This repository hosts Jupyter Notebooks used for TensorFlow examples of Neural Networks.
Jupyter Notebook(s) written in Python.
| Notebook | Description |
|---|---|
| TF_ANN_Single_Layer_Sequential_Regression.ipynb | The purpose of this notebook is to show a simple example of an Artificial Neural Network (ANN) using TensorFlow to predict the cost of an apartment per month based on the number of rooms. Assume the cost of an apartment per month = 500 + 250 per bedroom, such that 1 bedroom apartment costs 750 per month, 2 bedroom apartment costs 1000, etc. We want to predict the cost of a 4 bedroom apartment with this neural network. |
| TF_DNN_Multi_Layer_Sequential_Classification.ipynb | The purpose of this notebook is to create a Multi-Layer Perceptron (MLP) Neural Network using the Sequential API from TensorFlow for prediction on the fashion mnist dataset. |
| TF_CNN_Sequential_Classification_Softmax.ipynb | The purpose of this notebook is to create a Convolutional Neural Network (CNN) with TensorFlow for prediction with fashion mnist dataset. |
| TF_CNN_Sequential_Classification_Sigmoid_Simple.ipynb | The purpose of this notebook is to create a Convolutional Neural Network (CNN) with TensorFlow for prediction with cats vs. dogs dataset. |
| TF_CNN_TransferLearning.ipynb | The purpose of this notebook is to use Transfer Learning with TensorFlow for prediction with cats vs. dogs dataset. |
| TF_CNN_Sequential_NLP_imdb_reviews.ipynb | The purpose of this notebook is to use Embedding for a Natural Language Processing (NLP) task with TensorFlow on the imbd_reviews dataset. Predictions are "1" for a bad review and "0" for a good review. |
| TF_LSTM_GRU_NLP_TextGeneration.ipynb | The purpose of this notebook is to use Embedding for a Natural Language Processing (NLP) task with TensorFlow on the Text Generation from Eminem. This uses entire words for generating lyrics. |
| TF_LSTM_GRU_NLP_TextGeneration_SingleLetters.ipynb | The purpose of this notebook is to use Embedding for a Natural Language Processing (NLP) task with TensorFlow on the Text Generation from Eminem. This uses single characters instead of whole words for generating lyrics. |
| TF_TimeSeries_Custom_TFDataset.ipynb | The purpose of this notebook is to explore Time Series datasets with TensorFlow on a variety of neural network architectures using a custom TensorFlow dataset. |
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Python. a) Travis E. Oliphant. Python for Scientific Computing, Computing in Science & Engineering, 9, 10–20 (2007) b) K. Jarrod Millman and Michael Aivazis. Python for Scientists and Engineers, Computing in Science & Engineering, 13, 9–12 (2011)
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TensorFlow. Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis,Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Rafal Jozefowicz, Yangqing Jia,Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Mike Schuster,Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Jonathon Shlens,Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker,Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas,Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke,Yuan Yu, and Xiaoqiang Zheng. TensorFlow: Large-scale machine learning on heterogeneous systems, 2015. Software available from tensorflow.org.
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SciPy. Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, CJ Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E.A. Quintero, Charles R Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors. (2019) SciPy 1.0–Fundamental Algorithms for Scientific Computing in Python. preprint arXiv:1907.10121
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NumPy. a) Travis E. Oliphant. A guide to NumPy, USA: Trelgol Publishing, (2006). b) Stéfan van der Walt, S. Chris Colbert and Gaël Varoquaux. The NumPy Array: A Structure for Efficient Numerical Computation, Computing in Science & Engineering, 13, 22–30 (2011)
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IPython. a) Fernando Pérez and Brian E. Granger. IPython: A System for Interactive Scientific Computing, Computing in Science & Engineering, 9, 21–29 (2007)
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Matplotlib. J. D. Hunter, “Matplotlib: A 2D Graphics Environment”, Computing in Science & Engineering, vol. 9, no. 3, pp. 90–95, 2007.
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Pandas. Wes McKinney. Data Structures for Statistical Computing in Python, Proceedings of the 9th Python in Science Conference, 51–56 (2010)
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Scikit-Learn. Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, Édouard Duchesnay. Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research, 12, 2825–2830 (2011)
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Scikit-Image. Stéfan van der Walt, Johannes L. Schönberger, Juan Nunez-Iglesias, François Boulogne, Joshua D. Warner, Neil Yager, Emmanuelle Gouillart, Tony Yu and the scikit-image contributors. scikit-image: Image processing in Python, PeerJ 2:e453 (2014)
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