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Machine Learning

Most Common Data Science Interview Questions and Answers - KDnuggets


Becoming a data scientist is considered a prestigious trait. Back in 2012, Harvard Business Review called'data scientist' the sexiest job of the 21st century, and the growing trend of roles in the industry seems to be confirming that statement. To confirm this sexiness is still ongoing, the info from Glassdoor shows being a data scientist is the second-best job in America in 2021. To get such a prestigious job, you have to go through rigorous job interviews. Data science questions asked can be very broad and complex. This is expected, considering the role of a data scientist usually incorporates so many areas.

GitHub - openvinotoolkit/openvino_notebooks: 📚 A collection of Jupyter notebooks for learning and experimenting with OpenVINO 👓


The notebooks provide an introduction to OpenVINO basics and teach developers how to leverage our API for optimized deep learning inference. Notebooks with a button can be run without installing anything. Binder is a free online service with limited resources. For the best performance, please follow the Installation Guide and run the notebooks locally. Brief tutorials that demonstrate how to use OpenVINO's Python API for inference.

Where AI does and doesn't pay off in enterprise computing


Gartner predicts that AI is still in its two- to five-year hype cycle. So, we are not yet at the point where robots are going to take over the world. However, computer scientist Andrew Ng boldly calls "AI as the new electricity." Every industry -- education, retail, manufacturing, energy, health care, technology -- all have great opportunities that will transform their landscape. With that, I would not call out one specific use case as having the most significant potential, but instead, recommend a focus on an "Augmented Intelligence."

Here's why a great gaming laptop is the best all-around computer for college


If you're tackling a degree in science, technology, engineering, or mathematics, there's nothing more frustrating than a machine that can't keep up with the apps you need for your coursework. Here's where a powerful gaming laptop proves its mettle. With GPU acceleration, your machine delivers super-fast image processing, real-time rendering for complex component designs, and it lets you work quickly and efficiently. For engineering students, this means more interactive, real-time rendering for 3D design and modeling, plus faster solutions and visualization for mechanical, structural, and electrical simulations. For computer science, data science, and economics students, NVIDIA's GeForce RTX 30 Series laptops enable faster data analytics for processing large data sets -- all with efficient training for deep learning and traditional machine learning models for computer vision, natural language processing, and tabular data.

The Future of Artificial Intelligence in Weather Forecasting


Today's weather forecasts are generated by some of the world's most sophisticated computers. As you may know, weather forecasts are very unpredictable. This is because the climate is a very complex and volatile phenomenon that requires a great amount of money, data, and time to evaluate. The future may follow a very different path regarding weather forecasting: and that future is A.I. Weather forecasting has been done in the same way for a few decades: supercomputers process massive volumes of atmospheric and oceanic data. Forecasting companies aggregate data from weather stations and integrate it with data from a variety of different sources, such as ocean buoys and independent weather trackers.

NSF Adds 11 New AI Research Institutes to Its Collaborative, Nationwide Network


The National Science Foundation officially extended the reach of its National Artificial Intelligence Research Institutes across more of the United States. On the heels of funding seven institutes in 2020, the agency last week unveiled its establishment of 11 new ones--where officials will strategically pursue AI research in complex realms like augmented learning, cybersecurity, precision agriculture and more. "The expertise of the researchers engaged in the AI Research Institutes spans a wide range of disciplines, providing an integrated effort to tackle the challenges society faces, drawing upon both foundational and use-inspired research," Director of NSF's Robust Intelligence Program Rebecca Hwa told Nextgov Tuesday. "NSF has long been able to bring together numerous fields of scientific inquiry, and in this program that includes such disciplines as computer and information science and engineering, cognitive science and psychology, economics and game theory, engineering and control theory, ethics, linguistics, mathematics, and philosophy--and that has positioned us to lead in efforts to expand the frontiers of AI." In all, the 18 institutes NSF is investing in so far underpin research spanning 40 U.S. states and the District of Columbia, Hwa confirmed.

Machine learning is demonstrating its mettle across industries


Artificial intelligence and machine learning are the most disruptive technologies, according to IT professionals in the 2020 CIO Tech Priorities Poll. Respondents say these solutions -- more so than cloud, IoT, and analytics -- have the potential to significantly alter the way businesses and entire industries operate. But where is machine learning having the most impact? That's the question we posed to the IDG Influencer Network, a community of industry analysts, IT professionals, and journalists who contribute their knowledge and expertise to the broader IDG community. Here are some key takeaways from their responses.

Top MLOps Books In 2021


Machine learning is getting mainstreamed as many organisations have integrated or are trying to integrate ML systems into their products and platforms. MLOps is the branch of ML that unifies ML systems development (dev) and ML systems deployments (ops). We have curated a list of top MLOps books to help you get a handle on the subject (in no particular order). The Machine Learning Engineering book is one of the most complete applied AI books out there and is filled with best practices and design patterns of building reliable machine learning solutions at scale. Andriy Burkov has a PhD in AI and is currently the machine learning team leader at Gartner.

Deep Doc


Our goal is to provide quality news content regarding machine learning in medicine for you in this and coming versions. Dataset shift is one of the main challenges for AI model generalization. For example, in a clinical setting, training data may differ from the data used by the model to provide diagnostic, prognostic, or treatment advice. Finlayson et al. have published letters in the New England Journal of Medicine outlining how to identify and potentially mitigate familiar sources of dataset shift in machine learning systems. Casto et al. have considered causal reasoning to tackle different types of shifts in medical imaging.

A Distinctive Introduction to Artificial Intelligence, Machine Learning, and Deep Learning


Artificial Intelligence, also known as AI, Machine Learning, and Deep Learning, is generating a lot of attention across the world. Despite all of the hype, this is going to be a huge revolution in the coming years. The world's most successful firms are pouring money into research in these domains to see what else they can get out of AI. The computational power we have now is the reason why AI is so popular right now. We've witnessed the change in processors as well.