Get introduced to Deep Learning and learn history of Deep Learning.

TensorFlow has become the first choice for deep learning tasks because of the way it facilitates building powerful and sophisticated neural networks. The Google Cloud Platform is a great place to run TF models at scale, and perform distributed training and prediction. This course shows you how to use Google Cloud to train TensorFlow models and use them to predict results for multiple users. You will learn to efficiently train neural networks using large datasets and to serve your training models. With this video course, you will use the power of Google's Cloud Platform to train deep neural networks faster.

Designed for beginners, undergraduate students, and robotics enthusiasts, Practical Robot Design: Game Playing Robots is a comprehensive guide to the theory, design, and construction of game-playing robots. Drawing on years of robot building and teaching experience, the authors demonstrate the key steps of building a robot from beginning to end, with independent examples for extra modules. Each chapter covers basic theory and key topics, including actuators, sensors, robot vision, and control, with examples and case studies from robotic games. Furthermore, the book discusses the application of AI techniques and provides algorithms, and application examples with MATLABÂ code. Comprehensive coverate on drive motors and drive motor control References to vendor websites as necessary Digital control techniques, with a focus on implementation Techniques for designing and implementing slightly advanced controllers for pole-balancing robots Basic artificial intelligence techniques with examples in MATLAB Discussion of the vision systems, sensor systems, and controlling of robots The result of a summer course for students taking up robotic games as their final-year project, the authors hope that this book will empower readers in terms of the necessary background as well as the understanding of how various engineering fields are amalgamated in robotics.

Lectures translation is a case of spoken language translation and there is a lack of publicly available parallel corpora for this purpose. To address this, we examine a language independent framework for parallel corpus mining which is a quick and effective way to mine a parallel corpus from publicly available lectures at Coursera. Our approach determines sentence alignments, relying on machine translation and cosine similarity over continuous-space sentence representations. We also show how to use the resulting corpora in a multistage fine-tuning based domain adaptation for high-quality lectures translation. For Japanese--English lectures translation, we extracted parallel data of approximately 40,000 lines and created development and test sets through manual filtering for benchmarking translation performance. We demonstrate that the mined corpus greatly enhances the quality of translation when used in conjunction with out-of-domain parallel corpora via multistage training. This paper also suggests some guidelines to gather and clean corpora, mine parallel sentences, address noise in the mined data, and create high-quality evaluation splits. For the sake of reproducibility, we will release our code for parallel data creation.

To make machine exhibit human-like abilities in the domains like robotics and conversation, social commonsense knowledge (SCK), i.e., common sense about social contexts and social roles, is absolutely necessarily. Therefor, our ultimate goal is to acquire large-scale SCK to support much more intelligent applications. Before that, we need to know clearly what is SCK and how to represent it, since automatic information processing requires data and knowledge are organized in structured and semantically related ways. For this reason, in this paper, we identify and formalize three basic types of SCK based on first-order theory. Firstly, we identify and formalize the interrelationships, such as having-role and having-social_relation, among social contexts, roles and players from the perspective of considering both contexts and roles as first-order citizens and not generating role instances. Secondly, we provide a four level structure to identify and formalize the intrinsic information, such as events and desires, of social contexts, roles and players, and illustrate the way of harvesting the intrinsic information of social contexts and roles from the exhibition of players in concrete contexts. And thirdly, enlightened by some observations of actual contexts, we further introduce and formalize the embedding of social contexts, and depict the way of excavating the intrinsic information of social contexts and roles from the embedded smaller and simpler contexts. The results of this paper lay the foundation not only for formalizing much more complex SCK but also for acquiring these three basic types of SCK.

Finland is offering a techy Christmas gift to European Union citizens -- a free-of-charge online course in artificial intelligence in their own language, officials said Tuesday. The tech-savvy Nordic nation, led by the 34-year-old Prime Minister Sanna Marin, is marking the end of its rotating presidency of the EU at the end of the year with a highly ambitious goal. Instead of handing out the usual ties and scarves to EU officials and journalists, the Finnish government has opted to give practical understanding of AI to 1% of EU citizens, or about 5 million people, through a basic online course by the end of 2021. It is teaming up with the University of Helsinki, Finland's largest and oldest academic institution, and the Finland-based tech consultancy Reaktor. Teemu Roos, a University of Helsinki associate professor in the department of computer science, described the nearly $2 million project as "a civics course in AI" to help EU citizens cope with society's ever-increasing digitalization and the possibilities AI offers in the jobs market.

Say you've been driving on the roads of Phoenix, Arizona, all your life, and then you move to New York. Do you need to learn driving all over again? You just have to drive a bit more cautiously and adapt yourself to the new environment. The same can't be said about deep learning algorithms, the cutting edge of artificial intelligence, which are also one of the main components of autonomous driving. Despite having propelled the field of AI forward in recent years, deep learning, and its underlying technology, deep neural networks, suffer from fundamental problems that prevent them from replicating some of the most basic functions of the human brain.

There has never been so much talk about the future of work. We live in an age of technological acceleration and never has so much changed in such a short time. Technology greatly influences the way we live. Today we can order hot food that has just been made at our favorite restaurant and have it delivered to our doorstep. We can call a private transport through an application.

Jennifer Turner's algebra classes were once sleepy affairs and a lot of her students struggled to stay awake. She uses Bakpax, which can read students' handwriting and auto-grade schoolwork, and she assigns lectures for students to watch online while they are at home. Using the program has provided Turner, 41, who teaches at the Gloucester County Christian School in Sewell, N.J., more flexibility in how she teaches, reserving class time for interactive exercises. "The grades for homework have been much better this year because of Bakpax," Turner said. "Students are excited to be in my room, they're telling me they love math, and those are things that I don't normally hear."

Congratulations if you are reading this. That simply means, you have understood the importance of mathematics to truly understand and learn Data Science and Machine Learning. In this course, we will cover right from the foundations of Algebraic Equations, Linear Algebra, Calculus including Gradient using Single and Double order derivatives, Vectors, Matrices, Probability and much more. Without maths, there is no Machine Learning. Machine Learning uses mathematical implementation of the algorithms and without understanding the math behind it is like driving a car without knowing what kind of engine powers it.