Masterclass en Inteligencia Artificial

Repositorio de la Masterclass en Inteligencia Artificial

Bienvenido

Bienvenido al repositorio de datos para el curso Masterclass en Inteligencia Artificial de Kirill Eremenko, Hadelin de Ponteves y Juan Gabriel Gomila. Aquí encontrarás los datasets y materiales complementarios del curso. Disfrútalos!

Sección 1. Introduction

• Estructura del curso
• Modelo del Mundo Completo

Lecturas Adicionales

• Artículo del Modelo del Mundo Completo
• El mejor recurso sobre Estrategias Evolutivas

Sección 2. Redes Neuronales Artificiales (ANN)

Lecturas Adicionales

• Yann LeCun et al., 1998, Efficient BackProp
• By Xavier Glorot et al., 2011, Deep sparse rectifier neural networks
• CrossValidated, 2015, A list of cost functions used in neural networks, alongside applications
• Andrew Trask, 2015, A Neural Network in 13 lines of Python (Part 2 - Gradient Descent)
• Michael Nielsen, 2015, Neural Networks and Deep Learning

Sección 3. Redes Neuronales Convolucionales (CNN)

Lecturas Adicionales

• Yann LeCun et al., 1998, Gradient-Based Learning Applied to Document Recognition
• Jianxin Wu, 2017, Introduction to Convolutional Neural Networks
• C.-C. Jay Kuo, 2016, Understanding Convolutional Neural Networks with A Mathematical Model
• Kaiming He et al., 2015, Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
• Dominik Scherer et al., 2010, Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition
• Adit Deshpande, 2016, The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3)
• Rob DiPietro, 2016, A Friendly Introduction to Cross-Entropy Loss
• Peter Roelants, 2016, How to implement a neural network Intermezzo 2

Sección 4. AutoEncoders (AE)

Lecturas Adicionales

• Malte Skarupke, 2016, Neural Networks Are Impressively Good At Compression
• Francois Chollet, 2016, Building Autoencoders in Keras
• Chris McCormick, 2014, Deep Learning Tutorial - Sparse Autoencoder
• Eric Wilkinson, 2014, Deep Learning: Sparse Autoencoders
• Alireza Makhzani, 2014, k-Sparse Autoencoders
• Pascal Vincent, 2008, Extracting and Composing Robust Features with Denoising Autoencoders
• Salah Rifai, 2011, Contractive Auto-Encoders: Explicit Invariance During Feature Extraction
• Pascal Vincent, 2010, Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
• Geoffrey Hinton, 2006, Reducing the Dimensionality of Data with Neural Networks

Sección 5. AutoEncoders Variacionales (VAE)

Lecturas Adicionales

• Irhum Shafkat, 2018, Intuitively Understanding Variational Autoencoders
• Diederik P. Kingma and Max Welling, 2014, Auto-Encoding Variational Bayes
• Francois Chollet, 2016, Building Autoencoders in Keras
• Chris McCormick, 2014, Deep Learning Tutorial - Sparse Autoencoder
• Eric Wilkinson, 2014, Deep Learning: Sparse Autoencoders
• Alireza Makhzani, 2014, k-Sparse Autoencoders
• Pascal Vincent, 2008, Extracting and Composing Robust Features with Denoising Autoencoders
• Salah Rifai, 2011, Contractive Auto-Encoders: Explicit Invariance During Feature Extraction
• Pascal Vincent, 2010, Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
• Geoffrey Hinton, 2006, Reducing the Dimensionality of Data with Neural Networks

Sección 6. Implementación de CNN-VAE

Sección 7. Redes Neuronales Recurrentes (RNN)

Lecturas Adicionales

• Oscar Sharp & Benjamin, 2016, Sunspring
• Sepp (Josef) Hochreiter, 1991, Untersuchungen zu dynamischen neuronalen Netzen
• Yoshua Bengio, 1994, Learning Long-Term Dependencies with Gradient Descent is Difficult
• Razvan Pascanu, 2013, On the difficulty of training recurrent neural networks
• Sepp Hochreiter & Jurgen Schmidhuber, 1997, Long Short-Term Memory
• Christopher Olah, 2015, Understanding LSTM Networks
• Shi Yan, 2016, Understanding LSTM and its diagrams
• Andrej Karpathy, 2015, The Unreasonable Effectiveness of Recurrent Neural Networks
• Andrej Karpathy, 2015, Visualizing and Understanding Recurrent Networks
• Klaus Greff, 2015, LSTM: A Search Space Odyssey
• Xavier Glorot, 2011, Deep sparse rectifier neural networks

Sección 7. Red Neuronal de Densidad Mixta (MDN)

Sección 8. Implementación de MDN-RNN

Sección 9. Reinforcement Learning

Lecturas Adicionales

• Arthur Juliani, 2016, Simple Reinforcement Learning with Tensorflow (10 Parts)
• Richard Sutton et al., 1998, Reinforcement Learning I: Introduction
• Richard Bellman, 1954, [The Theory of Dynamic Programming](The Theory of Dynamic Programming)
• D. J. White, 1993, A Survey of Applications of Markov Decision Processes
• Martijn van Otterlo, 2009, Markov Decision Processes: Concepts and Algorithms
• Richard Sutton, 1988, Learning to Predict by the Methods of Temporal Differences

Sección 10. Deep Neuro Evolution (GA, ES)