At the end of the article, I posted a link to an example portfolio that I liked by Tim Dettmers. Afterward, I had a few people ask me to compile a larger list of great data science portfolios and projects. While not a portfolio, but rather a project, I think this is a great format to try and exemplify. Melissa Runfeldt did a great job defining and motivating her problem, discussing how she gathered data and explaining her methods with images of results. All in a way that would be easy for a non-technical person to follow (at least at a high level).

Light Gradient Boosted Machine, or LightGBM for short, is an efficient and effective implementation of the gradient boosting algorithm. LightGBM extends the gradient boosting algorithm by adding a type of automatic feature selection as well as focusing on boosting examples with larger gradients. This can result in a dramatic speedup of training and improved predictive performance. As such, LightGBM has become a de facto algorithm for machine learning competitions when working with tabular data for regression and classification predictive modeling tasks. As such, it owns a share of the blame for the increased popularity and wider adoption of gradient boosting methods in general, along with Extreme Gradient Boosting (XGBoost).

EASA envisions three stages of AI's rollout in aviation: systems that will assist pilots (2022–2025); human-machine collaboration in flying an aircraft, such as a "virtual" first officer (2025–2030); and autonomous commercial air transport, or, more colloquially, pilotless airliners that fly themselves (2035 and beyond). EASA broadly defines AI as "any technology that appears to emulate the performance of a human." Ultimately, the widespread deployment of AI in aviation comes down to a matter of trust, EASA stated. "A European ethical approach to AI is central to strengthen citizens' trust in the digital development and aims at building a competitive advantage for European companies," according to the EASA roadmap. "Only if AI is developed and used in a way that respects widely shared ethical values can it be considered trustworthy. Therefore, there is a need for ethical guidelines that build on the existing regulatory framework. In June 2018, the [European] Commission set up a High-Level Expert Group on Artificial Intelligence (AI HLEG), the general objective of which was to support the implementation of the European strategy on AI. This includes the elaboration of recommendations on future-related policy development and on ethical, legal and societal issues related to AI, including socio-economic challenges. In April 2019, the AI HLEG proposed the following seven key requirements for trustworthy AI, which were published in its report on Ethics Guidelines on Trustworthy Artificial Intelligence."

BEGIN ARTICLE PREVIEW: By Jessica Kent November 23, 2020 – While the dawn of the EHR promised streamlined, accelerated healthcare delivery, the technology can also include burdensome alerts and documentation requirements that lead to clinician burnout. Providers often spend more time documenting than they do seeing their patients, resulting in poor care experiences and stunted patient-provider relationships. At Providence, one of the largest health systems in the country, leaders were searching for a solution to problems stemming from EHR documentation. “At our organization, clinician burnout and productivity were an issue. The amount of time that clinicians spend on documentation is probably the single biggest issue for our caregivers,” said B.J. Moore, executive vice president and CIO at Providence. “We saw an opportunity to make the documentation process more seamless for our caregivers, to take that burden off their hands and allow

We propose a new regularization method to alleviate over-fitting in deep neural networks. The key idea is utilizing randomly transformed training samples to regularize a set of sub-networks, which are originated by sampling the width of the original network, in the training process. As such, the proposed method introduces self-guided disturbances to the raw gradients of the network and therefore is termed as Gradient Augmentation (GradAug). We demonstrate that GradAug can help the network learn well-generalized and more diverse representations. Moreover, it is easy to implement and can be applied to various structures and applications. GradAug improves ResNet-50 to 78.79% on ImageNet classification, which is a new state-of-the-art accuracy.

It might be possible but super rough if so. M.2 key e PCIe riser cables exist, ARM nvidia drivers exist for that GPU. Your eGPU likely has a thunderbolt or usb-c port so you'd need a card for that... which may not work with the x1 PCIe connection you get (if you're using the standard board IIRC). You could pull the 750 ti out and use a separate power supply for it. I feel like it could work but I wouldn't necessarily trust my judgement on this.

How might The Terminator have played out if Skynet had decided it probably wasn't responsible enough to hold the keys to the entire US nuclear arsenal? As it turns out, scientists may just have saved us from such a future AI-led apocalypse, by creating neural networks that know when they're untrustworthy. These deep learning neural networks are designed to mimic the human brain by weighing up a multitude of factors in balance with each other, spotting patterns in masses of data that humans don't have the capacity to analyse. While Skynet might still be some way off, AI is already making decisions in fields that affect human lives like autonomous driving and medical diagnosis, and that means it's vital that they're as accurate as possible. To help towards this goal, this newly created neural network system can generate its confidence level as well as its predictions.

It is a great honor for me to come under the Top 15% percentile in the Data Science Bootcamp at DPhi. Thank you DPhi, for democratizing the Data Science. It was a wonderful journey! This Bootcamp gave me a chance to connect with Data Science enthusiasts all across the globe, where we learn and grow like a family. The course is planned to suit all the categories of learners from Absolute Beginners to Advanced level.

RPA helps deliver superior customer experience while also simplifying workflows handled by the human workforce. Rudimentary contact center tasks such as updating contact information, listening to routine voicemail messages, sending acknowledgment emails, etc. can easily be automated saving human agents a great amount of time. RPA reduces operational costs while upping efficiency and productivity. Front-office bots like chatbots can integrate with various enterprise systems like CRM, helpdesk etc. An RPA bot can interface with multiple enterprise systems within the company that may have UIs but not APIs.

Have you ever considered the possibility that the faces you see on the internet are not even real! The reviews that you read online, the face which you trust on a product's review, the comments and posts shared by people that you may follow and even idealize may not even be real! Yes, in the growing era of digitalization, which has skyrocketed during this pandemic, most of us would not be even surprised to know that Artificial Intelligence (AI) is busy making fake people. In the world where there is an emphasis on publishing, advertising, buying, making friends get influenced by people they meet virtually, AI is being used to create fake profiles with fake faces and treating them as real human beings. The worst part is that they seem so real, that even the smartest of us cannot easily distinguish between a real human face and the one created by AI.