Python was slowly becoming the de-facto language for Deep Learning models. In this course you will learn how to build powerful convolutional neural networks in R, from scratch. This special kind of deep networks is used to make accurate predictions in various fields of research, either academic or practical. If you want to use R for advanced tasks like image recognition, face detection or handwriting recognition, this course is the best place to start. All the procedures are explained live, step by step, in every detail. Most important, you will be able to apply immediately what you will learn, by simply replicating and adapting the code we will be using in the course.

They are powerful data mining techniques that allow you to detect patterns in your data or variables. For each technique, a number of practical exercises are proposed. By doing these exercises you'll actually apply in practice what you have learned. This course is your opportunity to become a machine learning expert in a few weeks only! With my video lectures, you will find it very easy to master the major machine learning techniques. Everything is shown live, step by step, so you can replicate any procedure at any time you need it. So click the "Enroll" button to get instant access to your machine learning course. It will surely provide you with new priceless skills. And, who knows, it could give you a tremendous career boost in the near future.

Scientists who study human-robot interaction often focus on understanding human intentions from a robot's perspective, so the robot learns to cooperate with people more effectively. But human-robot interaction is a two-way street, and the human also needs to learn how the robot behaves. Thanks to decades of cognitive science and educational psychology research, scientists have a pretty good handle on how humans learn new concepts. So, researchers at MIT and Harvard University collaborated to apply well-established theories of human concept learning to challenges in human-robot interaction. They examined past studies that focused on humans trying to teach robots new behaviors.

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Designing a distribution of environments in which RL agents can learn interesting and useful skills is a challenging and poorly understood task, for multi-agent environments the difficulties are only exacerbated. One approach is to train a second RL agent, called a teacher, who samples environments that are conducive for the learning of student agents. However, most previous proposals for teacher rewards do not generalize straightforwardly to the multi-agent setting. We examine a set of intrinsic teacher rewards derived from prediction problems that can be applied in multi-agent settings and evaluate them in Mujoco tasks such as multiagent Hide and Seek [1] as well as a diagnostic single-agent maze task. Of the intrinsic rewards considered we found value disagreement to be most consistent across tasks, leading to faster and more reliable emergence of advanced skills in Hide and Seek and the maze task. Another candidate intrinsic reward considered, value prediction error, also worked well in Hide and Seek but was susceptible to noisy-TV style distractions in stochastic environments. Policy disagreement performed well in the maze task but did not speed up learning in Hide and Seek. Our results suggest that intrinsic teacher rewards, and in particular value disagreement, are a promising approach for automating both single and multi-agent environment design.

Better-supervised models might have better performance. In this paper, we first clarify what makes for good supervision for a classification problem, and then explain two existing label refining methods, label smoothing and knowledge distillation, in terms of our proposed criterion. To further answer why and how better supervision emerges, we observe the learning path, i.e., the trajectory of the model's predictions during training, for each training sample. We find that the model can spontaneously refine "bad" labels through a "zig-zag" learning path, which occurs on both toy and real datasets. Observing the learning path not only provides a new perspective for understanding knowledge distillation, overfitting, and learning dynamics, but also reveals that the supervisory signal of a teacher network can be very unstable near the best points in training on real tasks. Inspired by this, we propose a new knowledge distillation scheme, Filter-KD, which improves downstream classification performance in various settings.

Virtual worlds will be part of our lives as much as social networks are now,

Artificial intelligence has produced new teaching and learning solutions that are now undergoing testing in different contexts. In addition to its impact on the education sector, AI is substantially altering labour markets, industrial services, agriculture processes, value chains and the organization of workplaces in particular. Technical and vocational education and training (TVET) contributes to sustainable development by fostering employment, decent work and lifelong learning. However, the effectiveness of a TVET system depends on its links and relevance to the labour market. As one of the major drivers of change, there is a need to better understand the impact of AI on labour markets, and consequently on TVET systems.

In this month's project update, I would like to apprise our partners and the general public of our ethnographic study that is currently ongoing in selected schools. Three schools from two districts were selected for our ethnographic study. We classified the schools into categories 1, 2, and 3 depending on the extent of their ICT infrastructure. Our strategy going into the schools was to sit in every class in order to understand how teachers incorporated ICT into their teaching and learning. This was done for two weeks in our category 1 school while we awaited approval from the school management to commence work in category 2 and 3 schools.

Data science is a field that spans many disciplines. It is not merely in control of the digital world. It is used for everything from internet searches to social media feeds to political campaigns, grocery store inventory, airline routes, and medical appointments. A Data Scientist should acquire a complete set of abilities that covers each building block of the discipline in order to have a successful career. Statistics is one of the building blocks.