With the rapid pace of innovation continually disrupting business models, and in many cases entire industries, how will online learning keep up to provide the relevant courseware for today's and tomorrow's workforce? This will be essential for economic growth and to support a thriving, college-educated workforce that's equipped with the very latest knowledge, ideas and technology. In the future, I believe that institutions at the forefront of online education will be recognized via several capabilities which will have digitally transformed today's EdTech market. They will include a powerful combination of omni-channel learning pathways, cognitive courseware, virtual counselors and AI-enabled course development and grading. These innovations, underpinned by artificial intelligence (AI), will help to provide students the ultimate choice in their courseware – including up-to-the-minute courses on high-interest/high-growth subject matter – as well as highly-innovative digital services that support them every step of the way to help maximize their success and personal objectives.

On July 9th, our team hosted a live webinar--Scalable End-to-End Deep Learning using TensorFlow and Databricks--with Brooke Wenig, Data Science Solutions Consultant at Databricks and Sid Murching, Software Engineer at Databricks. In this webinar, we walked you through how to use TensorFlow and Horovod (an open-source library from Uber to simplify distributed model training) on the Databricks Unified Analytics Platform to build a more effective recommendation system at scale. If you missed the webinar, you can view it now as well download the slides here. If you'd like free access Databricks Unified Analytics Platform and try our notebooks on it, you can access a free trial here. Toward the end, we held a Q&A, and below are all the questions and their answers.

Editor's Note: This article originally appeared on tensorflow.org and is being republished under the guidelines of the Creative Commons Attribution 3.0 License (more legal details at the end of the article). This tutorial is intended for readers who are new to both machine learning and TensorFlow. If you already know what MNIST is, and what softmax (multinomial logistic) regression is, you might prefer this faster paced tutorial. Be sure to install TensorFlow before starting either tutorial. When one learns how to program, there's a tradition that the first thing you do is print "Hello World." Just like programming has Hello World, machine learning has MNIST.

According to Forrester, the average enterprise is sitting on 100 terabytes (TB) or more of unstructured data. In order to extract actionable insights from that data and become insights-driven businesses, those enterprises must adopt best-in-class Text Analytics technology 1. Our guest speaker, Forrester's Boris Evelson, will discuss his latest report: The Forrester Wave: AI-Based Text Analytics Platforms Report Q2 2018 and review his findings and recommendations on what companies must look for when evaluating text analytics providers. Fabrice Martin, Clarabridge's Chief Product Officer will join Boris and provide examples of how companies have successfully adopted this technology in both the contact center and in traditional CEM settings as well as offer insight into the future of Clarabridge's text analytics capabilities. In this webinar, you will learn: How companies are transitioning from data-driven to insights-driven How to evaluate and select the right text analytics platform What to look for beyond the basic building blocks of sentiment and categorization 1 Source: Forrester Analytics Global Business Technographics Data And Analytics Survey, 2017.

Bloomberg presents "Foundations of Machine Learning," a training course that was initially delivered internally to the company's software engineers as part of its "Machine Learning EDU" initiative. This course covers a wide variety of topics in machine learning and statistical modeling. The primary goal of the class is to help participants gain a deep understanding of the concepts, techniques and mathematical frameworks used by experts in machine learning. It is designed to make valuable machine learning skills more accessible to individuals with a strong math background, including software developers, experimental scientists, engineers and financial professionals. The 30 lectures in the course are embedded below, but may also be viewed in this YouTube playlist.

Discovering and getting started with Machine Learning can be daunting. Perhaps you have a vague project idea and are looking for a place to start and adapt from. Or you're looking for inspiration and want to get a sense of what's possible. Today we're launching Seedbank, a place to discover interactive machine learning examples which you can run from your browser, no set-up required. Each example is a little seed to inspire you that you can edit, extend, and grow into your own projects and ideas, from data analysis problems to art projects. Recently Google has been releasing many examples of Machine Learning code in the form of Colab notebooks.

Dez Blanchfield speaks with business leaders about artificial intelligence and deep learning adoption in the "From Here to AI" podcast series from IBM Power Systems. When you start to investigate artificial intelligence (AI), or branch out to buy a couple AI servers to tinker with for your organization, the process of implementing a full AI solution can seem daunting. With the help of four business executives and AI leaders and digital transformation expert and avid podcaster Dez Blanchfield, we set out to outline the natural progression of implementing AI in the data center. No matter what stage of the journey you are on, these podcasts should help you get "from here to AI." Below is a quick overview of each session. We've posted them as a series so you can binge-listen if you have the time, or you can tee them up separately to plug into the ones that interest you most.

An Easy Introduction To AI And Deep Learning: Using TensorFlow, this course will show you how to build learning models and explore artificial intelligence. An Easy Introduction To Machine Learning Using Scikit-Learn: Explore how to help computers accurately predict outcomes. An Easy Introduction To Recommendation Systems: Using Python, you'll discover how to design and implement recommendation systems and learn the different ways they curate content. Choose your price, and you can start pioneering tomorrow's AI innovations with the Pay What You Want: Total Python Machine Learning Bundle. Our DealPost commerce team presents the best deals on products and services from online retailers and our partners.

So you've decided data science is the field for you. More and more businesses are becoming data driven, the world is increasingly becoming more connected and looks like every business will need a data science practice. So, the demand for data scientists is huge. Even better, everyone acknowledges the shortfall of talent in the industry. However, becoming a data scientist does not come easy. It needs a mix of problem solving, structured thinking, coding and various technical skills among others to be truly successful. If you are from a non-technical and non-mathematical background, there's a good chance a lot of your learning happens through books and video courses. Most of these resources don't teach you what the industry is looking for in a data scientist.

Brandman University is taking a new approach to adult education, focusing on student competencies and work experience rather than transcripts when deciding which students to admit and when they graduate. Brandman already is working with companies, including Walmart and Discover, to offer employee-education programs. The Irvine, California-based nonprofit university accepts subject matter expertise and experience as course credit, making it easier for working adults to earn college degrees and advance their careers. At most conventional colleges, students must fulfill prerequisite courses to earn admission and a set of required courses to earn a degree. Under the Brandman approach, if an applicant has, say, a 20-year career in finance but no formal coursework in finance, "she can now test out of many course requirements, simply by proving her mastery through standard assessments, writing samples, even work projects," says the university's chief financial officer.