While understanding machine learning has never been easier, it helps to know how to get started on some of the latest tools and features that are available. In this hands-on workshop with Galvanize, you'll learn the very basics of machine learning via the language of Python. Our community of staff, students, and professional instructors will guide you through the elementary aspects of SciKit Learn, a popular machine learning library for the Python language. We will learn a bit more about matrices, develop a linear model on a real data set, and play around in the sandbox if we have time. You'll leave this session equipped to write your own scripts and feel more prepared to take the next steps on your path to understanding machine learning.

There are two kinds of people: Those who aren't sure what a chatbot is, and those who are really tired of hearing about them. Last year, 2016, was the year that chatbots -- software programs that converse with humans in a human language, such as English or Chinese, rather than through a graphic interface or via computer-language commands -- were widely talked up as the Future of Technology. Facebook's April 2016 announcement of support for chatbots in its Messenger system for one-on-one communications was largely resposible for what can only be described as hype around what chatbots would soon be doing for all of us. The chatbots that debuted with Facebook's launch were comically disappointing, but as a VentureBeat reporter quipped, "Facebook could announce they found a new recipe for blueberry pancakes and people would pay attention. We were off to the races."

If you've ever caught yourself thinking, "She looks like a Sue," or "He doesn't look like a Bob," a new study may back up your instincts about whether people's names suit them. In fact, people often do "look like their names," perhaps especially those named Tom or Veronique, the research suggests. In the study, researchers found that people could correctly match an unfamiliar face to that person's name at a rate higher than expected due to chance, according to a new study. In two experiments involving 185 participants in Israel and France, people were shown only color headshot photographs of 25 total strangers, and the researchers asked them to guess the stranger's name from a list of four or five name possibilities. For example, a participant who is shown a face and given four names to choose from has a 25 percent chance of guessing the right name.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

By transforming their networks and operations with the newest technologies, mobile operators could double their cash-flow conversion within five years. The past several years have been tough for telecom companies. Their revenue and cash flows1 1. Cash flow is measured here in terms of earnings before interest, taxes, depreciation, and amortization (EBITDA) minus capital expenditures. Consumption of mobile data boomed, as masses of new wireless customers used their handsets to spend ever-increasing amounts of time online. Companies responded by investing heavily in their wireless networks, even as subscriber growth slowed. As a result, the average ratio of capital spending to revenues has remained stubbornly high, at around 15 percent, for the major players (Exhibit 1).

I imagine if we were sitting in a room decades ago as farmers, we would rightfully be worried about our future employment. However, as the world evolved, new jobs were created in industries we never would have fathomed would exist. Brent Schutte is the chief investment strategist of Northwestern Mutual Wealth Management Company. Today Americans seem to be at odds with themselves when it comes to their feelings on economic growth. People are worried about a future lack of economic growth due to a less productive workforce.

Roman Mugriyev was driving his long-haul 18-wheeler down a two-lane Texas highway when he saw an oncoming car drift into his lane just a few hundred feet ahead. There was a ditch to his right and more oncoming cars to his left, so there was little for him to do but hit his horn and brake. "I could hear the man who taught me to drive telling me what he always said was rule number one: 'Don't hurt anybody,'" Mugriyev recalls. But it wasn't going to work out that way. It shattered his front axle, and he struggled to keep his truck and the wrecked car now fused to it from hitting anyone else as it barreled down the road. After Mugriyev finally came to a stop, he learned that the woman driving the car had been killed in the collision.

So, you have been thinking about picking up machine learning, but given the confusing state of the web you don't know where to begin? Or maybe you have finished the first 7 steps and are looking for some follow-up material, beyond the introductory? This post is the second installment of the 7 Steps to Mastering Machine Learning in Python series (since there are 2 parts, I guess it now qualifies as a series). If you have started with the original post, you should already be satisfactorily up to speed, skill-wise. If not, you may want to review that post first, which may take some time, depending on your current level of understanding; however, I assure you that doing so will be worth your effort.

Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate most everything in language: web search, advertisement, emails, customer service, language translation, radiology reports, etc. There are a large variety of underlying tasks and machine learning models behind NLP applications. Recently, deep learning approaches have obtained very high performance across many different NLP tasks.

Restricted Boltzmann machines~(RBMs) and conditional RBMs~(CRBMs) are popular models for a wide range of applications. In previous work, learning on such models has been dominated by contrastive divergence~(CD) and its variants. Belief propagation~(BP) algorithms are believed to be slow for structured prediction on conditional RBMs~(e.g., Mnih et al. [2011]), and not as good as CD when applied in learning~(e.g., Larochelle et al. [2012]). In this work, we present a matrix-based implementation of belief propagation algorithms on CRBMs, which is easily scalable to tens of thousands of visible and hidden units. We demonstrate that, in both maximum likelihood and max-margin learning, training conditional RBMs with BP as the inference routine can provide significantly better results than current state-of-the-art CD methods on structured prediction problems. We also include practical guidelines on training CRBMs with BP, and some insights on the interaction of learning and inference algorithms for CRBMs.