Today's highlighted deal comes via our Online Courses section of the Neowin Deals store, where you can save 95% off* this Complete iOS 11 Machine Learning Masterclass. Become a master of smart app development. Ready to start building professional, career-boosting mobile apps? Machine learning is one of the fastest growing fields in tech and many apps are starting to integrate machine learning to add a layer of intelligence. Only the smart will survive. In this comprehensive course, you'll learn how to build apps for the brand new iOS 11 while incorporating machine learning, turning them into smart apps that will interact and learn from users to enhance the user experience.

Time series datasets may contain trends and seasonality, which may need to be removed prior to modeling. Trends can result in a varying mean over time, whereas seasonality can result in a changing variance over time, both which define a time series as being non-stationary. Stationary datasets are those that have a stable mean and variance, and are in turn much easier to model. Differencing is a popular and widely used data transform for making time series data stationary. In this tutorial, you will discover how to apply the difference operation to your time series data with Python.

The data for your sequence prediction problem probably needs to be scaled when training a neural network, such as a Long Short-Term Memory recurrent neural network. When a network is fit on unscaled data that has a range of values (e.g. In this tutorial, you will discover how to normalize and standardize your sequence prediction data and how to decide which to use for your input and output variables. How to Scale Data for Long Short-Term Memory Networks in Python Photo by Mathias Appel, some rights reserved. There are two types of scaling of your series that you may want to consider: normalization and standardization.

Sure, there are lots of tutorials and overviews on gaining the insight you need into picking up machine learning, but many (most?) of them take the long view: get a foundation first, learn the basics next, then learn a bit of complementary theory before getting too far ahead of yourself in practical terms, take a step back, try your hand at a few examples, undertake a project on your own... This is all great advice, and a great approach to learning... well, almost anything. But let's say you're not starting from scratch. Or you don't have the patience to go through all of the motions. Let's say you want to hit the ground running and scramble under pressure to learn everything right now.

American Institute of A.I. offers 12 executive courses and 7 certification programs. Click the courses below to read the course descriptions.

This course created by Data Weekends, Jose Portilla, and Francesco Mosconi is designed to provide a complete introduction to Deep Learning. It is aimed at beginners and intermediate programmers and data scientists who are familiar with Python and want to understand and apply Deep Learning techniques to a variety of problems. We start with a review of Deep Learning applications and a recap of Machine Learning tools and techniques. Then we introduce Artificial Neural Networks and explain how they are trained to solve Regression and Classification problems. Over the rest of the course we introduce and explain several architectures including Fully Connected, Convolutional and Recurrent Neural Networks, and for each of these we explain both the theory and give plenty of example applications.

Google has taught its DeepMind AI to navigate a parkour course by using reinforcement learning. Reinforcement learning is the practice of rewarding desirable behaviour. The faster the AI could navigate the virtual parkour course, the greater the reward. Further incentives and penalties were added for various other metrics. "We train several simulated bodies on a diverse set of challenging terrains and obstacles, using a simple reward function based on forward progress," explains Nicolas Heess, a researcher on the project.

This three-day course is for data engineers, analysts, architects; software engineers; IT operations; and technical managers interested in a thorough, hands-on overview of the Apache Spark platform. The course covers the core APIs for using Spark, fundamental mechanisms and basic internals of the platform, SQL and other high-level data access tools, as well as Spark's streaming capabilities and machine learning APIs. Each topic includes slide and lecture content along with hands-on use of Spark through the elegant Databricks web-based notebook environment. Inspired by tools like IPython/Jupyter and Matlab, Databricks notebooks allow attendees to code jobs, data analysis queries, and generate visualizations using their own cloud-based Spark cluster, accessed through a web browser.* Duration: 3 Days, Full Time (9AM to 5PM) We will have a break from noon to 1pm daily; lunch will not be provided, but there are several options nearby.

This training is ideal for engineers creating algorithms and software for visual machine perception in all types of applications (e.g. in the industrial, medical, consumer, retail, public safety or automotive area) who want to quickly come up to speed on using TensorFlow for these applications. It's also appropriate for managers who want to get a flavor for creating deep neural networks and using TensorFlow. After the training, attendees will be ready to begin using TensorFlow productively in their work. In addition, for attendees who require an introduction to deep neural network algorithms, the Embedded Vision Alliance will offer a two-hour video tutorial presentation online prior to the TensorFlow class.

For decades, textbooks were seen as the foundation for instruction in American schools. These discipline-specific tomes were a fundamental part of the educational infrastructure, assigned to students for each subject and carried in heavy backpacks every day – from home to school and back again. The experience of students is much different today. As a scholar of learning technologies and a director for outreach and engagement at Ohio State's College of Education and Human Ecology, we've seen how technological advances and an increase in digital curriculum materials have hastened the move away from textbooks. Does all of this technology spell the end of traditional textbooks?