When you are performing with Caffe, you need to determine your deep neural network architecture in a '.prototxt' file. These prototxt information ordinarily consist of hundreds of strains, defining layers and corresponding parameters. Before you commence schooling your neural network, you need to produce these information and determine your architecture. But from time to time, it's useful to dynamically produce this architecture depending on our wants. In this sort of circumstances, producing a deep neural network programmatically can be really valuable.

One key buzzword these days is "algorithm," which technically means any computational formula but which has come to mean a formula that predicts our behavior. Amazon and Netflix have algorithms that predict what books a user is likely to want to read or what movies and TV shows he or she is likely to want to watch. Facebook has an algorithm that predicts the news a user is likely to want. Dating sites like Match.com and OkCupid use algorithms to predict with whom we would fall in love. Google, with the most famous algorithm of all, predicts what we want when we type a search term.

It's hard to find someone who isn't a fan of "Game of Thrones." The TV show, which returns Sunday, has reached peaks of popularity that few shows do, and draws in fans of all shapes and sizes -- even computers. Maluuba, a Canadian startup, posted a YouTube video on Friday showing its artificial-intelligence software reading the synopsis for the fifth season of "Game of Thrones'" and immediately knowing all of the show's plot lines. It's the equivalent to a human, let's call him "John" for this example, who knows nothing about the show, has never seen it, takes one look at a Wikipedia page and instantaneously knows everything that's happening. "Who stabbed Jon Snow?" the Maluuba engineer asks the AI software.

This is the first in a series of reports to document development of a generalized method to create artificial neural networks (ANNs) via a genetic algorithm (GA). This report will be divided into several main sections. The goal of this project is to develop a library of ANNs that can be used to resolve a variety of problems. The initial development goal is to demonstrate its viability by accurately distinguishing between women and men when provided key facial data. Due to the generalized nature of the ANN creation process it should be straight forward to train a number of specialized ANNs.

A data scientist is one of the most in-demand, high-profile careers in IT today, but Tom Walsh and Alex Krowitz have been working behind the scenes in the field for years. Walsh, a research engineer and Krowitz, a senior research engineer at cloud workforce management solutions company Kronos, sift through the influx of proprietary and customer data to identify patterns and gain insights based on that data. There are generally two kinds of projects we regularly handle; mining patterns within data to improve our own products is one and the other is taking on specific sets of customer data to gather and deliver insights from that," says Walsh. What companies are looking for is ultimately the capability to make predictions based on that data, says Krowitz. Companies use those predictions to help drive everything from marketing strategy to resource allocation, personnel levels and staffing, or to predict retail sales, he says. "We have products that use machine learning algorithms to help customers with these predictions.

Big data can't deliver on its potential unless enterprises have the right tools to extract insights. Woodside, an Australia-based oil and gas giant, realizes this and is using advanced machine learning technology to leverage its data via predictive analysis. Front and center in the company's toolkit is IBM Watson, a cutting-edge machine learning and natural language processing platform that analyzes vast amounts of unstructured data. According to CIO, Woodside is using a variety of big data tools -- including Amazon Web Services (AWS), Apache Spark and Watson -- to improve operational efficiency and predict potential catastrophes at its production facilities. Elsa Jordan, principal data scientist at Woodside, told attendees of the Chief Analytics Officer Forum in Sydney how the company has implemented these data science technologies in recent years and how the Watson engine has become a key component of the organization's big data platform.

Even though Gilles has recently graduated with a degree in Fundamental Mathematics, he knows that there's more to be done than mathematics. With a solid knowledge in classical statistics, he now pursues a PhD in parallelizing regression modeling techniques. Vincent has just finished his Master's degree in Artificial Intelligence, and has more than 3 years of experience with machine learning problems of different kinds. He experienced first-hand the difficulties that come with building and assessing machine learning systems. This made him passionate about teaching people how to do machine learning the right way.

There are many deep learning resources freely available online, but it can be confusing knowing where to begin. Go from vague understanding of deep neural networks to knowledgeable practitioner in 7 steps!

We at Sift Science provide fraud detection for hundreds of customers spanning many industries and use cases. To do this, we have devised a specialized modeling stack that is able to adapt to individual customers while simultaneously delivering a great out-of-box experience for new customers, achieved by mixing the output from a "global" model – trained on our entire network of data – with the output from a customer's individualized model. Prior to decision forests, we used a custom-built logistic regression classifier combined with highly specialized feature engineering for our global model. While logistic regression has many great attributes, it is fundamentally limited by its inability to model non-linear interactions between features. At Sift, we tend to think of our modeling stack primarily as an enabler of our feature engineering; more powerful modeling allows us to extract the most insight from our features and can even lead to new classes of features.

A former science teacher who believed in the power and possibility of online learning over two decades ago, he taught himself how to build courses in HTML on class intranets. Kevin taught one of the first hybrid, educational technology courses for teachers, for the University of Washington. And, after building countless web pages and classes on the early world wide web, he now helps develop e-learning programs, consults on virtual training'best practices' and has many interests in other internet and educational technology-related areas. Kevin finds he's now enjoying learning more from his children who are all deep into their own technology-related careers and entrepreneurial endeavors. With two new grandchildren, he's investigating more seriously the advancing new technologies in an effort to understand the knowledge and skills necessary to achieve happiness and success in a technological future.