June 2019
Intermediate to advanced
308 pages
7h 21m
English
Content preview from Hands-On Deep Learning for IoT
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Data preprocessing
Data preprocessing is an essential step for a DL pipeline. The CPU utilization dataset is ready to be used in the training, but the KDD cup 1999 IDS dataset needs multilevel preprocessing that includes the following three steps:
- Splitting the data into three different protocol sets (application, transport, and network)
- Duplicate data removal, categorical data conversion, and normalization
- Feature selection (optional)
Using the following lines of code is a potential way of splitting the dataset into three datasets, namely Final_App_Layer, Final_Transport_Layer, and Final_Network_Layer:
#Importing all the required Librariesimport pandas as pdIDSdata = pd.read_csv("kddcup.data_10_percent.csv",header = None,engine = 'python',sep=",") ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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