> For the complete documentation index, see [llms.txt](https://stephanosterburg.gitbook.io/scrapbook/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://stephanosterburg.gitbook.io/scrapbook/career/learn.co/deep-learning/practice-datasets.md).

# PRACTICE DATASETS

### PART 1. ARTIFICIAL NEURAL NETWORKS (ANN)

Datasets & Templates:

* [Artificial\_Neural\_Networks](http://www.superdatascience.com/wp-content/uploads/2017/03/Artificial_Neural_Networks.zip)

Additional Reading:

* Yann LeCun et al., 1998, [*Efficient BackProp*](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)
* By Xavier Glorot et al., 2011, [*Deep sparse rectifier neural networks*](http://jmlr.org/proceedings/papers/v15/glorot11a/glorot11a.pdf)
* CrossValidated, 2015, [*A list of cost functions used in neural networks, alongside applications*](http://stats.stackexchange.com/questions/154879/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)](https://iamtrask.github.io/2015/07/27/python-network-part2/)
* Michael Nielsen, 2015, [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/chap2.html)

### PART 2. CONVOLUTIONAL NEURAL NETWORKS (CNN)

Datasets & Templates:

* [Convolutional\_Neural\_Networks](http://www.superdatascience.com/wp-content/uploads/2017/04/Convolutional_Neural_Networks.zip)

Additional Reading:

* Yann LeCun et al., 1998, [*Gradient-Based Learning Applied to Document Recognition*](http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf)
* Jianxin Wu, 2017, [Introduction to Convolutional Neural Networks](http://cs.nju.edu.cn/wujx/paper/CNN.pdf)
* C.-C. Jay Kuo, 2016, [Understanding Convolutional Neural Networks with A Mathematical Model](https://arxiv.org/pdf/1609.04112.pdf)
* Kaiming He et al., 2015, [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://arxiv.org/pdf/1502.01852.pdf)
* Dominik Scherer et al., 2010, [Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition](http://ais.uni-bonn.de/papers/icann2010_maxpool.pdf)
* Adit Deshpande, 2016, [The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3)](https://adeshpande3.github.io/adeshpande3.github.io/The-9-Deep-Learning-Papers-You-Need-To-Know-About.html)
* Rob DiPietro, 2016, [A Friendly Introduction to Cross-Entropy Loss](https://rdipietro.github.io/friendly-intro-to-cross-entropy-loss/)
* Peter Roelants, 2016, [How to implement a neural network Intermezzo 2](http://peterroelants.github.io/posts/neural_network_implementation_intermezzo02/)

### PART 3. RECURRENT NEURAL NETWORKS (RNN)

Datasets & Templates:

* [Recurrent\_Neural\_Networks](http://www.superdatascience.com/wp-content/uploads/2017/03/Recurrent_Neural_Networks-1.zip)
* [Homework\_Challenge](http://www.superdatascience.com/wp-content/uploads/2017/03/Homework_Challenge.zip)

Additional Reading:

* Oscar Sharp & Benjamin, 2016, [*Sunspring*](https://arstechnica.com/the-multiverse/2016/06/an-ai-wrote-this-movie-and-its-strangely-moving/)
* Sepp (Josef) Hochreiter, 1991, [Untersuchungen zu dynamischen neuronalen Netzen](http://people.idsia.ch/~juergen/SeppHochreiter1991ThesisAdvisorSchmidhuber.pdf)
* Yoshua Bengio, 1994, [Learning Long-Term Dependencies with Gradient Descent is Difficult](http://www-dsi.ing.unifi.it/~paolo/ps/tnn-94-gradient.pdf)
* Razvan Pascanu, 2013, [On the difficulty of training recurrent neural networks](http://www.jmlr.org/proceedings/papers/v28/pascanu13.pdf)
* Sepp Hochreiter & Jurgen Schmidhuber, 1997, [Long Short-Term Memory](http://www.bioinf.jku.at/publications/older/2604.pdf)
* Christopher Olah, 2015, [Understanding LSTM Networks](http://colah.github.io/posts/2015-08-Understanding-LSTMs/)
* Shi Yan, 2016, [Understanding LSTM and its diagrams](https://medium.com/@shiyan/understanding-lstm-and-its-diagrams-37e2f46f1714)
* Andrej Karpathy, 2015, [The Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)
* Andrej Karpathy, 2015, [Visualizing and Understanding Recurrent Networks](https://arxiv.org/pdf/1506.02078.pdf)
* Klaus Greff, 2015, [LSTM: A Search Space Odyssey](https://arxiv.org/pdf/1503.04069.pdf)
* Xavier Glorot, 2011, [*Deep sparse rectifier neural networks*](http://jmlr.org/proceedings/papers/v15/glorot11a/glorot11a.pdf)

### PART 4. SELF ORGANIZING MAPS (SOM)

Datasets & Templates:

* [Self\_Organizing\_Maps](http://www.superdatascience.com/wp-content/uploads/2017/03/Self_Organizing_Maps.zip)
* [Mega\_Case\_Study](http://www.superdatascience.com/wp-content/uploads/2017/03/Mega_Case_Study.zip)

Additional Reading:

* Tuevo Kohonen, 1990, [The Self-Organizing Map](http://sci2s.ugr.es/keel/pdf/algorithm/articulo/1990-Kohonen-PIEEE.pdf)
* Mat Buckland, 2004?, [Kohonen's Self Organizing Feature Maps](http://www.ai-junkie.com/ann/som/som1.html)
* Nadieh Bremer, 2003, [SOM – Creating hexagonal heatmaps with D3.js](https://www.visualcinnamon.com/2013/07/self-organizing-maps-creating-hexagonal.html)

### PART 5. BOLTZMANN MACHINES (BM)

Datasets & Templates:

* [Boltzmann\_Machines](http://www.superdatascience.com/wp-content/uploads/2017/03/Boltzmann_Machines.zip)

Additional Reading:

* Yann LeCun, 2006, [*A Tutorial on Energy-Based Learning*](http://yann.lecun.com/exdb/publis/pdf/lecun-06.pdf)
* Jaco Van Dormael, 2009, [Mr. Nobody](http://www.imdb.com/title/tt0485947/)
* Geoffrey Hinton, 2006, [*A fast learning algorithm for deep belief nets*](https://www.cs.toronto.edu/~hinton/absps/fastnc.pdf)
* Oliver Woodford, 2012?, [Notes on Contrastive Divergence](http://www.robots.ox.ac.uk/~ojw/files/NotesOnCD.pdf)
* Yoshua Bengio, 2006, [*Greedy Layer-Wise Training of Deep Networks*](http://www.iro.umontreal.ca/~lisa/pointeurs/BengioNips2006All.pdf)
* Geoffrey Hinton, 1995, [*The wake-sleep algorithm for unsupervised neural networks*](http://www.gatsby.ucl.ac.uk/~dayan/papers/hdfn95.pdf)
* Ruslan Salakhutdinov, 2009?, [Deep Boltzmann Machines](http://www.utstat.toronto.edu/~rsalakhu/papers/dbm.pdf)

### PART 6. AUTOENCODERS (AE)

Datasets & Templates:

* [AutoEncoders](http://www.superdatascience.com/wp-content/uploads/2017/03/AutoEncoders.zip)

Additional Reading:

* Malte Skarupke, 2016, [*Neural Networks Are Impressively Good At Compression*](https://probablydance.com/2016/04/30/neural-networks-are-impressively-good-at-compression/)
* Francois Chollet, 2016, [Building Autoencoders in Keras](https://blog.keras.io/building-autoencoders-in-keras.html)
* Chris McCormick, 2014, [Deep Learning Tutorial – Sparse Autoencoder](http://mccormickml.com/2014/05/30/deep-learning-tutorial-sparse-autoencoder/)
* Eric Wilkinson, 2014, [Deep Learning: Sparse Autoencoders](http://www.ericlwilkinson.com/blog/2014/11/19/deep-learning-sparse-autoencoders)
* Alireza Makhzani, 2014, [k-Sparse Autoencoders](https://arxiv.org/pdf/1312.5663.pdf)
* Pascal Vincent, 2008, [*Extracting and Composing Robust Features with Denoising Autoencoders*](http://www.cs.toronto.edu/~larocheh/publications/icml-2008-denoising-autoencoders.pdf)
* Salah Rifai, 2011, [*Contractive Auto-Encoders: Explicit Invariance During Feature Extraction*](http://machinelearning.wustl.edu/mlpapers/paper_files/ICML2011Rifai_455.pdf)
* Pascal Vincent, 2010, [*Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion*](http://www.jmlr.org/papers/volume11/vincent10a/vincent10a.pdf)
* Geoffrey Hinton, 2006, [Reducing the Dimensionality of Data with Neural Networks](https://www.cs.toronto.edu/~hinton/science.pdf)


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