Open philusnarh opened 5 years ago
Martin: martin_random_forest https://nbviewer.jupyter.org/urls/dl.dropbox.com/s/xrk4e5wbxob2ilg/martin_random_forest.ipynb
Martin: multiple_regression https://nbviewer.jupyter.org/urls/dl.dropbox.com/s/fm0tt059txnnriv/martin_2_multiple_regression1.ipynb
Martin: multiple_regression https://nbviewer.jupyter.org/urls/dl.dropbox.com/s/3imkmd9gtrfg7xv/martin_results.ipynb
https://www.earthdatascience.org/courses/earth-analytics-python/use-time-series-data-in-python/date-time-types-in-pandas-python/
Transfer Learning for Image Classification in Keras https://towardsdatascience.com/transfer-learning-for-image-classification-in-keras-5585d3ddf54e
https://www.kaggle.com/drscarlat/unsupervised-anomaly-detection
https://towardsdatascience.com/machine-learning-for-anomaly-detection-and-condition-monitoring-d4614e7de770
https://machinelearningmastery.com/how-to-use-statistics-to-identify-outliers-in-data/ https://www.analyticsvidhya.com/blog/2016/02/time-series-forecasting-codes-python/ https://towardsdatascience.com/time-series-in-python-exponential-smoothing-and-arima-processes-2c67f2a52788 https://www.geeksforgeeks.org/random-walk-implementation-python/ https://towardsdatascience.com/anomaly-detection-with-lstm-in-keras-8d8d7e50ab1b https://machinelearningmastery.com/white-noise-time-series-python/
How to Get Started with Deep Learning for Time Series Forecasting https://machinelearningmastery.com/gentle-introduction-random-walk-times-series-forecasting-python/
Scipy.interpolate.griddata regridding data. http://christopherbull.com.au/python/scipy-interpolate-griddata/
How to Develop a Conditional GAN (cGAN) From Scratch https://machinelearningmastery.com/how-to-develop-a-conditional-generative-adversarial-network-from-scratch/
How to Explore the GAN Latent Space When Generating Faces https://machinelearningmastery.com/how-to-interpolate-and-perform-vector-arithmetic-with-faces-using-a-generative-adversarial-network/
How to Develop a GAN for Generating Handwritten Digits https://machinelearningmastery.com/how-to-develop-a-generative-adversarial-network-for-an-mnist-handwritten-digits-from-scratch-in-keras/
A Gentle Introduction to Generative Adversarial Networks (GANs) https://machinelearningmastery.com/what-are-generative-adversarial-networks-gans/
More on GANs https://machinelearningmastery.com/?s=GAN&post_type=post&submit=Search
Algorithm assesses image and video quality https://www.vision-systems.com/boards-software/article/16736698/algorithm-assesses-image-and-video-quality
Denoise AE http://i-systems.github.io/HSE545/iAI/DL/topics/05_autoencoder/02_DAE.html https://becominghuman.ai/variational-autoencoders-for-new-fruits-with-keras-and-pytorch-6d0cfc4eeabd http://www.kecl.ntt.co.jp/people/kameoka.hirokazu/Demos/mvae-ass/index.html https://www.datasciencecentral.com/profiles/blogs/an-elegant-way-to-represent-forward-propagation-and-back https://wiseodd.github.io/techblog/2016/12/10/variational-autoencoder/
Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras https://machinelearningmastery.com/handle-missing-timesteps-sequence-prediction-problems-python/ https://machinelearningmastery.com/how-to-develop-lstm-models-for-time-series-forecasting/
Confusion Matrix https://leandeep.com/datalab-kaggle/comprehensive-data-exploration-with-python.html https://seaborn.pydata.org/generated/seaborn.heatmap.html
3 Techniques to Extract Features from Image Data using Python https://www.analyticsvidhya.com/blog/2019/08/3-techniques-extract-features-from-image-data-machine-learning-python/
Analyze a Soccer game using Tensorflow Object Detection and OpenCV https://towardsdatascience.com/analyse-a-soccer-game-using-tensorflow-object-detection-and-opencv-e321c230e8f2
How to scrape businesses’ info with Python and Beautiful Soup https://medium.com/employbl/scrape-the-web-for-amsterdam-coffeeshops-with-python-and-beautiful-soup-19ed25394234
Outlier detection 101: Median and Interquartile range. https://medium.com/@davidnh8/outlier-detection-101-median-and-interquartile-range-cc9dde94c0ac
Bankruptcy Analysis [classification] https://www.kaggle.com/ginelledsouza/bankruptcy-analysis https://www.kaggle.com/nandalald/bankruptcy-prediction-using-ann-f1-score-97
