Navigating through unstructured environments is a basic capability of intelligent creatures, and thus is of fundamental interest in the study and development of artificial intelligence. Long-range navigation is a complex cognitive task that relies on developing an internal representation of space, grounded by recognisable landmarks and robust visual processing, that can simultaneously support continuous self-localisation ("I am here") and a representation of the goal ("I am going there"). Building upon recent research that applies deep reinforcement learning to maze navigation problems, we present an end-to-end deep reinforcement learning approach that can be applied on a city scale. Recognising that successful navigation relies on integration of general policies with locale-specific knowledge, we propose a dual pathway architecture that allows locale-specific features to be encapsulated, while still enabling transfer to multiple cities. We present an interactive navigation environment that uses Google StreetView for its photographic content and worldwide coverage, and demonstrate that our learning method allows agents to learn to navigate multiple cities and to traverse to target destinations that may be kilometres away. A video summarizing our research and showing the trained agent in diverse city environments as well as on the transfer task is available at: https://sites.google.com/view/streetlearn.

PYONGYANG – North Korea has recently started to introduce state-of-the-art technology for the training of future schoolteachers in a possible world first. At the newly remodeled Pyongyang Teacher Training College, the mostly female students study how to educate kindergartners and primary school children with the aid of virtual reality and 3D display technologies. A group of Kyodo News reporters was granted rare access to the college late last week. In one classroom is installed a large widescreen monitor on which are displayed animated avatars representing primary school pupils. Speaking to the virtual children through a microphone, they respond in a timely manner.

This complete guide will teach you how to build and deploy Machine Learning models on your mobile device with TensorFlow Lite. You will understand the core architecture of TensorFlow Lite and the inbuilt models that have been optimized for mobiles. You will learn to implement smart data-intensive behavior, fast, predictive algorithms, and efficient networking capabilities with TensorFlow Lite. You will master the TensorFlow Lite Converter, which converts models to the TensorFlow Lite file format. This course will teach you how to solve real-life problems related to Artificial Intelligence--such as image, text, and voice recognition--by developing models in TensorFlow to make your applications really smart.

Implementing fully connected nets, convnets, RNNs, backprop and SGD from scratch (using pure python, numpy, or even JS) and training these models on small datasets is a great way to learn how neural nets work. Invest time to gain valuable intuition before jumping onto frameworks. This elicited a series of response tweets from François Chollet (@fchollet), creator of Keras, which, when considered collectively, presents a different point of view. Grad students knew how to implement neural nets in C in 2000. And they didn't have good intuition about them.

Data analysis is a process for inspecting, consolidating, transforming, and making sense of data in a way that guides the decision-making process. If you're interested to know the statistical data analysis techniques and implement them using the popular Java APIs and 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. This Learning Path starts by showing you the various techniques of pre-processing your data.

Machine Learning is one of the most transformative and impactful technologies of our time. From advertising to healthcare, to self-driving cars, it is hard to find an industry that has not been or is not being revolutionized by machine learning. Using the two most popular frameworks, Tensor Flow and Scikit-Learn, this course will show you insightful tools and techniques for building intelligent systems. Using Scikit-learn you will create a Machine Learning project from scratch, and, use the Tensor Flow library to build and train professional neural networks. We will use these frameworks to build a variety of applications for problems such as ad ranking and sentiment classification.

Do your non-player characters lack drive and ambition? Are they slow, stupid and constantly banging their heads against the wall? Then this course is for you. Join Penny as she explains, demonstrates and assists you to create your very own NPCs in Unity with C#. All you need is a sound knowledge of Unity, C# and the ability to add two numbers together.

If there is a place where human knowledge in its all-out complexity can be argued, learned and shared, it must be, without any doubt, the University. Universities can be understood not just as a place where this knowledge is passed on to new young generations but a place where knowledge is indeed created. A place which surpasses business and money-making-driven schemes, and rather embrassing everything that has to do with knowledge, and where knowledge can be born and transmitted. From politics and arts, history and science, architecture and education, all human knowledge is well preserved and developed within the different colleges that makes up an University. Although all branches are crucial for a healthy growth of the society itself, there are few of them that are getting higher demand on the outside world: the likes of IT and computing related.

Data science is an ever-evolving field, with exponentially growing popularity. Data science includes techniques and theories extracted from the fields of statistics, computer science, and most importantly machine learning, databases, and visualization. So, if you're a developer who wants to enter in the field of data science by exploring concepts of statistics, data analysis, and data mining, then follow this Learning Path. Packt's Video Learning Path is 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. This Learning Path begins with explaining the steps to analyse data and identify which summary statistics are relevant to the type of data you are summarizing.

Are you interested to explore advanced algorithm concepts such as random forest vector machine, K- nearest, and more through real-world examples? 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. Machine learning and data science are some of the top buzzwords in the technical world today. Machine learning - the application and science of algorithms that makes sense of data, is the most exciting field of all the computer sciences! It explores the study and construction of algorithms that can learn from and make predictions on data.