lifecycle Process is a process for taking data science teams from Idea to Value repeatedly and sustainably. The process is documented in this repo.
Quetzalcoatl29/CursoAprendizajeAutomatizado
Diapositivas, tareas, código de ejemplo y página del curso de aprendizaje automatizado impartido en el PCIC de la UNAM
Quetzalcoatl29/utils_public
Public scripts
Quetzalcoatl29/MASTER
Quetzalcoatl29/graph_presentation
Quetzalcoatl29/py
Repository to store sample python programs for python learning
Quetzalcoatl29/recommenders
Best Practices on Recommendation Systems
Quetzalcoatl29/Python
:snake: Python Programs
Quetzalcoatl29/amazon-sagemaker-examples
Example notebooks that show how to apply machine learning, deep learning and reinforcement learning in Amazon SageMaker
Quetzalcoatl29/deeplearning-models
A collection of various deep learning architectures, models, and tips
Quetzalcoatl29/AeroPython
Classical Aerodynamics of potential flow using Python and Jupyter Notebooks
Quetzalcoatl29/Mission-to-Mars
Web data extraction is used for extracting data from external websites. In this assignment, 5 different websites are scraped and the results are stored in MongoDB. Flask application is then used to render the scraped data to a HTML webpage.