Fundamentos de Deep Learning

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SESIÓN 01 (6hrs): Temas ...

SESIÓN 02 (4hrs): Temas ...

etc.

Criterios de evaluación

xx%: TALLERES, cada taller del curso valdrá lo mismo (o distinto, etc.)
yy%: INFORME de XXXX

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Las entregas del curso (informes, notebooks, etc.) se realizarán en el Drive compartido con cada estudiante.

La fecha límite de entrega será el 2020/XX/XX a las 11pm hora colombiana

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Fundamentos de Deep Learning

Descripción del curso

Programación de sesiones

Criterios de evaluación

Entregas

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Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
local		local
README.md		README.md
U1.01 - DL Overview.ipynb		U1.01 - DL Overview.ipynb
U1.02 - Modelos derivados de los datos.ipynb		U1.02 - Modelos derivados de los datos.ipynb
U1.03 - Como se disena un algoritmo de Machine Learning.ipynb		U1.03 - Como se disena un algoritmo de Machine Learning.ipynb
U2.01 - Introduction to Artificial Neural Networks.ipynb		U2.01 - Introduction to Artificial Neural Networks.ipynb
U2.02 - MLP with Keras.ipynb		U2.02 - MLP with Keras.ipynb
U2.03 - Overfitting and regularization.ipynb		U2.03 - Overfitting and regularization.ipynb
U2.04 - [TALLER] Customized loss functions and regularization with Keras.ipynb		U2.04 - [TALLER] Customized loss functions and regularization with Keras.ipynb
U2.05 - Network Architectures - Autoencoders.ipynb		U2.05 - Network Architectures - Autoencoders.ipynb
U2.06 - [TALLER] - Autoencoders.ipynb		U2.06 - [TALLER] - Autoencoders.ipynb
U2.06 - [TALLER] - Autoencoders_SOLUTION.ipynb		U2.06 - [TALLER] - Autoencoders_SOLUTION.ipynb
U2.07 - [TALLER OPCIONAL] - Autoencoders and PCA.ipynb		U2.07 - [TALLER OPCIONAL] - Autoencoders and PCA.ipynb
U2.08 - Network Architectures - Multimodal information.ipynb		U2.08 - Network Architectures - Multimodal information.ipynb
U2.09 - [TALLER] - Multimodal architectures - SOLUTION.ipynb		U2.09 - [TALLER] - Multimodal architectures - SOLUTION.ipynb
U2.09 - [TALLER] - Multimodal architectures.ipynb		U2.09 - [TALLER] - Multimodal architectures.ipynb
U2.10 - Network Architectures - Restricted Boltzman Machines.ipynb		U2.10 - Network Architectures - Restricted Boltzman Machines.ipynb
U2.11 - [TALLER] - Restricted Boltzman Machines - SOLUTION.ipynb		U2.11 - [TALLER] - Restricted Boltzman Machines - SOLUTION.ipynb
U2.11 - [TALLER] - Restricted Boltzman Machines.ipynb		U2.11 - [TALLER] - Restricted Boltzman Machines.ipynb
U2.12 - Vanishing gradients.ipynb		U2.12 - Vanishing gradients.ipynb
U2.13 - [TALLER] - Vanishing gradients - SOLUTION.ipynb		U2.13 - [TALLER] - Vanishing gradients - SOLUTION.ipynb
U2.13 - [TALLER] - Vanishing gradients.ipynb		U2.13 - [TALLER] - Vanishing gradients.ipynb
U2.14 - Weights initialization.ipynb		U2.14 - Weights initialization.ipynb
U2.15 - [TALLER] - Weights initialization - SOLUTION.ipynb		U2.15 - [TALLER] - Weights initialization - SOLUTION.ipynb
U2.15 - [TALLER] - Weights initialization.ipynb		U2.15 - [TALLER] - Weights initialization.ipynb
U3.01 - Designing ML algorithms assisted by a symbolic computing library.ipynb		U3.01 - Designing ML algorithms assisted by a symbolic computing library.ipynb
