Estimates suggest that by the year 2020 we will have 20 billion devices connected to the Internet. Industries are shifting, lines are blurring, and markets are changing all as a result of a technology and digital-centric approach to commerce and business. In this podcast, Cliff Justice, Partner in KPMG's Innovation and Enterprise Solutions group, sits down with Stan Lepeak to discuss: The convergence of robotic process automation (RPA), machine learning, cognitive computing, artificial intelligence, and advanced analytics are driving unparalleled business model transformation. To learn more visit KPMG's Digital Labor website: www.kpmg.com/us/digitallabor. To discover more, read Embracing the Cognitive Era.

The next time you're hired, you might find yourself getting information about payroll, vacations, and expenses by talking to a chatbot instead of consulting a handbook for new employees or talking to someone in HR. A startup called Talla, based in Boston, is working on chatbots designed to help new workers get up to speed and be more productive. The company is using advanced machine learning and natural language processing techniques in an effort to create software that is smarter than the average bot. Talla recently launched a simple prototype bot for managing to-do lists on the workplace communications platform Slack. So far, about 600 companies have added the chatbot to their Slack channel and are using it, says May.

Deep learning neural networks are very easy to create and evaluate in Python with Keras, but you must follow a strict model life-cycle. In this post you will discover the step-by-step life-cycle for creating, training and evaluating deep learning neural networks in Keras and how to make predictions with a trained model.

I'll respond more specifically for deep learning. You can get a broad idea of deep what deep learning is about through tutorial lectures that are available from the Web.

The fastest way to get good at applied machine learning is to practice on end-to-end projects. In this post you will discover how to work through a regression problem in Weka, end-to-end. Step-By-Step Regression Machine Learning Project Tutorial in Weka Photo by vagawi, some rights reserved. This tutorial will walk you through the key steps required to complete a machine learning project in Weka. Weka is the best platform for beginners getting started in applied machine learning.

Machine learning is a fascinating study, but how do you actually use it on your own problems? You may be confused as to how best prepare your data for machine learning, which algorithms to use or how to choose one model over another. In this post you will discover a 14-part crash course into applied machine learning using the Weka platform without a single mathematical equation or line of programming code. Applied Machine Learning With Weka Mini-Course Photo by Leon Yaakov, some rights reserved. Before we get started, let's make sure you are in the right place.

In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I have observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post. Recently, there has been an upsurge in the availability of many easy-to-use machine and deep learning packages such as scikit-learn, Weka, Tensorflow, R-caret etc. Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results. Selecting the right algorithm which includes giving considerations to accuracy, training time, model complexity, number of parameters and number of features.

Vernor Vinge once stated in his book The Singularity, "We are on the edge of change comparable to the rise of human life on Earth." As AI now undoubtedly plays a pivotal role in many industries, its risks and repercussions simply cannot be ignored; and shining a light upon these has never been more imperative. That's why, in response to the White House's Notice Of Request For Information (RFI), IBM has created what seems to be an AI 101--covering the huge field of AI and its vast potential applications. "The views of the American people, including stakeholders such as consumers, academic and industry researchers, private companies, and charitable foundations, are important to inform an understanding of current and future needs for AI in diverse fields," the RFI summary read. IBM's AI 101 consisted of a numbered list of topics in a somewhat re-ordered and slightly re-factored response to the RFI's questions.

This is a very incomplete and subjective selection of resources to learn about the algorithms and maths of Artificial Intelligence (AI) / Machine Learning (ML) / Statistical Inference (SI) / Deep Learning (DL) / Reinforcement Learning (RL). It is aimed at beginners (those without Computer Science background and not knowing anything about these subjects) and hopes to take them to quite advanced levels (able to read and understand DL papers). It is not an exhaustive list and only contains some of the learning materials that I have personally completed so that I can include brief personal comments on them. It is also by no means the best path to follow (nowadays most MOOCs have full paths all the way from basic statistics and linear algebra to ML/DL). But this is the path I took and in a sense it's a partial documentation of my personal journey into DL (actually I bounced around all of these back and forth like crazy).

The field of study that focuses on the interactions between human language and computers is called Natural Language Processing, or NLP for short. It sits at the intersection of computer science, artificial intelligence, and computational linguistics (Wikipedia). "Nat ur al Lan guage Pro cessing is a field that cov ers com puter un der stand ing and ma nip u la tion of hu man lan guage, and it's ripe with pos sib il it ies for news gath er ing," Anthony Pesce said in Natural Language Processing in the kitchen. "You usu ally hear about it in the con text of ana lyz ing large pools of legis la tion or other doc u ment sets, at tempt ing to dis cov er pat terns or root out cor rup tion." NLP is a way for computers to analyze, understand, and derive meaning from human language in a smart and useful way.