A nasty new form of ransomware is wreaking havoc on computers. Hackers that encrypt your files and demand money from you in the form of bitcoin is bad enough, but a few versions also overwrite your Windows PC's master boot record (MBR). The master boot record is a key part of your PC's startup system. It contains information about the computer's disk partitions and helps load the operating system. More recently, the Petya variant of ransomware has been causing MBR problems.

In this guide we are going to discuss how to install and use the Datumbox Machine Learning framework in your Java projects. Since almost all of the code is written in Java, using it is as simple as including it as dependency in your Java project. Nevertheless a couple of classes (DataEnvelopmentAnalysis and LPSolver) use an external C library called lpsolve (Linear Programming Solver). Note that if you don't plan to use those 2 classes you are not required to install any binary libraries on your system. Nevertheless if you want to explore all the supported algorithms it is recommended to do the full installation as described below.

Gradient boosting involves the creation and addition of decision trees sequentially, each attempting to correct the mistakes of the learners that came before it. This raises the question as to how many trees (weak learners or estimators) to configure in your gradient boosting model and how big each tree should be. In this post you will discover how to design a systematic experiment to select the number and size of decision trees to use on your problem. How to Tune the Number and Size of Decision Trees with XGBoost in Python Photo by USFWSmidwest, some rights reserved. XGBoost is the high performance implementation of gradient boosting that you can now access directly in Python.

I guess it could be an older version, but here's a single PDF: https://www.dgp.toronto.edu/

Attracting the best talent is the ultimate motive for recruiting professionals. In discussion with Mr. Adhiraj, Vice President (HR), ITC, we at CareerBuilder were acquainted with some innovative thoughts on how technology and recruiting would take place in 2015. "As technology continues to develop, can we assume that businesses will still be using recruiters 10 years from now? There are grounds for doubt, but it seems on the contrary that recruiters will have a highly strategic role to play in attracting and motivating the best talents for the projects, management and operations of the organizations they serve. One thing is certain, however: the digital transformation that began in 1995 with the advent of the Internet compels us to rethink everything.

I guess it could be an older version, but here's a single PDF: https://www.dgp.toronto.edu/

We describe GTApprox -- a new tool for medium-scale surrogate modeling in industrial design. Compared to existing software, GTApprox brings several innovations: a few novel approximation algorithms, several advanced methods of automated model selection, novel options in the form of hints. We demonstrate the efficiency of GTApprox on a large collection of test problems. In addition, we describe several applications of GTApprox to real engineering problems. Keywords: 1. Introduction approximation, surrogate model, surrogate-based optimization Approximation problems (also known as regression problems) arise quite often in industrial design, and solutions of such problems are conventionally referred to as surrogate models [1]. The most common application of surrogate modeling in engineering is in connection to engineering optimization [2]. Indeed, on the one hand, design optimization plays a central role in the industrial design process; on the other hand, a single optimization step typically requires the optimizer to create or refresh a model of the response function whose optimum is sought, to be able to come up with a reasonable next design candidate. The surrogate models used in optimization range from simple local linear regression employed in the basic gradient-based optimization [3] to complex global models employed in the so-called Surrogate-Based Optimization (SBO) [4]. Aside from optimization, surrogate modeling is used in dimension reduction [5, 6], sensitivity analysis [7-10], and for visualization of response functions. Preprint submitted to February 23, 2018 Mathematically, the approximation problem can generally be described as follows. A great variety of surrogate modeling methods exist, with different assumptions on the underlying response functions, data sets, and model structure [11].

The XGBoost library for gradient boosting uses is designed for efficient multi-core parallel processing. This allows it to efficiently use all of the CPU cores in your system when training. In this post you will discover the parallel processing capabilities of the XGBoost in Python. How to Best Tune Multithreading Support for XGBoost in Python Photo by Nicholas A. Tonelli, some rights reserved. XGBoost is the high performance implementation of gradient boosting that you can now access directly in Python.

For the sake of reproducibility, I'm giving you access to personalized Docker image for provisioning the environment. You should be able to run it on your operating system. If you don't want to (or can't) you will have to install all the required libraries manually. You should also have Git installed to download necessary course materials. The course starts now and never ends!

Say what you will, but IBM Watson is one resourceful supercomputer. We've previously seen the AI describe the contents of photos, predict the most popular toys during Christmas season and gauge your emotional state – all of that with an exceptional accuracy. Now IBM Watson has added yet another skill to its arsenal as it just learned how to make movie trailers. Earlier this week, 20th Century Fox trusted the supercomputer with the task to create the trailer for its upcoming sci-fi drama Morgan. Our new event for New York is focused on quality, not quantity.