Large enterprises, startups and high-performance businesses across industries are increasingly turning to Artificial Intelligence and advanced analytics to make faster, more effective, data-driven decisions. The volume of unstructured and structured data stored by enterprises is growing at an accelerating rate. The demand for skilled data scientists and candidates with AI skills is at an all-time high. Yet developing those skills typically requires significant investments of time, energy and money. Businesses are struggling to successfully deploy and manage AI projects due to lack of resources.

OnePlus has announced the latest in its series of new phones: the 6T. The phone includes features such as fingerprint recognition that is built into the display, and technology that prepares for faster 5G networks. It also includes updates to the screen, processor and camera. Most of the changes built on the design and features introduced with the OnePlus 6, which was released earlier this year. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

In the fall of 2016, I was a Teaching Fellow (Harvard's version of TA) for the graduate class on "Advanced Topics in Data Science (CS209/109)" at Harvard University. I was in-charge of designing the class project given to the students, and this tutorial has been built on top of the project I designed for the class. As a researcher on Computer Vision, I come across new blogs and tutorials on ML (Machine Learning) every day. However, most of them are just focussing on introducing the syntax and the terminology relevant to the field. While people are able to copy paste and run the code in these tutorials and feel that working in ML is really not that hard, it doesn't help them at all in using ML for their own purposes.

Looking for Artificial Intelligence Tutorial to learn introduction to artificial intelligence? Grab the list of Best Artificial Intelligence Courses Online, Tutorials, and Training are offered by a number of massive open online course (MOOC) providers like Udemy, Coursera, and edX. Artificial Intelligence (AI) and machine intelligence are the most booming topics in every industry now. Some of this popular MOOC providers offer some in-depth artificial intelligence programs. The list of the Best Artificial Intelligence Certification is often taught by industry top AI researchers or experts and you will learn the best applications of artificial intelligence.

It's no longer unusual to have Artificial Intelligence technology respond for you: remember that time when you received an email from your boss and even before you could key in a reply, a series of predictive texts popped up on screen? There's "noted," "sure," "i'll be there," and the most common, "thank you for your mail." Well, not too far behind is the day when full-length novels written by computers will also be the norm, say AI experts. Only two weeks ago, researchers at Facebook developed an AI that wrote a story on alien abduction. Closer home, a 300-page English book written by Calicut-based author Srinivas Mahankali, comprising one lakh words, was translated into Mandarin in flat 30 seconds by an AI bot, and that, too, "with 95 per cent accuracy".

VergeML is a command line based environment for exploring, training and running state-of-the-art Machine Learning models. It provides ready-to-use models, handles data preprocessing and augmentation, tracks your AI's training sessions and provides other goodies such as an automatic REST interface. Here's how it looks in action: VergeML runs on Windows, Linux and MacOS. You need to have Python 3.6 and TensorFlow installed. Congratulations, you have successfully installed VergeML!

Deep learning neural networks are capable of automatically learning and extracting features from raw data. This feature of neural networks can be used for time series forecasting problems, where models can be developed directly on the raw observations without the direct need to scale the data using normalization and standardization or to make the data stationary by differencing. Impressively, simple deep learning neural network models are capable of making skillful forecasts as compared to naive models and tuned SARIMA models on univariate time series forecasting problems that have both trend and seasonal components with no pre-processing. In this tutorial, you will discover how to develop a suite of deep learning models for univariate time series forecasting. How to Develop Deep Learning Models for Univariate Time Series Forecasting Photo by Nathaniel McQueen, some rights reserved. You can learn more about the dataset from DataMarket. Save the file with the filename'monthly-car-sales.csv' in your current working directory. We can load this dataset as a Pandas series using the function read_csv(). Once loaded, we can summarize the shape of the dataset in order to determine the number of observations. We can then create a line plot of the series to get an idea of the structure of the series. We can tie all of this together; the complete example is listed below.

If you want to build your blog audience, you're going to have to get smarter with your content. One of the biggest challenges that bloggers and content marketers face is writing content that's optimized for search engines, yet will also appeal to people. According to Copyblogger, SEO is the most misunderstood topic online. But, SEO content isn't complicated, once you understand that people come first, before search algorithms. SEO firms make their money understanding these simple concepts. Thriving in your online business means that you must go beyond simply "writing content." Your content needs to accomplish two goals: first, appeal to the end-user (customers, clients, prospects, readers, etc.) and second, solve a particular problem. But, how do you create content that meets those goals? How do you create content that ranks well with Google and also persuades people? Don't worry if you can't afford an expensive SEO copywriter. You can do this following simple rules. And, that's what you're going to learn in this article. We all know what happens when you type a search query into a search engine and hit "enter": You get a list of search results that are relevant to your search term. Those results pages appear as a result of search engine optimization (SEO).

Complex networks are ubiquitous to several Computer Science domains. Centrality measures are an important analysis mechanism to uncover vital elements of complex networks. However, these metrics have high computational costs and requirements that hinder their applications in large real-world networks. In this tutorial, we explain how the use of neural network learning algorithms can render the application of the metrics in complex networks of arbitrary size. Moreover, the tutorial describes how to identify the best configuration for neural network training and learning such for tasks, besides presenting an easy way to generate and acquire training data. We do so by means of a general methodology, using complex network models adaptable to any application. We show that a regression model generated by the neural network successfully approximates the metric values and therefore are a robust, effective alternative in real-world applications. The methodology and proposed machine learning model use only a fraction of time with respect to other approximation algorithms, which is crucial in complex network applications.

Spot-checking algorithms is a technique in applied machine learning designed to quickly and objectively provide a first set of results on a new predictive modeling problem. Unlike grid searching and other types of algorithm tuning that seek the optimal algorithm or optimal configuration for an algorithm, spot-checking is intended to evaluate a diverse set of algorithms rapidly and provide a rough first-cut result. This first cut result may be used to get an idea if a problem or problem representation is indeed predictable, and if so, the types of algorithms that may be worth investigating further for the problem. Spot-checking is an approach to help overcome the "hard problem" of applied machine learning and encourage you to clearly think about the higher-order search problem being performed in any machine learning project. In this tutorial, you will discover the usefulness of spot-checking algorithms on a new predictive modeling problem and how to develop a standard framework for spot-checking algorithms in python for classification and regression problems. How to Develop a Reusable Framework for Spot-Check Algorithms in Python Photo by Jeff Turner, some rights reserved. We cannot know beforehand what algorithms will perform well on a given predictive modeling problem.