Martin: martin_random_forest https://nbviewer.jupyter.org/urls/dl.dropbox.com/s/xrk4e5wbxob2ilg/martin_random_forest.ipynb
Martin: multiple_regression https://nbviewer.jupyter.org/urls/dl.dropbox.com/s/fm0tt059txnnriv/martin_2_multiple_regression1.ipynb
Martin: multiple_regression https://nbviewer.jupyter.org/urls/dl.dropbox.com/s/3imkmd9gtrfg7xv/martin_results.ipynb
https://www.earthdatascience.org/courses/earth-analytics-python/use-time-series-data-in-python/date-time-types-in-pandas-python/
Transfer Learning for Image Classification in Keras https://towardsdatascience.com/transfer-learning-for-image-classification-in-keras-5585d3ddf54e
https://www.kaggle.com/drscarlat/unsupervised-anomaly-detection
https://towardsdatascience.com/machine-learning-for-anomaly-detection-and-condition-monitoring-d4614e7de770
https://machinelearningmastery.com/how-to-use-statistics-to-identify-outliers-in-data/ https://www.analyticsvidhya.com/blog/2016/02/time-series-forecasting-codes-python/ https://towardsdatascience.com/time-series-in-python-exponential-smoothing-and-arima-processes-2c67f2a52788 https://www.geeksforgeeks.org/random-walk-implementation-python/ https://towardsdatascience.com/anomaly-detection-with-lstm-in-keras-8d8d7e50ab1b https://machinelearningmastery.com/white-noise-time-series-python/
How to Get Started with Deep Learning for Time Series Forecasting https://machinelearningmastery.com/gentle-introduction-random-walk-times-series-forecasting-python/
Scipy.interpolate.griddata regridding data. http://christopherbull.com.au/python/scipy-interpolate-griddata/
How to Develop a Conditional GAN (cGAN) From Scratch https://machinelearningmastery.com/how-to-develop-a-conditional-generative-adversarial-network-from-scratch/
How to Explore the GAN Latent Space When Generating Faces https://machinelearningmastery.com/how-to-interpolate-and-perform-vector-arithmetic-with-faces-using-a-generative-adversarial-network/
How to Develop a GAN for Generating Handwritten Digits https://machinelearningmastery.com/how-to-develop-a-generative-adversarial-network-for-an-mnist-handwritten-digits-from-scratch-in-keras/
A Gentle Introduction to Generative Adversarial Networks (GANs) https://machinelearningmastery.com/what-are-generative-adversarial-networks-gans/
More on GANs https://machinelearningmastery.com/?s=GAN&post_type=post&submit=Search
Algorithm assesses image and video quality https://www.vision-systems.com/boards-software/article/16736698/algorithm-assesses-image-and-video-quality
Denoise AE http://i-systems.github.io/HSE545/iAI/DL/topics/05_autoencoder/02_DAE.html https://becominghuman.ai/variational-autoencoders-for-new-fruits-with-keras-and-pytorch-6d0cfc4eeabd http://www.kecl.ntt.co.jp/people/kameoka.hirokazu/Demos/mvae-ass/index.html https://www.datasciencecentral.com/profiles/blogs/an-elegant-way-to-represent-forward-propagation-and-back https://wiseodd.github.io/techblog/2016/12/10/variational-autoencoder/
Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras https://machinelearningmastery.com/handle-missing-timesteps-sequence-prediction-problems-python/ https://machinelearningmastery.com/how-to-develop-lstm-models-for-time-series-forecasting/
Confusion Matrix https://leandeep.com/datalab-kaggle/comprehensive-data-exploration-with-python.html https://seaborn.pydata.org/generated/seaborn.heatmap.html
3 Techniques to Extract Features from Image Data using Python https://www.analyticsvidhya.com/blog/2019/08/3-techniques-extract-features-from-image-data-machine-learning-python/
Analyze a Soccer game using Tensorflow Object Detection and OpenCV https://towardsdatascience.com/analyse-a-soccer-game-using-tensorflow-object-detection-and-opencv-e321c230e8f2
How to scrape businesses’ info with Python and Beautiful Soup https://medium.com/employbl/scrape-the-web-for-amsterdam-coffeeshops-with-python-and-beautiful-soup-19ed25394234
Outlier detection 101: Median and Interquartile range. https://medium.com/@davidnh8/outlier-detection-101-median-and-interquartile-range-cc9dde94c0ac
Bankruptcy Analysis [classification] https://www.kaggle.com/ginelledsouza/bankruptcy-analysis https://www.kaggle.com/nandalald/bankruptcy-prediction-using-ann-f1-score-97