With Enterprise Connect 2018 fast approaching, you're no doubt doing a lot of planning to prioritize which meetings to schedule and which sessions to attend. You can't do it all, and this is my moment to draw your attention to the Speech Technology track, a new addition to the EC lineup. In this inaugural year, the Speech Tech track may not yet be on your radar. I'm hoping this post will change that, especially since I'm kicking off the program with a tutorial on enterprise speech technology on Monday, March 12, at 8:00 a.m. If you like what I have to say, you'll probably want to attend more sessions for this track, and that will help validate the move to put speech tech on the program.

Around the Hackaday secret bunker, we've been talking quite a bit about machine learning and neural networks. There's been a lot of renewed interest in the topic recently because of the success of TensorFlow. If you are adept at Python and remember your high school algebra, you might enjoy [Oliver Holloway's] tutorial on getting started with Tensorflow in Python. Then he shows some basic setup operations. From there, he has the software "learn" how to classify random points that either fall into a circle or don't.

In this episode, you will learn about the General Availability release of Azure Bot Service and Language Understanding service, the two top-notch AI services to create amazing conversational AI experiences. You will learn how to get started easily with Azure Bot Service to create a bot using out of box templates such as the Language Understanding template, and reach your audience with multiple supported channels.

Humans and animals have the ability to continually acquire and fine-tune knowledge throughout their lifespan. This ability is mediated by a rich set of neurocognitive functions that together contribute to the early development and experience-driven specialization of our sensorimotor skills. Consequently, the ability to learn from continuous streams of information is crucial for computational learning systems and autonomous agents (inter)acting in the real world. However, continual lifelong learning remains a long-standing challenge for machine learning and neural network models since the incremental acquisition of new skills from non-stationary data distributions generally leads to catastrophic forgetting or interference. This limitation represents a major drawback also for state-of-the-art deep neural network models that typically learn representations from stationary batches of training data, thus without accounting for situations in which the number of tasks is not known a priori and the information becomes incrementally available over time. In this review, we critically summarize the main challenges linked to continual lifelong learning for artificial learning systems and compare existing neural network approaches that alleviate, to different extents, catastrophic interference. Although significant advances have been made in domain-specific continual lifelong learning with neural networks, extensive research efforts are required for the development of general-purpose artificial intelligence and autonomous agents. We discuss well-established research and recent methodological trends motivated by experimentally observed lifelong learning factors in biological systems. Such factors include principles of neurosynaptic stability-plasticity, critical developmental stages, intrinsically motivated exploration, transfer learning, and crossmodal integration.

Evaluating the log determinant of a positive definite matrix is ubiquitous in machine learning. Applications thereof range from Gaussian processes, minimum-volume ellipsoids, metric learning, kernel learning, Bayesian neural networks, Determinental Point Processes, Markov random fields to partition functions of discrete graphical models. In order to avoid the canonical, yet prohibitive, Cholesky $\mathcal{O}(n^{3})$ computational cost, we propose a novel approach, with complexity $\mathcal{O}(n^{2})$, based on a constrained variational Bayes algorithm. We compare our method to Taylor, Chebyshev and Lanczos approaches and show state of the art performance on both synthetic and real-world datasets.

Course lectures, whether on a college campus or in a corporation, are predominantly one-size-fits-all, with the dominant mode being teachers speaking to students,

In this course we are going to look at advanced NLP. Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices. These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words. In this course I'm going to show you how to do even more awesome things. We'll learn not just 1, but 4 new architectures in this course.

Organizations expend constant effort to deliver information employees need to know for their jobs. You depend on training to help your employees make more sales, provide better customer service, avoid regulatory issues, and make fewer mistakes. But training has no value if we can't retrieve the information we're taught. Training reinforcement is essential to ensure that knowledge and skills learned in training are applied on the job. If you are new to training reinforcement or a bit unfamiliar, here are seven key things to know.

The Priberam Machine Learning Lunch Seminars are a series of informal meetings which occur every two weeks at Instituto Superior Técnico, in Lisbon. It works as a discussion forum involving different research groups, from IST and elsewhere. Its participants are interested in areas such as (but not limited to): statistical machine learning, signal processing, pattern recognition, computer vision, natural language processing, computational biology, neural networks, control systems, reinforcement learning, or anything related (even if vaguely) with machine learning. The seminars last for about one hour (including time for discussion and questions) and revolve around the general topic of Machine Learning. The speaker is a volunteer who decides the topic of his/her presentation.

This game-changing course will cover artificial intelligence tools in website, chatbot design and analytics which will help you to create website in minutes. I will teach you to easily create websites in the fastest time possible and customize your site look and feel according to your requirement in a simple drag-and-drop timeline by talking to chatbots. Why learn this course and how is this a differentiator? This course can change your life as a web developer or marketer. With no coding experience, you can create amazing looking websites and pave the path for unlimited designs and interchange content and play god.