Robot Academy is an online platform that provides free-to-use undergraduate-level learning resources for robotics and robotic vision. The content was developed for two 6-week Massively Open Online Courses (MOOCs) that Corke taught in 2015 and 2016. This content is now available as individual lessons (over 200 videos, each less than 10 minutes long) or in masterclasses (collections of videos, around 1 hour in duration, previously a MOOC lecture). Unlike a MOOC, all lessons are available all the time. While the content is typically designed for undergraduate-level students, around 20% of the lessons require no more than general knowledge.

Learn how to use NumPy, Pandas, Seaborn, Matplotlib, Plotly, Scikit-Learn, Machine Learning, Tensorflow, and more! This comprehensive course by Jose Portilla will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms! Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!

This is the first part or a series of posts. To have a short introduction, read my Intro post. It is not rare for technology and engineering to take inspiration from nature's great designs. In this post, I will talk about genetic or evolutionary algorithms, their role in robotics, and more widely in computer science. Evolutionary algorithms are inspired by the natural process of evolution and natural selection.

For more than 250 years the fundamental drivers of economic growth have been technological innovations. The most important of these are what economists call general-purpose technologies -- a category that includes the steam engine, electricity, and the internal combustion engine. The internal combustion engine, for example, gave rise to cars, trucks, airplanes, chain saws, and lawnmowers, along with big-box retailers, shopping centers, cross-docking warehouses, new supply chains, and, when you think about it, suburbs. Companies as diverse as Walmart, UPS, and Uber found ways to leverage the technology to create profitable new business models. The most important general-purpose technology of our era is artificial intelligence, particularly machine learning (ML) -- that is, the machine's ability to keep improving its performance without humans having to explain exactly how to accomplish all the tasks it's given. Within just the past few years machine learning has become far more effective and widely available. We can now build systems that learn how to perform tasks on their own. Why is this such a big deal? First, we humans know more than we can tell: We can't explain exactly how we're able to do a lot of things -- from recognizing a face to making a smart move in the ancient Asian strategy game of Go. Prior to ML, this inability to articulate our own knowledge meant that we couldn't automate many tasks. Second, ML systems are often excellent learners.

Recent years have witnessed amazing progress in AI related fields such as computer vision, machine learning and autonomous vehicles. As with any rapidly growing field, however, it becomes increasingly difficult to stay up-to-date or enter the field as a beginner. While several topic specific survey papers have been written, to date no general survey on problems, datasets and methods in computer vision for autonomous vehicles exists. This paper attempts to narrow this gap by providing a state-of-the-art survey on this topic. Our survey includes both the historically most relevant literature as well as the current state-of-the-art on several specific topics, including recognition, reconstruction, motion estimation, tracking, scene understanding and end-to-end learning. A lengthy, thorough overview, and probably the best starting place for anyone looking to get up to speed in the field quickly, and in one spot.

Sequence prediction is a problem that involves using historical sequence information to predict the next value or values in the sequence. The sequence may be symbols like letters in a sentence or real values like those in a time series of prices. Sequence prediction may be easiest to understand in the context of time series forecasting as the problem is already generally understood. In this post, you will discover the standard sequence prediction models that you can use to frame your own sequence prediction problems. Recurrent Neural Networks, like Long Short-Term Memory (LSTM) networks, are designed for sequence prediction problems.

The future of work looks full of promise. Combining human brainpower with artificial intelligence, virtual reality and automatization will revolutionize how we work. "The future of work looks full of promise." Already, robotic enhancement is helping humans exceed their natural capabilities. AI is opening the door to real-time, personalized intelligent services, cutting waste and maximizing results.

Everyone is talking about artificial intelligence (AI). In fact, many SAS customers who've been using our analytics capabilities for years or even decades are asking: This flurry of inquiries led to a decision to team up with TM Forum to tackle the subject on a live webinar and in an upcoming Quick Insights paper. If you have some of the same questions, keep reading to learn where you can get the answers. But first, a short tangent about me: this August marks 20 years that I've been at SAS. And believe it or not, in all those years, I have never done a live, global webinar to an external audience.

About this course: This 1-week accelerated on-demand course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities. At the end of this course, participants will be able to: • Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform • Use CloudSQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform • Employ BigQuery and Cloud Datalab to carry out interactive data analysis • Choose between Cloud SQL, BigTable and Datastore • Train and use a neural network using TensorFlow • Choose between different data processing products on the Google Cloud Platform Before enrolling in this course, participants should have roughly one (1) year of experience with one or more of the following: • A common query language such as SQL • Extract, transform, load activities • Data modeling • Machine learning and/or statistics • Programming in Python Google Account Notes: • You'll need a Google/Gmail account and a credit card or bank account to sign up for the Google Cloud Platform free trial (Google is currently blocked in China).

Let me know what you think @StephLKim.