Sequence prediction is a problem that involves using historical sequence information to predict the next value or values in the sequence. The sequence may be symbols like letters in a sentence or real values like those in a time series of prices. Sequence prediction may be easiest to understand in the context of time series forecasting as the problem is already generally understood. In this post, you will discover the standard sequence prediction models that you can use to frame your own sequence prediction problems. Recurrent Neural Networks, like Long Short-Term Memory (LSTM) networks, are designed for sequence prediction problems.

The future of work looks full of promise. Combining human brainpower with artificial intelligence, virtual reality and automatization will revolutionize how we work. "The future of work looks full of promise." Already, robotic enhancement is helping humans exceed their natural capabilities. AI is opening the door to real-time, personalized intelligent services, cutting waste and maximizing results.

Everyone is talking about artificial intelligence (AI). In fact, many SAS customers who've been using our analytics capabilities for years or even decades are asking: This flurry of inquiries led to a decision to team up with TM Forum to tackle the subject on a live webinar and in an upcoming Quick Insights paper. If you have some of the same questions, keep reading to learn where you can get the answers. But first, a short tangent about me: this August marks 20 years that I've been at SAS. And believe it or not, in all those years, I have never done a live, global webinar to an external audience.

About this course: This 1-week accelerated on-demand course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities. At the end of this course, participants will be able to: • Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform • Use CloudSQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform • Employ BigQuery and Cloud Datalab to carry out interactive data analysis • Choose between Cloud SQL, BigTable and Datastore • Train and use a neural network using TensorFlow • Choose between different data processing products on the Google Cloud Platform Before enrolling in this course, participants should have roughly one (1) year of experience with one or more of the following: • A common query language such as SQL • Extract, transform, load activities • Data modeling • Machine learning and/or statistics • Programming in Python Google Account Notes: • You'll need a Google/Gmail account and a credit card or bank account to sign up for the Google Cloud Platform free trial (Google is currently blocked in China).

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There's no doubt about it - Data Science is big news right now. We see it on the news every day, the increasing number of news stories about Big Data, the Internet of Things, Deep Learning, Artificial Intelligence, smart cars, smart cities, smart politicians. OK, maybe I went a bit too far with that last one... Every month I get an email from Udemy telling me which courses are their best sellers. The list isn't about Data Science, but there are always plenty of Data Science courses right up there at the top of the list. We decided to share this resource with you, and so here are Udemy's top selling courses.

Join us at the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017) for a full day workshop that will bring together international stakeholders in robotics to examine best practices for accelerating robotics innovation through strategic policy frameworks. This is a unique opportunity to learn from people who have played a significant role in designing and implementing major strategic robotics initiatives around the globe. Objectives In the past decade, a number of governing bodies and industry consortia have developed strategic roadmaps to guide investment and development of robotic technology. With the roadmaps from the US, South Korea, Japan and EU etc. well underway, the time is right to take stock of these strategic robotics initiatives to see what is working, what is not, and what best practices in roadmap development might be broadly applied to other regions. The objective of this two-part workshop is to examine the process of how these policy frameworks came to be created in the first place, how they have been tailored to local capabilities and strengths, and what performance indicators are being used to measure their success -- so that participants may draw from international collective experience as they design and evaluate strategic robotics initiatives for their own regions.

Imbalanced classes put "accuracy" out of business. This is a surprisingly common problem in machine learning (specifically in classification), occurring in datasets with a disproportionate ratio of observations in each class. Standard accuracy no longer reliably measures performance, which makes model training much trickier. In this guide, we'll explore 5 effective ways to handle imbalanced classes. Let's say your client is a leading research hospitals, and they've asked you to train a model for detecting a disease based on biological inputs collected from patients.

When I was writing books on networking and programming topics in the early 2000s, the web was a good, but an incomplete resource. Blogging had started to take off, but YouTube wasn't around yet, nor was Quora, Twitter, or podcasts. Over ten years later as I've been diving into AI and machine learning, it is a completely different ballgame. There are so many resources -- it's difficult to know where to start (and stop)! To save you some of the effort I went through in researching all the different nooks and crannies of the web to find the best content; I've organized them into a big collection here.

A time series is a sequentially indexed representation of your historical data that can be used to solve classification and segmentation problems, in addition to forecasting future values of numerical properties, for example, air pollution level in Madrid for the last two days. This is a very versatile method often used for predicting stock prices, sales forecasting, website traffic, production and inventory analysis, or weather forecasting, among many other use cases. Soon, BigML will have time series as a new resource. Following our mission of democratizing machine learning and making it easy for everyone, we will provide new learning material for you to start with time series from scratch and become a power user over time. We start by publishing a series of six blog posts that will progressively dive deeper into the technical and practical aspects of time series with an emphasis on time series models for forecasting.