U3.02 - Tensorflow for symbolic computing and optimization.ipynb		U3.02 - Tensorflow for symbolic computing and optimization.ipynb
U3.03 - Using tf.function.ipynb		U3.03 - Using tf.function.ipynb
U3.04 - [TALLER] TensorFlow Low Level API.ipynb		U3.04 - [TALLER] TensorFlow Low Level API.ipynb
U3.04 - [TALLER] TensorFlow Low Level API_SOLUTION.ipynb		U3.04 - [TALLER] TensorFlow Low Level API_SOLUTION.ipynb
U3.05 - Batch Normalization.ipynb		U3.05 - Batch Normalization.ipynb
U3.06 - [TALLER] Batch Normalization.ipynb		U3.06 - [TALLER] Batch Normalization.ipynb
U3.06 - [TALLER] Batch Normalization_SOLUTION.ipynb		U3.06 - [TALLER] Batch Normalization_SOLUTION.ipynb
U4.01 - Convolutional Neural Networks.ipynb		U4.01 - Convolutional Neural Networks.ipynb
U4.02 - [TALLER] - Implementing convolutions.ipynb		U4.02 - [TALLER] - Implementing convolutions.ipynb
U4.02 - [TALLER] - Implementing convolutions_SOLUTION.ipynb		U4.02 - [TALLER] - Implementing convolutions_SOLUTION.ipynb
U4.03 - Transfer learning.ipynb		U4.03 - Transfer learning.ipynb
U4.04 - [TALLER] - Transfer Learning.ipynb		U4.04 - [TALLER] - Transfer Learning.ipynb
U4.05 - [TALLER] - CNN Architectures.ipynb		U4.05 - [TALLER] - CNN Architectures.ipynb
U4.05 - [TALLER] - CNN Architectures_SOLUTION.ipynb		U4.05 - [TALLER] - CNN Architectures_SOLUTION.ipynb
U4.06 - Object Detection Tasks.ipynb		U4.06 - Object Detection Tasks.ipynb
U4.07 - [TALLER] Object Detection SOLUTION.ipynb		U4.07 - [TALLER] Object Detection SOLUTION.ipynb
U4.07 - [TALLER] Object Detection.ipynb		U4.07 - [TALLER] Object Detection.ipynb
U4.08 - Transposed convolutions for image segmentation.ipynb		U4.08 - Transposed convolutions for image segmentation.ipynb
U4.09 - Image Segmentation.ipynb		U4.09 - Image Segmentation.ipynb
U4.10 - [TALLER] - Transposed convolutions - SOLUTION.ipynb		U4.10 - [TALLER] - Transposed convolutions - SOLUTION.ipynb
U4.10 - [TALLER] - Transposed convolutions.ipynb		U4.10 - [TALLER] - Transposed convolutions.ipynb
U5.01 - Recurrent Neural Networks.ipynb		U5.01 - Recurrent Neural Networks.ipynb
U5.02 - Long Short Term Memory RNN.ipynb		U5.02 - Long Short Term Memory RNN.ipynb
U5.03 - [TALLER] RNN - Multivariate time series prediction.ipynb		U5.03 - [TALLER] RNN - Multivariate time series prediction.ipynb
U5.04 - Truncated BPTT.ipynb		U5.04 - Truncated BPTT.ipynb
U5.05 - Basic concepts of text processing.ipynb		U5.05 - Basic concepts of text processing.ipynb
U5.06 - [TALLER] - Padding, Masking - Sentiment Analysis.ipynb		U5.06 - [TALLER] - Padding, Masking - Sentiment Analysis.ipynb
U5.07 - Sequences generation using LSTM.ipynb		U5.07 - Sequences generation using LSTM.ipynb
U5.08- Bidirectional RNNs - Attention Model.ipynb		U5.08- Bidirectional RNNs - Attention Model.ipynb
U5.09- [TALLER] ELMo - Name Entity Recognition.ipynb		U5.09- [TALLER] ELMo - Name Entity Recognition.ipynb
U5.10- Self-Attention - Transformer - BERT.ipynb		U5.10- Self-Attention - Transformer - BERT.ipynb
U5.11- [TALLER] Sentiment Analysis using BERT.ipynb		U5.11- [TALLER] Sentiment Analysis using BERT.ipynb
U5.12 (CNN-LSTM arquitecture).ipynb		U5.12 (CNN-LSTM arquitecture).ipynb
init.py		init.py

UDEA-Esp-Analitica-y-Ciencia-de-Datos/EACD-07-DEEP-LEARNING

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Fundamentos de Deep Learning

Descripción del curso

Programación de sesiones

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