【Richard M. Reese，Jennifer Reese，Alexey Grigorev】Java: Data Science Mad

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1. Java: Data Science Made Easy
   
2. Authors: Richard M. Reese – Jennifer L. Reese – Alexey Grigorev
   
3. 
   
4. ISBN-10 书号: 1788475658
   
5. ISBN-13 书号: 9781788475655
   
6. Release 出版日期: 2017-07-07
   
7. pages 页数: (734)
   
8. 
   
9. Book Description
   
10. Data collection, processing, analysis, and more
    
11. 
    
12. About This Book
    
13. Your entry ticket to the world of data science with the stability and power of Java
    
14. Explore, analyse, and visualize your data effectively using easy-to-follow examples
    
15. A highly practical course covering a broad set of topics – from the basics of Machine Learning to Deep Learning and Big Data frameworks.
    
16. Who This Book Is For
    
17. This course is meant for Java developers who are comfortable developing applications in Java, and now want to enter the world of data science or wish to build intelligent applications. Aspiring data scientists with some understanding of the Java programming language will also find this book to be very helpful. If you are willing to build efficient data science applications and bring them in the enterprise environment without changing your existing Java stack, this book is for you!
    
18. 
    
19. What You Will Learn
    
20. Understand the key concepts of data science
    
21. Explore the data science ecosystem available in Java
    
22. Work with the Java APIs and techniques used to perform efficient data analysis
    
23. Find out how to approach different machine learning problems with Java
    
24. Process unstructured information such as natural language text or images, and create your own searc
    
25. Learn how to build deep neural networks with DeepLearning4j
    
26. Build data science applications that scale and process large amounts of data
    
27. Deploy data science models to production and evaluate their performance
    
28. In Detail
    
29. Data science is concerned with extracting knowledge and insights from a wide variety of data sources to analyse patterns or predict future behaviour. It draws from a wide array of disciplines including statistics, computer science, mathematics, machine learning, and data mining. In this course, we cover the basic as well as advanced data science concepts and how they are implemented using the popular Java tools and libraries.The course starts with an introduction of data science, followed by the basic data science tasks of data collection, data cleaning, data analysis, and data visualization. This is followed by a discussion of statistical techniques and more advanced topics including machine learning, neural networks, and deep learning. You will examine the major categories of data analysis including text, visual, and audio data, followed by a discussion of resources that support parallel implementation. Throughout this course, the chapters will illustrate a challenging data science problem, and then go on to present a comprehensive, Java-based solution to tackle that problem. You will cover a wide range of topics – from classification and regression, to dimensionality reduction and clustering, deep learning and working with Big Data. Finally, you will see the different ways to deploy the model and evaluate it in production settings.
    
30. 
    
31. By the end of this course, you will be up and running with various facets of data science using Java, in no time at all.
    
32. 
    
33. This course contains premium content from two of our recently published popular titles:
    
34. 
    
35. Java for Data Science
    
36. Mastering Java for Data Science
    
37. Style and approach
    
38. This course follows a tutorial approach, providing examples of each of the concepts covered. With a step-by-step instructional style, this book covers various facets of data science and will get you up and running quickly.

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关键词：Data Science Science Data Java Made

1. Contents
   
2. Module 1
   
3. Getting Started with Data Science
   
4. Data Acquisition
   
5. Data Cleaning
   
6. Data Visualization
   
7. Statistical Data Analysis Techniques
   
8. Machine Learning
   
9. Neural Networks
   
10. Deep Learning
    
11. Text Analysis
    
12. Visual and Audio Analysis
    
13. Mathematical and Parallel Techniques for Data Analysis
    
14. Bringing It All Together
    
15. 
    
16. Module 2
    
17. Data Science Using Java
    
18. Data Processing Toolbox
    
19. Exploratory Data Analysis
    
20. Supervised Learning – Classification and Regression
    
21. Unsupervised Learning – Clustering and Dimensionality Reduction
    
22. Working with Text – Natural
    
23. 
    
