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.

From virtual reality police training programmes to gun-toting drones and iris scanners, a public security expo in China showed the range of increasingly high-tech tools available to the country's police. The exhibition, which ran Tuesday to Friday in Beijing, emphasised surveillance and monitoring technology just as the Communist government's domestic security spending has skyrocketed. Facial-recognition screens analysing candid shots of conference attendees were scattered around the exhibition hall, while other vendors packed their booths with security cameras. From virtual reality police training programmes to gun-toting drones and iris scanners, a public security expo in China showed the range of increasingly high-tech tools available to the country's police. More innocuous applications, like smart locks for homes and big data applications to reduce traffic congestion, also occupied large swathes of the conference.

Lifelong learning can be viewed as a continuous transfer learning procedure over consecutive tasks, where learning a given task depends on accumulated knowledge -- the so-called knowledge base. Most published work on lifelong learning makes a batch processing of each task, implying that a data collection step is required beforehand. We are proposing a new framework, lifelong online learning, in which the learning procedure for each task is interactive. This is done through a computationally efficient algorithm where the predicted result for a given task is made by combining two intermediate predictions: by using only the information from the current task and by relying on the accumulated knowledge. In this work, two challenges are tackled: making no assumption on the task generation distribution, and processing with a possibly unknown number of instances for each task. We are providing a theoretical analysis of this algorithm, with a cumulative error upper bound for each task. We find that under some mild conditions, the algorithm can still benefit from a small cumulative error even when facing few interactions. Moreover, we provide experimental results on both synthetic and real datasets that validate the correct behaviour and practical usefulness of the proposed algorithm.

As the industry continues to shift into value-based care, many organizations are leveraging new technology to support care delivery. But new technology requires a change in how care is provided, which should begin in medical school and continue throughout a clinician's career. "Outcomes and staff retention are driven, in part, by providing access to lifelong learning to advance skills and knowledge," said Cathy Wolfe, Wolters Kluwer health learning, research and practice CEO and president. "Advanced technologies like machine learning, artificial intelligence and virtual simulation are transforming adaptive learning models in ways that optimize learning and improve knowledge retention," she added. As a result, many healthcare organizations are investing in staff development to support evidence-based care, which can improve outcomes, reduce care variability and help with high reimbursements, Wolfe explained.

The fundamentals issues in Deep Learning are access to data, processing power and data scientists (i.e. But there are four more fundamental things that set companies apart. In this 10min presentation you will learn what the main challenges in Deep Learning are. For the next part of the tutorial, see a live example of running a TensorFlow MNIST example on Valohai: https://www.youtube.com/watch?v L5CcJ...

Algorithmic bias, or the notion that human biases can be magnified when consciously or subconsciously programmed into algorithms, has been a hot topic in machine learning. How do we create "fair" algorithms that behave in as unbiased a manner as possible? Google has released a free 60-minute online course on fairness as part of its popular Machine Learning Crash Course. This includes a short video lecture, materials on types of bias and how to identify and evaluate for bias, and a programming exercise to put your learning into action. Even if you are not a software engineer, you are a consumer of the fruits of their work and it behoves all of us to educate ourselves on how they function (and malfunction).

Recent developments such as Virtual and Augmented Reality as well as the introduction of gamification in corporate learning are changing the face of training. The challenge still remains to engage and entertain as well as teach in an environment that is harder to control by L&D professionals. With corporate education becoming almost entirely learner-centric, the solution I was advocating in one of my previous articles is personalized adaptive learning. It makes sense in the context of things and studies already support its benefits. However, there is some effort to be put in development and implementation so companies may not jump at the idea.