download скачать Free download скачать : Deep Learning for Anomaly Detection with Python
mp4 | Video: h264,1280X720 | Audio: AAC, 44.1 KHz
Genre:eLearning | Language: English | Size:270.97 MB

Files Included :

1  Introduction.mp4 (11.92 MB)
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2  About this Project.mp4 (2.95 MB)
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3  Applications.mp4 (24.49 MB)
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4  Job opportunities.mp4 (29.85 MB)
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5  Python, Keras, and Google Colab.mp4 (5.38 MB)
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1  Setup Working Directory.mp4 (3.68 MB)
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10  Load second dataset for testing.mp4 (7.87 MB)
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11  Display first few rows of data.mp4 (5 MB)
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12  Visualize the small noise dataset.mp4 (5.17 MB)
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13  Visualize the daily jumps dataset.mp4 (1.18 MB)
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14  Mean and standard deviation of the small noise dataset.mp4 (4.63 MB)
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15  Normalize the training data from the small noise dataset.mp4 (2.79 MB)
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16  Print the number of training samples.mp4 (2 MB)
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17  Create sequences of data.mp4 (8.53 MB)
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18  Create sequences for the training data.mp4 (4.77 MB)
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19  Build an autoencoder model.mp4 (22.05 MB)
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2  Numenta Anomaly Benchmark (NAB) dataset.mp4 (6.29 MB)
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20  Trains the autoencoder model.mp4 (10.31 MB)
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21  Saving the trained autoencoder model.mp4 (3.72 MB)
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22  Loading a trained autoencoder model.mp4 (3.21 MB)
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23  Plot training and validation loss curves.mp4 (5.62 MB)
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24  Predictions on the training data.mp4 (3.4 MB)
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25  Calculate Mean Absolute Error (MAE) loss.mp4 (4.25 MB)
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26  Histogram of Mean Absolute Error (MAE) loss.mp4 (4.97 MB)
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27  Setting threshold based on MAE loss.mp4 (4.59 MB)
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28  Plotting original and predicted sequences for first training sample.mp4 (3.66 MB)
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29  Normalizing the test data.mp4 (3.86 MB)
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3  What is code ipynb.mp4 (1.94 MB)
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30  Visualizing normalized test data.mp4 (4.72 MB)
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31  Creating sequences from normalized test data.mp4 (5.2 MB)
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32  Predictions for test data.mp4 (4.09 MB)
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33  Calculating MAE loss for test samples.mp4 (5.17 MB)
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34  Histogram of MAE loss values calculated for test samples.mp4 (5.45 MB)
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35  Detecting anomalies in test data.mp4 (10.34 MB)
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36  Finding indices of anomalous data points.mp4 (7.5 MB)
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37  Plotting detected anomalies.mp4 (6.83 MB)
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4  Launch Code.mp4 (2.08 MB)
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5  Activate GPU.mp4 (2.57 MB)
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6  Mount Google Drive in a Google Colab notebook.mp4 (4.04 MB)
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7  Import Python libraries.mp4 (5.05 MB)
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8  Path to data files.mp4 (5.6 MB)
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9  Read data from CSV file.mp4 (4.23 MB)
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 https://rapidgator.net/file/301764e851d9e2d6630283fcc1498cf3/Deep_Learning_for_Anomaly_Detection_with_Python.zip

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