9ac7bce1d1c27e8fa871207663ad2e12
|
Evaluate_new_clay_with_old_model.ipynb
|
Evaluate new Clay Data with Old Model
|
bb6a4d42eb35626a35839f2a1a728016
|
Evaluate_new_clay_with_new_model.ipynb
|
Evaluate New Clay Data with Model Trained on New Clay Data
|
5e5eb286e5a900a70423b816537ece1d
|
Evaluate_sand_with_sand.ipynb
|
Evaluate Sand Data with CNN model trained on Sand Data
|
1e78d7a899540527b2238ef9e264fc80
|
Evaluate_sand_with_clay.ipynb
|
Evaluation on Sand data with model trained on Clay Data
|
3aae0c13cd9d32dcf6409042cbc91bfc
|
understanding-word-vectors.ipynb
|
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/
|
43c9db281b0bd3224114084c44263c13
|
Sinusoidal_Testing.ipynb
Time_Biased_CostFunction.ipynb
NeuralNetDiffEq_ODE.ipynb
SODE Testing.ipynb
|
NeuralNetDiffEq.jl
|
43ed7dfa7721523f6284ff691f936693
|
SODE Tests.ipynb
|
|
d37b8587cd016127a27648e8151a0b24
|
NeuralNetDiffEq Testing.ipynb
|
|
78185d23da568b83d5eac166d8512782
|
TSA_ELM.ipynb
|
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.
|
03d48b4cbf6e636ddeb3
|
SimplePerceptron.ipynb
|
|
2443f827da0d5c68deed
|
MultilayerNN.ipynb
|
|