the feature map visualizations depicted in figure - are definitely interesting you can clearly see that each feature matrix produced by the convolutional neural network is trying to learn something about the image like its texturecornersedgesilluminationhuebrightnessand so on this should give you an idea of how these activation feature maps can then be used as features for images in fact you can stack the output of cnnflatten it if neededand pass it as an input layer to multi-layer fully connected perceptron neural network and use it to solve the problem of image classification this should give you head start on automated feature extraction with the power of deep learningdon' worry if you did not understand some of the terms mentioned in this sectionwe will cover deep learning and cnns in more depth in subsequent if can' wait to get started with deep learningyou can fire up the bonus notebook provided with this called bonus classifying handwritten digits using deep cnns ipynbfor complete real-world example of applying cnns and deep learning to classify hand-written digitsfeature scaling when dealing with numeric featureswe have specific attributes which may be completely unbounded in naturelike view counts of video or web page hits using the raw values as input features might make models biased toward features having really high magnitude values these models are typically sensitive to the magnitude or scale of features like linear or logistic regression other models like tree based methods can still work without feature scaling however it is still recommended to normalize and scale down the features with feature scalingespecially if you want to try out multiple machine learning algorithms on input features we have already seen some examples of scaling and transforming features using log and box-cox transforms earlier in this in this sectionwe look at some popular feature scaling techniques you can load feature_scaling py directly and start running the examples or use the jupyter notebookfeature scaling ipynb for more interactive experience let' start by loading the following dependencies and configurations in [ ]from sklearn preprocessing import standardscalerminmaxscalerrobustscaler import numpy as np import pandas as pd np set_printoptions(suppress=truelet' now load some sample data of user views pertaining to online videos the following snippet creates this sample dataset in [ ]views pd dataframe([ ]columns=['views']views out[ ]views from the preceding dataframe we can see that we have five videos that have been viewed by users and the total view count for each video is depicted by the feature views it is quite evident that some videos have been viewed lot more than the othersgiving rise to values of high scale and magnitude let' look at how we can scale this feature using several handy techniques