Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Mastering Scala Machine Learning
  • Table Of Contents Toc
Mastering Scala Machine Learning

Mastering Scala Machine Learning

By : Kozlov
close
close
Mastering Scala Machine Learning

Mastering Scala Machine Learning

By: Kozlov

Overview of this book

Since the advent of object-oriented programming, new technologies related to Big Data are constantly popping up on the market. One such technology is Scala, which is considered to be a successor to Java in the area of Big Data by many, like Java was to C/C++ in the area of distributed programing. This book aims to take your knowledge to next level and help you impart that knowledge to build advanced applications such as social media mining, intelligent news portals, and more. After a quick refresher on functional programming concepts using REPL, you will see some practical examples of setting up the development environment and tinkering with data. We will then explore working with Spark and MLlib using k-means and decision trees. Most of the data that we produce today is unstructured and raw, and you will learn to tackle this type of data with advanced topics such as regression, classification, integration, and working with graph algorithms. Finally, you will discover at how to use Scala to perform complex concept analysis, to monitor model performance, and to build a model repository. By the end of this book, you will have gained expertise in performing Scala machine learning and will be able to build complex machine learning projects using Scala.
Table of Contents (12 chapters)
close
close
10
10. Advanced Model Monitoring
11
Index

ML libraries

Spark, particularly with memory-based storage systems, claims to substantially improve the speed of data access within and between nodes. ML seems to be a natural fit, as many algorithms require multiple passes over the data, or repartitioning. MLlib is the open source library of choice, although private companies are catching, up with their own proprietary implementations.

As I will chow in Chapter 5, Regression and Classification, most of the standard machine learning algorithms can be expressed as an optimization problem. For example, classical linear regression minimizes the sum of squares of y distance between the regression line and the actual value of y:

ML libraries

Here, ML libraries are the predicted values according to the linear expression:

ML libraries

A is commonly called the slope, and B the intercept. In a more generalized formulation, a linear optimization problem is to minimize an additive function:

ML libraries

Here, ML libraries is a loss function and ML libraries is a regularization function. The regularization function is an increasing...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Mastering Scala Machine Learning
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist download Download options font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon