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On this page
  • PART 1. ARTIFICIAL NEURAL NETWORKS (ANN)
  • PART 2. CONVOLUTIONAL NEURAL NETWORKS (CNN)
  • PART 3. RECURRENT NEURAL NETWORKS (RNN)
  • PART 4. SELF ORGANIZING MAPS (SOM)
  • PART 5. BOLTZMANN MACHINES (BM)
  • PART 6. AUTOENCODERS (AE)
  1. career
  2. learn.co
  3. Deep Learning

PRACTICE DATASETS

PreviousBackpropagation - math notationNextBig Data

Last updated 6 years ago

PART 1. ARTIFICIAL NEURAL NETWORKS (ANN)

Datasets & Templates:

Additional Reading:

  • Yann LeCun et al., 1998,

  • By Xavier Glorot et al., 2011,

  • CrossValidated, 2015,

  • Andrew Trask, 2015,

  • Michael Nielsen, 2015,

PART 2. CONVOLUTIONAL NEURAL NETWORKS (CNN)

Datasets & Templates:

Additional Reading:

  • Yann LeCun et al., 1998,

  • Jianxin Wu, 2017,

  • C.-C. Jay Kuo, 2016,

  • Kaiming He et al., 2015,

  • Dominik Scherer et al., 2010,

  • Adit Deshpande, 2016,

  • Rob DiPietro, 2016,

  • Peter Roelants, 2016,

PART 3. RECURRENT NEURAL NETWORKS (RNN)

Datasets & Templates:

Additional Reading:

PART 4. SELF ORGANIZING MAPS (SOM)

Datasets & Templates:

Additional Reading:

PART 5. BOLTZMANN MACHINES (BM)

Datasets & Templates:

Additional Reading:

PART 6. AUTOENCODERS (AE)

Datasets & Templates:

Additional Reading:

Oscar Sharp & Benjamin, 2016,

Sepp (Josef) Hochreiter, 1991,

Yoshua Bengio, 1994,

Razvan Pascanu, 2013,

Sepp Hochreiter & Jurgen Schmidhuber, 1997,

Christopher Olah, 2015,

Shi Yan, 2016,

Andrej Karpathy, 2015,

Andrej Karpathy, 2015,

Klaus Greff, 2015,

Xavier Glorot, 2011,

Tuevo Kohonen, 1990,

Mat Buckland, 2004?,

Nadieh Bremer, 2003,

Yann LeCun, 2006,

Jaco Van Dormael, 2009,

Geoffrey Hinton, 2006,

Oliver Woodford, 2012?,

Yoshua Bengio, 2006,

Geoffrey Hinton, 1995,

Ruslan Salakhutdinov, 2009?,

Malte Skarupke, 2016,

Francois Chollet, 2016,

Chris McCormick, 2014,

Eric Wilkinson, 2014,

Alireza Makhzani, 2014,

Pascal Vincent, 2008,

Salah Rifai, 2011,

Pascal Vincent, 2010,

Geoffrey Hinton, 2006,

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Deep sparse rectifier neural networks
A list of cost functions used in neural networks, alongside applications
A Neural Network in 13 lines of Python (Part 2 – Gradient Descent)
Neural Networks and Deep Learning
Convolutional_Neural_Networks
Gradient-Based Learning Applied to Document Recognition
Introduction to Convolutional Neural Networks
Understanding Convolutional Neural Networks with A Mathematical Model
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition
The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3)
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How to implement a neural network Intermezzo 2
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Sunspring
Untersuchungen zu dynamischen neuronalen Netzen
Learning Long-Term Dependencies with Gradient Descent is Difficult
On the difficulty of training recurrent neural networks
Long Short-Term Memory
Understanding LSTM Networks
Understanding LSTM and its diagrams
The Unreasonable Effectiveness of Recurrent Neural Networks
Visualizing and Understanding Recurrent Networks
LSTM: A Search Space Odyssey
Deep sparse rectifier neural networks
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Mega_Case_Study
The Self-Organizing Map
Kohonen's Self Organizing Feature Maps
SOM – Creating hexagonal heatmaps with D3.js
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A Tutorial on Energy-Based Learning
Mr. Nobody
A fast learning algorithm for deep belief nets
Notes on Contrastive Divergence
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The wake-sleep algorithm for unsupervised neural networks
Deep Boltzmann Machines
AutoEncoders
Neural Networks Are Impressively Good At Compression
Building Autoencoders in Keras
Deep Learning Tutorial – Sparse Autoencoder
Deep Learning: Sparse Autoencoders
k-Sparse Autoencoders
Extracting and Composing Robust Features with Denoising Autoencoders
Contractive Auto-Encoders: Explicit Invariance During Feature Extraction
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
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