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9ac7bce1d1c27e8fa871207663ad2e12
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Evaluate_new_clay_with_old_model.ipynb
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Evaluate new Clay Data with Old Model
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bb6a4d42eb35626a35839f2a1a728016
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Evaluate_new_clay_with_new_model.ipynb
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Evaluate New Clay Data with Model Trained on New Clay Data
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5e5eb286e5a900a70423b816537ece1d
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Evaluate_sand_with_sand.ipynb
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Evaluate Sand Data with CNN model trained on Sand Data
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1e78d7a899540527b2238ef9e264fc80
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Evaluate_sand_with_clay.ipynb
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Evaluation on Sand data with model trained on Clay Data
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3aae0c13cd9d32dcf6409042cbc91bfc
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understanding-word-vectors.ipynb
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Understanding word vectors: A tutorial for "Reading and Writing Electronic Text," a class I teach at ITP. (Python 2.7) Code examples released under CC0 https://creativecommons.org/choose/zero/, other text released under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/
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43c9db281b0bd3224114084c44263c13
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Sinusoidal_Testing.ipynb
Time_Biased_CostFunction.ipynb
NeuralNetDiffEq_ODE.ipynb
SODE Testing.ipynb
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NeuralNetDiffEq.jl
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43ed7dfa7721523f6284ff691f936693
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SODE Tests.ipynb
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d37b8587cd016127a27648e8151a0b24
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NeuralNetDiffEq Testing.ipynb
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78185d23da568b83d5eac166d8512782
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TSA_ELM.ipynb
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How to use ELM (Extreme Learning Machines) for time series forecasting. This example demonstrates TS forecasting with ELMs. It uses Python-ELM for implementation of ELMs and sklearn, pandas and matplotlib for data processing and visualization.
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03d48b4cbf6e636ddeb3
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SimplePerceptron.ipynb
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2443f827da0d5c68deed
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MultilayerNN.ipynb
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