The'Scientific Programming with C ' is easiest and the most innovative and complete hands-on practical C course on the Udemy Platform for learning scientific and research data programming! While languages like Python and R are increasingly popular for Scientific Programming or Data sciences, C/ C can be a stronger choice for efficient and effective data and scientific computing. The focus of this course lies on learning beginner to advanced programming on high-performance computers, object-oriented software design, generic or template-based programming, and the efficient implementation of numerical algorithms. C is the best choice for efficient and effective programming in Research Data mining & Scientific Computing. Learn from the basics of C to the advanced and useful libraries like STL, BOOST, OpenMP and MPI! Main learning goals in this awesome course can be formulated as: The focus of this course lies on aspects of software development like programming on high-performance computers, object-oriented software design, generic (template-based) programming, and the efficient implementation of numerical algorithms.

Description Do You Want to Gain an Edge by Gleaning Novel Insights from Social Media? Do You Want to Harness the Power of Unstructured Text and Social Media to Predict Trends? Over the past decade there has been an explosion in social media sites and now sites like Facebook and Twitter are used for everything from sharing information to distributing news. Mining unstructured text data and social media is the latest frontier of machine learning and data science. LEARN FROM AN EXPERT DATA SCIENTIST WITH 5 YEARS OF EXPERIENCE: My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate.

In February, Google announced that it was committing to training 100,000 Black women in digital skills. This announcement arrived as a PR Hail Mary amid the ever-growing industry and academic outcry over Google's firing of prominent, brilliant, respected A.I. researcher Timnit Gebru and recruiter April Christina Curley, both Black women and both exceptional contributors at the company. The backlash occurred during a year of widespread protest against the centuries-old violence of racism and racialized capitalism in the United States. This is not the first time that a prominent tech organization has attempted to "train up" Black Americans. From 1968 to 1972, at least 18 programs to provide computing skills training to Black and brown Americans were established in the United States. They were located in East Coast and California cities, with one in St. Louis, Missouri.

Created by Lazy Programmer Inc. Variational autoencoders and GANs have been 2 of the most interesting developments in deep learning and machine learning recently. Yann LeCun, a deep learning pioneer, has said that the most important development in recent years has been adversarial training, referring to GANs. GAN stands for generative adversarial network, where 2 neural networks compete with each other. Unsupervised learning means we're not trying to map input data to targets, we're just trying to learn the structure of that input data. Once we've learned that structure, we can do some pretty cool things.

Artificial Intelligence is one of the fastest growing technologies of the 21st century and accompanies us in our daily lives when interacting with technical applications. However, reliance on such technical systems is crucial for their widespread applicability and acceptance. The societal tools to express reliance are usually formalized by lawful regulations, i.e., standards, norms, accreditations, and certificates. Therefore, the T\"UV AUSTRIA Group in cooperation with the Institute for Machine Learning at the Johannes Kepler University Linz, proposes a certification process and an audit catalog for Machine Learning applications. We are convinced that our approach can serve as the foundation for the certification of applications that use Machine Learning and Deep Learning, the techniques that drive the current revolution in Artificial Intelligence. While certain high-risk areas, such as fully autonomous robots in workspaces shared with humans, are still some time away from certification, we aim to cover low-risk applications with our certification procedure. Our holistic approach attempts to analyze Machine Learning applications from multiple perspectives to evaluate and verify the aspects of secure software development, functional requirements, data quality, data protection, and ethics. Inspired by existing work, we introduce four criticality levels to map the criticality of a Machine Learning application regarding the impact of its decisions on people, environment, and organizations. Currently, the audit catalog can be applied to low-risk applications within the scope of supervised learning as commonly encountered in industry. Guided by field experience, scientific developments, and market demands, the audit catalog will be extended and modified accordingly.

In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader. By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning. This is also a standalone course for learners who have basic machine learning knowledge. This course draws on Andrew Ng's experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the "industry experience" that you might otherwise get only after years of ML work experience.

Abstraction is the cornerstone of modern-day scalable formal verification. Classic papers in formal literature talk about abstraction as a Galois connection, but understanding abstraction when you're new to formal is not easy. We discuss it in detail in our upcoming webinar on 11 Feb 2021 but for now, here is an intuitive and simple explanation of abstraction and refinement.

It was nothing more than what Brad Stevens termed "a curveball," as it turned out. After an initial false positive COVID test, Evan Fournier turned in a string of negative tests, leading to his first-time availability for the Celtics Monday night against New Orleans. "He will play significant minutes, as he will all the rest of the year," Stevens said of how he planned to begin with the talented wing player, acquired from Orlando at the trade deadline for the since-waived Jeff Teague and two second-round draft picks. "We had an obvious need for another wing that can do what he does, and we're fortunate he's with us, and he's on our team," said the Celtics coach. "So I got a chance to go over to the gym (Sunday) while he was shooting around when we got back and then this morning we went through some stuff prior to our shootaround, we shot around as a team for 30 minutes, so he's gotten the crash course in a very short amount of time. He's been there, done that. He's played against us, you know, tons of times, probably knows our plays as well as anybody, and certainly we just want him to play to his strengths and not worry about anything else."

Are you looking for the Best Free Udemy Courses 2021? This Online Courses list contains the Best Udemy Certifications, and Tutorial for you.

Highlight: In the previous post we talked how we can manipulate pixels and images in Python using the OpenCV library. Now, it's time to focus our attention to videos. In this post, you will learn some basic operations which are necessary for building your computer vision applications. First, we will explain how you can load camera frames and video files. Second, you will see how you can read, display and save videos using OpenCV.