Deep learning has demonstrated state-of-the-art performance for a variety of challenging computer vision tasks. On one hand, this has enabled deep visual models to pave the way for a plethora of critical applications like disease prognostics and smart surveillance. On the other, deep learning has also been found vulnerable to adversarial attacks, which calls for new techniques to defend deep models against these attacks. Among the attack algorithms, the black-box schemes are of serious practical concern since they only need publicly available knowledge of the targeted model. We carefully analyze the inherent weakness of deep models in black-box settings where the attacker may develop the attack using a model similar to the targeted model. Based on our analysis, we introduce a novel gradient regularization scheme that encourages the internal representation of a deep model to be orthogonal to another, even if the architectures of the two models are similar. Our unique constraint allows a model to concomitantly endeavour for higher accuracy while maintaining near orthogonal alignment of gradients with respect to a reference model. Detailed empirical study verifies that controlled misalignment of gradients under our orthogonality objective significantly boosts a model's robustness against transferable black-box adversarial attacks. In comparison to regular models, the orthogonal models are significantly more robust to a range of $l_p$ norm bounded perturbations. We verify the effectiveness of our technique on a variety of large-scale models.

Continually learning new skills is important for intelligent systems, yet standard deep learning methods suffer from catastrophic forgetting of the past. Recent works address this with weight regularisation. Functional regularisation, although computationally expensive, is expected to perform better, but rarely does so in practice. In this paper, we fix this issue by using a new functional-regularisation approach that utilises a few memorable past examples crucial to avoid forgetting. By using a Gaussian Process formulation of deep networks, our approach enables training in weight-space while identifying both the memorable past and a functional prior. Our method achieves state-of-the-art performance on standard benchmarks and opens a new direction for life-long learning where regularisation and memory-based methods are naturally combined.

As we rightly know that R is a programming language and software environment used for statistical analysis, data modeling, graphical representation and reporting. IF YOU FIND THIS FREE UDEMY COURSE "Machine Learning With R Programming"USEFUL AND HELPFUL PLEASE GO AHEAD SHARE THE KNOWLEDGE WITH YOUR FRIENDS WHILE THE COURSE IS STILL AVAILABLE

Data Science Specialization is one of the best-known sets of courses offered by Coursera in conjunction with Johns Hopkins University. This specialization covers the concepts and tools you'll need throughout the entire data science pipeline. The Specialization concludes with a Capstone project that allows you to apply the skills you've learned throughout the courses. Coursera John Hopkins Data Science is a ten-course program that covers the data science process from data collection to the production of data science products. It focuses on implementing the data science process in R. Coursera Johns Hopkins data science certification includes 9 courses and a capstone project.

"According to research conducted by Bersin by Deloitte, a typical employee can devote only 24 minutes during a workweek to training. That's why businesses eagerly adopt microlearning: it fits into a tight daily schedule seamlessly and employees can take bite-size courses in an off moment, on any device," says Slava Uskov, VP of Product Development at iSpring. "Many professional authoring tools might be overkill for preparing such courses because of their excessive features, a busy interface, and steep learning curve. The series of quick, bite-sized lessons can be used for new employee onboarding, product and sales training, and also as job aids and video explainers. They are especially effective for training "deskless" and field workers who typically don't have access to desktop computers during their workday.

Google Dataset Search: Similar to how Google Scholar works, Dataset Search lets you find datasets wherever they're hosted, whether it's a publisher's site, a digital library, or an author's web page. It's a phenomenal dataset finder, and it contains over 25 million datasets. Kaggle: A data science site that contains a variety of externally contributed to exciting datasets. You can find all kinds of niche datasets in its master list, from ramen ratings to basketball data to and even Seattle pet licenses. Although the data sets are user-contributed and thus have varying levels of cleanliness, the vast majority are clean.

Long known for its innovations in the development of artificial intelligence applications, Santa Barbara, California-based Intel Corp. is taking its AI …

BENGALURU: Eight in 10 survey respondents say that live online learning programmes have been equally or more effective than physical classroom sessions, with artificial intelligence and machine learning skilling programmes seeing the highest demand, according to a survey by digital skills training provider Simplilearn . The survey was conducted to understand the effects of the Covid-19 pandemic on employee training programmes and productivity post the implementation of work-from-home. The company surveyed executives and managers in the learning and development and HR functions of companies around the globe on issues related to current and future employee training plans. While responses were gathered from various global regions, the majority were gathered from representatives of companies located in India (39%) and the United States (41%). The global health crisis has disrupted work patterns worldwide, and one of the work areas most affected has been employee learning and development.

Transfer learning can be said as a shortcut to solving complex machine learning problems. In simple words, this learning is used to enhance the learning of the model, shorten the time as well as make the learning process quick for the current task. This technique can be applied in computer vision when the model has to learn from images or videos and in NLP techniques. In this article, we list down the top 9 free resources in Transfer Learning one must-read. About: This tutorial is provided by the developers of TensorFlow, where you will learn how to classify images of cats and dogs by using transfer learning from a pre-trained network.

This is another awesome course by Kirill Eremenko and his SuperDataScience Team on how to solve real-world business problems with AI. If you are business people or just curious how AI can help you then you should join this course. The complex topic of Artificial Intelligence and Machine Learning is presented the best it can without getting too technical. I highly recommend to business professionals trying to improve their skillset and help their business use AI. Talking about social proof, this course is trusted by more than 14,000 students and it has on average 4.3 rating which is amazing proof that this is a great course.