El objetivo de este curso de Deep Learning es aprender los fundamentos del Machine Learning, Deep Learning de forma practica y teorica.
Contenido del curso:
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Python
- Introduccion a Python
- Modulo Numpy
- Modulo Pandas
- Visualizacion de datos con Matplotlib y Seaborn
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Fundamentos Matematicos para el Deep Learning
- Estadistica aplicada al Machine Learning
- Probabilidad aplicada al ML
- Algebra lineal y Calculo Vectorial aplicada al ML
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Fundamentos del Machine Learning
- Regression Lineal
- Regresion Logistica
- Support Vector Machines
- Decision Trees, Random Forest and Gradient Boosting Machines
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Fundamentos de Deep Learning
- Perceptron
- Foward Propagation
- Backpropagation
- Redes Neuronales Multicapa
- Bases de Pytorch y Tensorflow
- Tensorflow eager execution y Keras API
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Deep Learning for Computer Vision
- Redes Neuronales Convolucionales
- Transfer Learning
- Clasificacion en Imagenes
- Deteccion de Objetos con Tensorflow Object Detection API
- Algoritmos de Segmentacion
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Deep Learning for Natural Language Proccessing
- Redes Neuronales Recurrentes
- Gated Recurrent Units
- Long Short Term Memory
- Bidirectional Recurrent Neural Networks
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Aprendizaje No supervisado
- Variational Autoencoders
- Generative Adversarial Neural Networks
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Introduccion al Deep Reinforcement Learning
Recursos para aprender Deep Learning
- Machine Learning Course
- Machine Learning Specialization
- CS229 Machine Learning
- CS231 Deep Learning for Computer Vision
- CS224 Natural Language Processing
- Deep Learning by Google
- Data Scientist with Python
- Deep Reinforcement Learning
- Neural Networks for Machine Learning
- Oxford's Machine Learning
- NYU's Deep Learning
- Cambridge Information Theory, Pattern Recognition, and Neural Networks
- MIT Statistical Learning Theory and Applications
- CMU Statistical Machine Learning
- CMU Convex Optimization
Libros