On May 9, Netflix launched its own research website. This highlights the focus Netflix has on Deep Learning and Data Science. The site is extremely well designed showing vertical classification of the different areas that Netflix research works on along with the horizontal business areas where Data Science is deployed at Netflix. It has some great articles with everything from video encoding to A/B testing where they use Data Science. I found the website to be very comprehensive making it a go to destination for things Netflix Data Science from different verticals to jobs.

Recent work has shown that deep reinforcement-learning agents can learn to follow language-like instructions from infrequent environment rewards. However, for many real-world natural language commands that involve a degree of underspecification or ambiguity, such as "tidy the room", it would be challenging or impossible to program an appropriate reward function. To overcome this, we present a method for learning to follow commands from a training set of instructions and corresponding example goal-states, rather than an explicit reward function. Importantly, the example goal-states are not seen at test time. The approach effectively separates the representation of what instructions require from how they can be executed.

To get a sense of the extent to which drones have captured the public imagination, look to the skies. In Folsom, Calif., more than 950 drones took to the skies earlier this month to create a glowing, real-world version of Time magazine's iconic cover, hovering 400 feet above the ground. "Up in the sky, I saw the future," a local resident told a local news station. In recent years, there have been no shortage of publicity-grabbing announcements involving unmanned aerial vehicles (UAVs)--just consider Amazon's headline-grabbing goal of drone-delivered packages. But reality is catching up with these long-stated aspirations and, through a combination of drone-friendly legislation and practical research, Virginia is poised to become a key player in determining how to make day-to-day drone operations a reality in a wide range of sectors.

For day 3 of project recaps from this year's unconf, here is an overview of the next five projects. Stay tuned for the last recap tomorrow. In the spirit of exploration and experimentation at rOpenSci unconferences, these projects are not necessarily finished products or in scope for rOpenSci packages. Let's dive into today's 5 projects in focus! Summary: jobstatus helps keep an eye on how complex and long-running jobs, including jobs running in parallel, are progressing.

Online job postings offer one of the most telling clues about a company's next move. Do recent job postings at Sonos suggest a coming IPO? As AI increasingly takes center stage in tech innovation, the question of transparency, privacy and ethics becomes paramount. Growth Tribe's David Arnoux on why a solid understanding of AI will be critical for all executives and how they can use it to get ahead of the competition

In Web and Network Data Science, a top faculty member of Northwestern University's prestigious analytics program presents the first fully-integrated treatment of both the business and academic elements of web and network modeling for predictive analytics. Some books in this field focus either entirely on business issues (e.g., Google Analytics and SEO); others are strictly academic (covering topics such as sociology, complexity theory, ecology, applied physics, and economics). This text gives today's managers and students what they really need: integrated coverage of concepts, principles, and theory in the context of real-world applications. Building on his pioneering Web Analytics course at Northwestern University, Thomas W. Miller covers usability testing, Web site performance, usage analysis, social media platforms, search engine optimization (SEO), and many other topics. He balances this practical coverage with accessible and up-to-date introductions to both social network analysis and network science, demonstrating how these disciplines can be used to solve real business problems.

Data analysis is a process for inspecting, consolidating, transforming, and making sense of data in a way that guides the decision-making process. Scala has emerged as an important tool for efficiently performing various data analysis tasks. So, if you're interested to load raw datasets with Spark, and perform exploratory data analysis on those via plotting Scala libraries, then go for this Learning Path. Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. Let's take a quick look at your learning journey.

For someone who's interested in the ways in which formal logical systems are deployed in AI-related fields, in particular as models for machine learning, can anyone point:

Medical Neuroscience explores the functional organization and neurophysiology of the human central nervous system, while providing a neurobiological framework for understanding human behavior. In this course, you will discover the organization of the neural systems in the brain and spinal cord that mediate sensation, motivate bodily action, and integrate sensorimotor signals with memory, emotion and related faculties of cognition. The overall goal of this course is to provide the foundation for understanding the impairments of sensation, action and cognition that accompany injury, disease or dysfunction in the central nervous system. The course will build upon knowledge acquired through prior studies of cell and molecular biology, general physiology and human anatomy, as we focus primarily on the central nervous system. This online course is designed to include all of the core concepts in neurophysiology and clinical neuroanatomy that would be presented in most first-year neuroscience courses in schools of medicine.

With the recent release of MXNet version 1.2.0, the new MXNet Scala Inference API was released. This release focuses on optimizing the developer experience for inference applications written in Scala. Scala is a general-purpose programming language that supports both functional programming and a strong static type system, and is used with high scale distributed processing with platforms such as Apache Spark. Now that you have been given the grand tour of the new Scala API, you're ready to try it out yourself. You will first need to setup your dev environment with mxnet-full package, and then you can try your hand at an image classification example and an object detection example (which we will demonstrate in the next post).