Repositorio de la Masterclass en Inteligencia Artificial
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!
- 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
- 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
- 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
- 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
- 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
- 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