Building Machine Learning Systems with Python - Richert, Coelho

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Filtering for different effects The use of image processing to achieve pleasing effects in images dates back to the beginning of digital images, but it has recently been the basis of a number of interesting applications, the most well-known of which is probably Instagram. Chapter 10 We are going to use a traditional image in image processing, the screenshot of the Lenna image, which is shown and can be downloaded from the book's website (or many other image-processing websites): im = mh.imread('lenna.jpg', as_grey=True) Adding salt and pepper noise We can perform many further manipulations on this result if we want to. For example, we will now add a bit of salt and pepper noise to the image to simulate a few scanning artifacts. We generate random arrays of the same width and height as the original image. Only 1 percent of these values will be true. salt = np.random.random(lenna.shape) > .975 pepper = np.random.random(lenna.shape) > .975 [ 207 ]
Computer Vision – Pattern Recognition We now add the salt (which means some values will be almost white) and pepper noise (which means some values will be almost black): lenna = mh.stretch(lenna) lenna = np.maximum(salt*170, sep) lenna = np.minimum(pepper*30 + lenna*(~pepper), lenna) We used the values 170 and 30 as white and black. This is slightly smoother than the more extreme choices of 255 and 0. However, all of these are choices that need to be made by subjective preferences and style. Putting the center in focus The final example shows how to mix NumPy operators <strong>with</strong> a tiny bit of filtering to get an interesting result. We start <strong>with</strong> the Lenna image and split it into the color channels: im = mh.imread('lenna.jpg') r,g,b = im.transpose(2,0,1) [ 208 ]
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Building Machine Learning Systems w
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Credits Authors Willi Richert Luis
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Luis Pedro Coelho is a Computationa
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Maurice HT Ling completed his PhD.
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Table of Contents Preface 1 Chapter
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Table of Contents Tuning the instan
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Table of Contents Improving classif
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Preface You could argue that it is
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Preface What you need for this book
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Downloading the example code You ca
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Getting Started with Python Machine
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Learning How to Classify with Real-
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Chapter 2 We are using Matplotlib;
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Chapter 2 The last few lines select
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Chapter 2 error = 0.0 for ei in ran
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Chapter 2 We can play around with t
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Chapter 2 Features and feature engi
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Chapter 2 In the preceding screensh
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Chapter 2 Binary and multiclass cla
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Clustering - Finding Related Posts
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Chapter 3 How to do it More robust
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Chapter 3 This means that the first
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Chapter 3 ... post = posts[i] ... i
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Chapter 3 If you have a clear pictu
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Chapter 3 Extending the vectorizer
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Chapter 3 0.0 >>> print(tfidf("b",
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Chapter 3 Flat clustering divides t
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Because the cluster centers are mov
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Chapter 3 'D:\\data\\379\\raw\\comp
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As we have learned previously, we w
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Chapter 3 Position Similarity Excer
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Chapter 3 But before you go there,
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Topic Modeling For those who are in
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Topic Modeling Sparsity means that
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Topic Modeling Although daunting at
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Topic Modeling … for tj,v in t:
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Topic Modeling Finally, we build th
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Topic Modeling Alternatively, we ca
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Topic Modeling Topic modeling was f
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Classification - Detecting Poor Ans
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Classification II - Sentiment Analy
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Chapter 6 Getting to know the Bayes
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Using Naive Bayes to classify Given
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Chapter 6 This denotation "" leads
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Chapter 6 Similarly, we do this for
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Chapter 6 A quick look at the previ
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Chapter 6 To keep our experimentati
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Chapter 6 Y = np.zeros(Y.shape[0])
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Chapter 6 ° ° Experiment with whe
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Chapter 6 We have to be patient whe
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Chapter 6 First, we define a range
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Chapter 6 Determining the word type
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Chapter 6 Successfully cheating usi
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Chapter 6 Our first estimator Now w
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Chapter 6 for d in documents: allca
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Regression - Recommendations You ha
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Chapter 7 The preceding graph shows
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Chapter 7 Root mean squared error a
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Penalized regression The important
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Chapter 7 P greater than N scenario
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Page 261 and 262: Big(ger) Data Looking under the hoo
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Big(ger) Data We can use the same j
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Where to Learn More about Machine L
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• Machined Learnings at http://ww
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Index A AcceptedAnswerId attribute
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F false negative 41 false positive
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inary matrix of recommendations, us
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sklearn.naive_bayes package 127 skl
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NumPy Beginner's Guide - Second Edi
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