The course covers Machine Learning in exhaustive way. The presentations and hands-on practical are made such that it's made easy. The knowledge gained through this tutorial series can be applied to various real world scenarios. UnSupervised learning does not require to supervise the model. Instead, it allows the model to work on its own to discover patterns and information that was previously undetected. It mainly deals with the unlabeled data.

Do you want to learn Artificial Intelligence and looking for the best resources to learn Artificial Intelligence?… If yes, you are in the right place. In this article, I have listed all the best resources to learn Artificial Intelligence including Online Courses, Tutorials, Books, and YouTube Videos. So, give your few minutes and find out the best resources to learn Artificial Intelligence. You can bookmark this article so that you can refer to this article later.

As she was looking for a camp last summer, Yabesra Ewnetu, who'd just finished eighth grade, found a reference to MIT's FutureMakers Create-a-thon. Ewnetu had heard that it's hard to detect bias in artificial intelligence because AI algorithms are so complex, but this didn't make sense to her. "I was like, well, we're the ones coding it, shouldn't we be able to see what it's doing and explain why?" She signed up for the six-week virtual FutureMakers program so she could delve into AI herself. FutureMakers is part of the MIT-wide Responsible AI for Social Empowerment and Education (RAISE) initiative launched earlier this year. RAISE is headquartered in the MIT Media Lab and run in collaboration with MIT Schwarzman College of Computing and MIT Open Learning.

The course instructor is a lecturer teaching Computer Organization and Computer Architecture subjects for 3 years. Why should you consider enrolling on this Computer Architecture & Computer Organization Masterclass?

Electrification was, without a doubt, the greatest engineering marvel of the 20th century. The electric motor was invented way back in 1821, and the electrical circuit was mathematically analyzed in 1827. But factory electrification, household electrification, and railway electrification all started slowly several decades later. The field of AI was formally founded in 1956. But it's only now--more than six decades later--that AI is expected to revolutionize the way humanity will live and work in the coming decades.

This course is about artificial neural networks. Artificial intelligence and machine learning are getting more and more popular nowadays. In the beginning, other techniques such as Support Vector Machines outperformed neural networks, but in the 21st century neural networks again gain popularity. In spite of the slow training procedure, neural networks can be very powerful. In the first part of the course you will learn about the theoretical background of neural networks, later you will learn how to implement them in Python from scratch.

In this tutorial, we will configure and deploy Nvidia Triton Inference Server on the Jetson Mate carrier board to perform inference of computer vision models. It builds on our previous post where I introduced Jetson Mate from Seeed Studio to run the Kubernetes cluster at the edge. Though this tutorial focuses on Jetson Mate, you can use one or more Jetson Nano Developer Kits connected to a network switch to run the Kubernetes cluster. Assuming you have installed and configured JetPack 4.6.x on all the four Jetson Nano 4GB modules, let's start with the installation of K3s. The first step is to turn Nvidia Container Toolkit into the default runtime for Docker.

Abstract: Dimension reduction is an important tool for analyzing high-dimensional data. The predictor envelope is a method of dimension reduction for regression that assumes certain linear combinations of the predictors are immaterial to the regression. The method can result in substantial gains in estimation efficiency and prediction accuracy over traditional maximum likelihood and least squares estimates. While predictor envelopes have been developed and studied for independent data, no work has been done adapting predictor envelopes to spatial data. In this work, the predictor envelope is adapted to a popular spatial model to form the spatial predictor envelope (SPE).

As robots evolve, society's collective imagination forever ponders what else robots can do, with recent fascinations coming to life as self-driving cars or robots that can walk and interact with objects as humans do. These sophisticated systems are powered by advances in deep learning that triggered breakthroughs in robotic perception, so that robots today have greater potential for better decision-making and improved functioning in real-world environments. But tomorrow's roboticists need to understand how to combine deep learning with dynamics, controls, and long-term planning. To keep this momentum in robotic manipulation going forward, engineers today must learn to hover above the whole field, connecting an increasingly diverse set of ideas with an interdisciplinary focus needed to design increasingly complex robotic systems. Last fall, MIT's Department of Electrical Engineering and Computer Science launched a new course, 6.800 (Robotic Manipulation) to help engineering students broadly survey the latest advancements in robotics while troubleshooting real industry problems.

Also explains sequence and string functions, slicing, concatenating, iterating, sorting, etc. with code examples. This course combines conceptual lectures to explain how a data structure works, and code lectures that walk through how to implement a data structure in Python code. All the code lectures are based on Python 3 code in a Jupyter notebook. Data structures covered in this course include native Python data structures String, List, Tuple, Set, and Dictionary, as well as Stacks, Queues, Heaps, Linked Lists, Binary Search Trees, and Graphs. The list data type has some more methods.