Charlottesville, VA, USA, March 31, 2021--Unbound Medicine, a leader in knowledge management solutions for healthcare, today announced a major upgrade to their end-to-end digital publishing platform. To enhance clinical decision support capabilities for professional societies and healthcare institutions, Unbound developed Unbound Intelligence (UBI)‒exclusive artificial intelligence and machine learning tools to help clinicians keep up to date with current research, as well as discover and fill knowledge gaps. Unbound Intelligence quickly analyzes large volumes of data and recommends options for next steps in patient management. While clinicians answer questions or research areas of interest on the Unbound Platform, UBI instantly filters through available resources, including the most up-to-date primary literature, to suggest closely related topics and relevant, recently published journal articles. This allows clinicians to quickly expand their reach and discover evidence-based guidance that may have otherwise gone unnoticed.

Each team of students will be led by a teacher, who will receive paid training on video game development. Students in the program will select a topic for their game based on a social issue and then work synchronously with their international counterparts online, as well as by themselves offline, to develop and create a game, all within 10 weeks. The students will learn to code as well; with some older students using game creation engines such as Unity.

Technology plays a significant role in education nowadays, and it's a hot topic. Others claim artificial intelligence will revolutionize education and improve education, while others claim artificial intelligence will take over teaching at students' and teachers' expense. Artificial Intelligence (AI) is slowly making its way into education, although we haven't seen robots in the classroom yet. Specific tasks can be rendered easier with artificial intelligence. Shortly, AI will be used to make grading relatively quick and easy on computer equipment.

You're looking for a complete Machine Learning course that can help you launch a flourishing career in the field of Data Science & Machine Learning, right? You've found the right Machine Learning course! Check out the table of contents below to see what all Machine Learning models you are going to learn. How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.

Are you looking to land a top-paying job in Data Science? Or are you a seasoned AI practitioner who want to take your career to the next level? Or are you an aspiring data scientist who wants to get Hands-on Data Science and Artificial Intelligence? If the answer is yes to any of these questions, then this course is for you! Data Science is one of the hottest tech fields to be in right now! The field is exploding with opportunities and career prospects.

This episode is about understanding why you can't build your startup alone, and some criteria to properly select your co-founders. In this podcast series of episodes we are going to explain how to create a robotics startup step by step. We are going to learn how to select your co-founders, your team, how to look for investors, how to test your ideas, how to get customers, how to reach your market, how to build your product… Starting from zero, how to build a successful robotics startup. I'm Ricardo Tellez, CEO and co-founder of The Construct startup, a robotics startup at which we deliver the best learning experience to become a ROS Developer, that is, to learn how to program robots with ROS. Our company is already 5 years long, we are a team of 10 people working around the world.

This is a list that I compiled over the years, it contains everything I found to be useful or interesting. There is no special categorization, it flows a bit to JS side but there little bit of everything. Please feel free to comment and add your favorite repos. Rough.js is a small ( 9 kB) graphics library that lets you draw in a sketchy, hand-drawn-like, style. The library defines primitives to draw lines, curves, arcs, polygons, circles, and ellipses.

QAT refers to the quantization of activations to binary, bipolar, or multi-bit values during neural network training. On the other hand, if deep neural networks (DNNs) [1]-[31], ultra-low-latency realization a set of values can only assume non-negative numbers, it relies on of these models for applications with stringent, sub-microsecond the parameterized clipping activation (PACT) [9] function to quantize latency requirements continues to be an unresolved, challenging activations. Field-programmable gate array (FPGA)-based DNN accelerators FCP applies fanin constraints to individual filters/neurons such that are gaining traction as a serious contender to replace graphics the number of inputs to each filter/neuron is small enough to make processing unit/central processing unit-based platforms considering a realization based on input enumeration (as described in NullaNet their performance, flexibility, and energy efficiency. In this work, FCP is either based on the alternating [32], LUTNet (2019) [33], and LogicNets (2020) [34] are among accelerators direction method of multipliers [35] or gradual pruning [11]. Finally, functions of different filters/neurons are represented using This paper presents NullaNet Tiny, an across-the-stack design truth tables which are then fed to the logic minimization module.

3D delineation of anatomical structures is a cardinal goal in medical imaging analysis. Prior to deep learning, statistical shape models that imposed anatomical constraints and produced high quality surfaces were a core technology. Prior to deep learning, statistical shape models that imposed anatomical constraints and produced high quality surfaces were a core technology. Today fully-convolutional networks (FCNs), while dominant, do not offer these capabilities. We present deep implicit statistical shape models (DISSMs), a new approach to delineation that marries the representation power of convolutional neural networks (CNNs) with the robustness of SSMs. DISSMs use a deep implicit surface representation to produce a compact and descriptive shape latent space that permits statistical models of anatomical variance. To reliably fit anatomically plausible shapes to an image, we introduce a novel rigid and non-rigid pose estimation pipeline that is modelled as a Markov decision process(MDP). We outline a training regime that includes inverted episodic training and a deep realization of marginal space learning (MSL). Intra-dataset experiments on the task of pathological liver segmentation demonstrate that DISSMs can perform more robustly than three leading FCN models, including nnU-Net: reducing the mean Hausdorff distance (HD) by 7.7-14.3mm and improving the worst case Dice-Sorensen coefficient (DSC) by 1.2-2.3%. More critically, cross-dataset experiments on a dataset directly reflecting clinical deployment scenarios demonstrate that DISSMs improve the mean DSC and HD by 3.5-5.9% and 12.3-24.5mm, respectively, and the worst-case DSC by 5.4-7.3%. These improvements are over and above any benefits from representing delineations with high-quality surface.

Despite the substantial progress of active learning for image recognition, there still lacks an instance-level active learning method specified for object detection. In this paper, we propose Multiple Instance Active Object Detection (MI-AOD), to select the most informative images for detector training by observing instance-level uncertainty. MI-AOD defines an instance uncertainty learning module, which leverages the discrepancy of two adversarial instance classifiers trained on the labeled set to predict instance uncertainty of the unlabeled set. MI-AOD treats unlabeled images as instance bags and feature anchors in images as instances, and estimates the image uncertainty by re-weighting instances in a multiple instance learning (MIL) fashion. Iterative instance uncertainty learning and re-weighting facilitate suppressing noisy instances, toward bridging the gap between instance uncertainty and image-level uncertainty. Experiments validate that MI-AOD sets a solid baseline for instance-level active learning. On commonly used object detection datasets, MI-AOD outperforms state-of-the-art methods with significant margins, particularly when the labeled sets are small. Code is available at https://github.com/yuantn/MI-AOD.