PRACTICE DATASETS
PART 1. ARTIFICIAL NEURAL NETWORKS (ANN)
Datasets & Templates:
Additional Reading:
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
PART 2. CONVOLUTIONAL NEURAL NETWORKS (CNN)
Datasets & Templates:
Additional Reading:
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
PART 3. RECURRENT NEURAL NETWORKS (RNN)
Datasets & Templates:
Additional Reading:
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
PART 4. SELF ORGANIZING MAPS (SOM)
Datasets & Templates:
Additional Reading:
Tuevo Kohonen, 1990, The Self-Organizing Map
Mat Buckland, 2004?, Kohonen's Self Organizing Feature Maps
Nadieh Bremer, 2003, SOM – Creating hexagonal heatmaps with D3.js
PART 5. BOLTZMANN MACHINES (BM)
Datasets & Templates:
Additional Reading:
Yann LeCun, 2006, A Tutorial on Energy-Based Learning
Jaco Van Dormael, 2009, Mr. Nobody
Geoffrey Hinton, 2006, A fast learning algorithm for deep belief nets
Oliver Woodford, 2012?, Notes on Contrastive Divergence
Yoshua Bengio, 2006, Greedy Layer-Wise Training of Deep Networks
Geoffrey Hinton, 1995, The wake-sleep algorithm for unsupervised neural networks
Ruslan Salakhutdinov, 2009?, Deep Boltzmann Machines
PART 6. AUTOENCODERS (AE)
Datasets & Templates:
Additional Reading:
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
Geoffrey Hinton, 2006, Reducing the Dimensionality of Data with Neural Networks
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