├── AutoEncoders-for-Anomaly-Detection.ipynb
├── AutoEncoders-for-Anomaly-Detection_Matplotlib.ipynb
├── AutoEncoders.png
├── JADS workshop.zip
├── JADS_CarrerDay_Data.cvs
├── README.md
└── outlier_pic.jpg


/AutoEncoders.png:
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https://raw.githubusercontent.com/abelusha/AutoEncoders-for-Anomaly-Detection/6a2b0b0baf84e3b77d42ffb0c5244fbc8fc20dd6/AutoEncoders.png


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/JADS workshop.zip:
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https://raw.githubusercontent.com/abelusha/AutoEncoders-for-Anomaly-Detection/6a2b0b0baf84e3b77d42ffb0c5244fbc8fc20dd6/JADS workshop.zip


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/README.md:
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 1 | # AutoEncoders-for-Anomaly-Detection
 2 | 
 3 | This is a jupyter Notebook that where I use a Neural Network model, namely Autoencioders for detecting anomallies in my data.
 4 | 
 5 | **Libraries & Respective Versions:**
 6 | 
 7 | **Numpy version      : 1.14.2**
 8 | 
 9 | **Pandas version     : 0.22.0**
10 | 
11 | **Matplotlib version : 2.0.2**
12 | 
13 | **Seaborn version    : 0.7.1**
14 | 
15 | **Plotly version     : 2.7.0**
16 | 
17 | **Tensorflow version : 1.8.0**
18 | 
19 | **Keras version      : 2.2.0**
20 | 


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/outlier_pic.jpg:
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https://raw.githubusercontent.com/abelusha/AutoEncoders-for-Anomaly-Detection/6a2b0b0baf84e3b77d42ffb0c5244fbc8fc20dd6/outlier_pic.jpg


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