https://github.com/juselara1/usa_neural_networks
Material del curso de redes neuronales para high-performance computing en el programa de Ciencias de la Computacion e Inteligencia Artificial en la Universidad Sergio Arboleda.
https://github.com/juselara1/usa_neural_networks
Last synced: 4 months ago
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Material del curso de redes neuronales para high-performance computing en el programa de Ciencias de la Computacion e Inteligencia Artificial en la Universidad Sergio Arboleda.
- Host: GitHub
- URL: https://github.com/juselara1/usa_neural_networks
- Owner: juselara1
- Created: 2023-08-01T03:01:54.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-08-29T02:28:28.000Z (almost 2 years ago)
- Last Synced: 2024-12-31T11:58:27.133Z (5 months ago)
- Language: Jupyter Notebook
- Size: 1.56 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Redes Neuronales para High-Performance Computing
---
En este repositorio encontrará el material relacionado al curso de redes neuronales para high-performance computing en el programa de ciencias de la computación e inteligencia artificial ofertado en la Universidad Sergio Arboleda.
## Contenido
---
### Corte 1: Conceptos Básicos
- Introducción a `jax`: compilación `jit`, diferenciación automática, GPGPU.
- Tipos de regresión: lineal, logística, multi-lineal, multi-logística.
- Neurona artificial.
### Corte 2: Redes Neuronales
- Perception multicapa.
- Redes convolucionales.
- Conjuntos de datos.
- Experimentación.
### Corte 3: Transformers
- Embeddings.
- Positional Encoding.
- Mecanismos de atención.
- Modelos pre-entrenados.
## Material
---
| Sesión | Tema | Notebook | Video |
| --- | --- | --- | --- |
| 27-07-23 | Introducción Jax | [1_intro_jax.ipynb](https://github.com/juselara1/usa_neural_networks/blob/main/src/1_intro_jax.ipynb) | [enlace](https://drive.google.com/file/d/1k5NshKXbQ4XlNrzzznrtnYPCfu4N7Xg-/view?usp=sharing) |
| 28-07-23 | Descenso estocástico del gradiente | [2_sgd.ipynb](https://github.com/juselara1/usa_neural_networks/blob/main/src/2_sgd.ipynb) | [enlace 1](https://drive.google.com/file/d/1lrsew2o77j2OS3nxaptVqqBGe7ukkFVl/view?usp=sharing) [enlace2](https://drive.google.com/file/d/17IKvT5n-3rf1IVyUORHQmcImjzpXCpyK/view?usp=sharing) |
| 03-08-23 | Neurona Artificial | [3_neurona_artificial.ipynb](https://github.com/juselara1/usa_neural_networks/blob/main/src/3_neurona_artificial.ipynb) | [enlace](https://drive.google.com/file/d/1kZjKJcrdH2N0q_iTZLqvhm9w7UqJNhuL/view?usp=drive_link) |
| 04-08-23 | Redes multicapa | [4_ann.ipnyb](https://github.com/juselara1/usa_neural_networks/blob/main/src/4_ann.ipynb) | [enlace](https://drive.google.com/file/d/1kcpcINyOz2YJdT_Tlqr1YMwUb4pKPRhn/view?usp=drive_link) |
| 10-08-23 | Redes multicapa 2 | [5_multilayer.ipynb](https://github.com/juselara1/usa_neural_networks/blob/main/src/5_multilayer.ipynb) | [enlace](https://drive.google.com/file/d/1UlqBOYX9adCr768fHAW-3Z91Fu4D3xuz/view?usp=drive_link) |
| 11-08-23 | Flax | [6_flax.ipynb](https://github.com/juselara1/usa_neural_networks/blob/main/src/6_flax.ipynb) | [enlace](https://drive.google.com/file/d/14jX0IyDbwOjdqLf7U9S7Ncq6p567HYP9/view?usp=drive_link) |
| 18-08-23 | Flax 2 | [7_flax2.ipynb](https://github.com/juselara1/usa_neural_networks/blob/main/src/7_flax2.ipynb) | [enlace]() |
| 24-08-23 | Optax | [8_optax.ipynb](https://github.com/juselara1/usa_neural_networks/blob/main/src/8_optax.ipynb) | [enlace](https://drive.google.com/file/d/1QG-MzCz1y_J29LYZm1umCcW6ozq4pUd0/view?usp=drive_link) |
| 25-08-23 | Cross-validation | [9_crossval.ipynb](https://github.com/juselara1/usa_neural_networks/blob/main/src/9_crossval.ipynb) | [enlace](https://drive.google.com/file/d/19Rm-F4G5vH_nD0Y9Y3SwqWMkJZ940kcA/view?usp=drive_link) |
# Talleres
- [Taller 1](https://github.com/juselara1/usa_neural_networks/blob/main/src/taller1.ipynb)
## Bibliografía
---
- Deep Learning Book - Ian Goodfellow and Yoshua Bengio and Aaron Courville.
- Pattern Recognition and Machine Learning - Christopher M. Bishop.