The reality is that it's neither one. The fear is that automation is sweeping all before it, gobbling up jobs; displacing millions of workers and leaving them unemployed and, worse, unemployable; and exacerbating the income gap. It's reviled by many as a greater threat than jobs shipped overseas, even prompting some to suggest taxing robots to slow their spread. The counterview is that automation is not replacing jobs nearly fast enough. We don't have enough workers to do the jobs available now, and this will get worse as demographic trends play out.

In this DZone Refcard, learn about TensorFlow, an open-source library with a rich set of application programming interfaces for most major languages and environments needed for deep learning programs like sentiment analysis and object detection.

Webinar: Tuesday, February 13, 1:00 pm ET / 10:00 am PT Register now Building predictive applications allows companies to respond to new threats and take advantage of developing opportunities. But executing these new applications against high-volume event streams with sub-second latency requires a powerful combination of machine learning and streaming analytics. In this webinar, you'll learn how to create and evaluate new machine learning models with DataRobot and deploy them within the SQLstream Blaze streaming analytics engine - so that you can identify risk in real-time and prevent fraud as it happens - rather than after the fact. On this 45-minute webinar, you'll discover how Automated Machine Learning and Streaming Analytics provides: - Automated machine learning models that can be created by anyone - Rapid deployment against incoming, high-volume events with extremely low-latency - The ability to update those models seamlessly - with no downtime - Deep transparency, including prediction reason codes, to enable rapid, targeted investigations Speakers: Greg Michaelson, PhD - Head of DataRobot Labs David Hickman - Senior Director, Product Marketing, SQLstream Register now

Data is being generated in huge quantities everywhere. Twitter generates 12 TB of data every day, Facebook generates 25 TB of data everyday and Google generates much more than these quantities everyday. Conventional models on such huge data are infeasible. All these data contribute to prediction. A good algorithm can take in such variety of data.

Finally, a comprehensive hands-on machine learning course with specific focus on regression based models for the investment community and any passionate investors. In the past few years, there has been a massive adoption and growth in the use of data science, artificial intelligence and machine learning to find alpha. However, information on and application of machine learning to investment are scarce. This course has been designed to address that. It is meant to spark your creative juices.

Organizations everywhere, are aiming to embed AI in everything they do. But while 85 percent of companies surveyed believe AI will help them outpace competition, only a handful of them have gotten started. While seemingly daunting, there are foundational technologies available now to all organizations. Most likely these pieces are already in place. Your company's journey to AI has already begun and is ready to be accelerated.

One only needs to type in the following command to start finding the most optimal learning rate to use before training a neural network. At this juncture we've covered what learning rate is all about, it's importance, and how can we systematically come to an optimal value to use when we start training our model. Next we would go through how learning rates can still be used to improve our model's performance.

Hurry--early price ends March 9. This post was originally published on Insight Data Science; it is republished here with permission. Whether you are an established company or working to launch a new service, you can always leverage text data to validate, improve, and expand the functionalities of your product. The science of extracting meaning and learning from text data is an active topic of research called natural language processing (NLP). NLP produces new and exciting results on a daily basis, and is a very large field.

Two new training provider partnerships are exploring the possibility of using artificial intelligence (AI) to help. Last month, Mandel Communications announced a partnership with Orai on a free communication coaching app that uses AI to provide instant feedback on speech, including clarity, the use of filler words, pacing and energy. This feedback is based on computational linguistics research and a data set of, according to co-founder Danish Dhamani, "thousands of people with different accents, different languages … speaking across the world." The app also includes Mandel's communication skills training and the ability to send recordings to users' managers and coaches for continued development. The company also offers the ability to create custom content for a company's specific messaging.

Developing deep learning applications just got even simpler and faster. In this session, you will learn how to program deep learning models using Gluon, the new intuitive, dynamic programming interface available for the Apache MXNet open-source framework. We'll also explore neural network architectures such as multi-layer perceptrons, convolutional neural networks (CNNs) and LSTMs.