24. Language 语言 Processing and Information Retrieval
    
25. Extreme Gradient Boosting
    
26. Deep Learning with DeepLearning4J
    
27. Scaling Data Science
    
28. Deploying Data Science Models
    
29. Bibliography

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1. Identifying location entities
   
2. We can also find other types of entities such as dates and locations. In the following example,
   
3. we find locations in a sentence. It is very similar to the previous person example, except that an
   
4. en-ner-location.bin file is used for the model:
   
5. try (InputStream tokenStream =
   
6. new FileInputStream("en-token.bin");
   
7. InputStream locationModelStream = new FileInputStream(
   
8. new File("en-ner-location.bin"));) {
   
9. TokenizerModel tm = new TokenizerModel(tokenStream);
   
10. TokenizerME tokenizer = new TokenizerME(tm);
    
11. TokenNameFinderModel tnfm =
    
12. new TokenNameFinderModel(locationModelStream);
    
13. NameFinderME nf = new NameFinderME(tnfm);
    
14. sentence = "Enid is located north of Oklahoma City.";
    
15. String tokens[] = tokenizer.tokenize(sentence);
    
16. Span spans[] = nf.find(tokens);
    
17. for (int i = 0; i < spans.length; i++) {
    
18. out.println(spans[i] + " - " +
    
19. tokens[spans[i].getStart()]);
    
20. }
    
21. } catch (Exception ex) {
    
22. // Handle exceptions
    
23. }

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1. First, we set up the basic framework for the conversion by creating a try-catch block to handle
   
2. exceptions. First, create an instance of the Configuration class. It is used to configure the
   
3. recognizer to recognize standard English. The configuration models and dictionary need to be
   
4. changed to handle other languages:
   
5. try {
   
6. Configuration configuration = new Configuration();
   
7. String prefix = "resource:/edu/cmu/sphinx/models/en-us/";
   
8. configuration
   
9. .setAcousticModelPath(prefix + "en-us");
   
10. configuration
    
11. .setDictionaryPath(prefix + "cmudict-en-us.dict");
    
12. configuration
    
13. .setLanguageModelPath(prefix + "en-us.lm.bin");
    
14. ...
    
15. } catch (IOException ex) {
    
16. // Handle exceptions
    
17. }

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1. The StreamSpeechRecognizer class is then created using configuration. This class processes the
   
2. speech based on an input stream. In the following code, we create an instance of the
   
3. StreamSpeechRecognizer class and an InputStream from the speech file:
   
4. StreamSpeechRecognizer recognizer = new StreamSpeechRecognizer(
   
5. configuration);
   
6. InputStream stream = new FileInputStream(new File("filename"));

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1. To start speech processing, the startRecognition method is invoked. The getResult method returns
   
2. a SpeechResult instance that holds the result of the processing. We then use the SpeechResult
   
3. method to get the best results. We stop the processing using the stopRecognition method:
   
4. recognizer.startRecognition(stream);
   
5. SpeechResult result;
   
6. while ((result = recognizer.getResult()) != null) {
   
7. out.println("Hypothesis: " + result.getHypothesis());
   
8. }
   
9. recognizer.stopRecognition();

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1. The ITesseract interface contains numerous OCR methods. The doOCR method takes a file and
   
2. returns a string containing the words found in the file, as shown here:
   
3. ITesseract instance = new Tesseract();
   
4. try {
   
5. String result = instance.doOCR(new File("OCRExample.png"));
   
6. out.println(result);
   
7. } catch (TesseractException e) {
   
8. // Handle exceptions
   
9. }

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1. System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
   
2. 
   
3. String base = "PathToResources";
   
4. 
   
5. The following statement initializes the class to detect faces:
   
6. CascadeClassifier faceDetector =
   
7. new CascadeClassifier(base +
   
8. "/lbpcascade_frontalface.xml");
   
9. 
   
10. The image to be processed is loaded, as shown here:
    
11. Mat image = Imgcodecs.imread(base + "/images.jpg");
    
12. 
    
13. When faces are detected, the location within the image is stored in a MatOfRect instance. This
    
14. class is intended to hold vectors and matrixes for any faces found:
    
15. MatOfRect faceVectors = new MatOfRect();
    
16. At this point, we are ready to detect faces. The detectMultiScale method performs this task. The
    
17. image and the MatOfRect instance to hold the locations of any images are passed to the method:
    
18. faceDetector.detectMultiScale(image, faceVectors);
    
19. The next statement shows how many faces were detected:
    
20. out.println(faceVectors.toArray().length + " faces found");
    
21. We need to use this information to augment the image. This process will draw boxes around
    
22. each face found, as shown next. To do this, the Imgproc class' rectangle method is used. The
    
23. method is called once for each face detected. It is passed the image to be modified and the points
    
24. represented the boundaries of the face:
    
25. for (Rect rect : faceVectors.toArray()) {
    
26. Imgproc.rectangle(image, new Point(rect.x, rect.y),
    
27. new Point(rect.x + rect.width, rect.y + rect.height),
    
28. new Scalar(0, 255, 0));
    
29. }
    
30. The last step writes this image to a file using the Imgcodecs class' imwrite method:
    
31. Imgcodecs.imwrite("faceDetection.png", image);

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