Building Machine Learning Systems with Python - Richert, Coelho

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Chapter 12 Therefore, we will be calling the ssh command and passing it the key files that we downloaded earlier as the identity (using the -i option). We are logging in as user ec2-user at the machine with the address ec2-54-244-194-143.us-west-2. conompute.amazonaws.com. This address will, of course, be different in your case. If you choose another distribution for your instance, the username may also change. In any case, the web panel will give you the correct information. Finally, if you are running an Unix-style operating system (including Mac OS), you may have to tweak its permissions by calling the following code: chmod 600 awskeys.pem This sets the read/write permission for the current user only. SSH will otherwise give you an ugly warning. Now, you should be able to log in to your machine. If everything is OK, you should see the banner as shown in the following screenshot: This is a regular Linux box where you have the sudo permission; you can run any command as the super user by prefixing it with sudo. You can run the update command it recommends to get your machine up to speed. Installing Python packages on Amazon Linux If you preferred another distribution, you can use your knowledge of that distribution to install Python, NumPy, and others. Here, we will do it on the standard Amazon distribution. We start by installing several basic Python packages as follows: sudo yum -y install python-devel python-dev python-pip numpy scipy python-matplotlib For compiling mahotas, we will also need a C++ compiler: sudo yum -y install gcc-c++ [ 253 ]
Big(ger) Data In this system, pip is installed as python-pip. For convenience, we will use pip to upgrade itself. We will then use pip to install the necessary packages: sudo pip-python install -U pip sudo pip install scikit-learn jug mahotas At this point, you can install any other package you like using pip. Running jug on our cloud machine We can now download the data and code from Chapter 10, Computer Vision–Pattern Recognition Finding Related Posts, as follows: wget FIXME-LET'S-BUILD-A-TAR-GZ-PACKAGE tar xzf chapter10.tar.gz cd chapter10 Finally, we can do the following: jug execute This would work just fine, but we would have to wait a long time for the results. Our free tier machine (of type t1.micro) is not very fast and it only has a single processor. So we will upgrade our machine. We go back to the EC2 console and right-click on the running instance to get the pop-up menu. We need to first stop the instance. Stopping an instance in a virtual machine is equivalent to powering it off. You can stop your machines at any time. At that point, you stop paying for them (you are still using disk space, which also has a cost that is billed separately). Once the machine is stopped, the change instance type option becomes available. Now we can select a more powerful instance, for example, a c1.xlarge instance, which has eight cores. The machine is still off, so you need to start it again (the virtual equivalent to booting up). AWS offers several instance types at different price points. Because this information is constantly being revised as more powerful options are introduced and prices are changed (generally getting cheaper), we cannot give you many details in this book; however, you can find the most up-to-date information on Amazon's website. We need to wait for the instance to appear once again. Once it has, right-click on Connect as described earlier to get the connection information as it will almost surely have changed. When you change instance types, your instance will get a new address assigned to it. [ 254 ]
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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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Chapter 7 So, we can see that the d
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Chapter 7 Fortunately, scikit-learn
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[ 161 ] Chapter 7 The loading of th
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Chapter 7 Summary In this chapter,
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Regression - Recommendations Improv
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Weights Regression - Recommendation
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Classification III - Music Genre Cl
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Chapter 9 Matplotlib provides the c
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[ 185 ] Chapter 9
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Chapter 9 def create_fft(fn): sampl
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Chapter 9 ax.set_yticks(range(len(g
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Chapter 9 On the left-hand side gra
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[ 193 ] Chapter 9 Improving classif
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We get the following promising resu
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Chapter 9 Summary In this chapter,
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Computer Vision - Pattern Recogniti
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Page 280 and 281: Index A AcceptedAnswerId attribute
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