The education sector was among the hardest hit by the COVID-19 pandemic. Schools across the globe were forced to shutter their campuses in the spring of 2020 and rapidly shift to online instruction. For many higher education institutions, this meant delivering standard courses and the "traditional" classroom experience through videoconferencing and various connectivity tools. The approach worked to support students through a period of acute crisis but stands in contrast to the offerings of online education pioneers. These institutions use AI and advanced analytics to provide personalized learning and on-demand student support, and to accommodate student preferences for varying digital formats.

Putting the fun in fundamentals! A collection of short videos to amuse beginners and experts alike.

My commute to work every day is roughly one hour ( /- 15 minutes depending on the day). It's safe to say I cruise through A LOT of podcasts. The subjects I listen to range from True Crime, NFL Fantasy Football, Major League Baseball, and Data Science. This is my personal ranking/list of the best data science podcasts on SoundCloud, Apple Podcast, and Spotify. I found the descriptions of each podcast to be pretty true to what I would have written myself, which is why you won't see a whole lot of my own writing in the descriptions (why reinvent the wheel?).

The graph represents a network of 1,248 Twitter users whose tweets in the requested range contained "iiot bigdata", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 16 September 2022 at 11:01 UTC. The requested start date was Friday, 16 September 2022 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 1-day, 21-hour, 52-minute period from Wednesday, 14 September 2022 at 02:07 UTC to Friday, 16 September 2022 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

In this paper, we research the new topic of object effects recommendation in micro-video platforms, which is a challenging but important task for many practical applications such as advertisement insertion. To avoid the problem of introducing background bias caused by directly learning video content from image frames, we propose to utilize the meaningful body language hidden in 3D human pose for recommendation. To this end, in this work, a novel human pose driven object effects recommendation network termed PoseRec is introduced. PoseRec leverages the advantages of 3D human pose detection and learns information from multi-frame 3D human pose for video-item registration, resulting in high quality object effects recommendation performance. Moreover, to solve the inherent ambiguity and sparsity issues that exist in object effects recommendation, we further propose a novel item-aware implicit prototype learning module and a novel pose-aware transductive hard-negative mining module to better learn pose-item relationships. What's more, to benchmark methods for the new research topic, we build a new dataset for object effects recommendation named Pose-OBE. Extensive experiments on Pose-OBE demonstrate that our method can achieve superior performance than strong baselines.

Predictions and forecasts of machine learning models should take the form of probability distributions, aiming to increase the quantity of information communicated to end users. Although applications of probabilistic prediction and forecasting with machine learning models in academia and industry are becoming more frequent, related concepts and methods have not been formalized and structured under a holistic view of the entire field. Here, we review the topic of predictive uncertainty estimation with machine learning algorithms, as well as the related metrics (consistent scoring functions and proper scoring rules) for assessing probabilistic predictions. The review covers a time period spanning from the introduction of early statistical (linear regression and time series models, based on Bayesian statistics or quantile regression) to recent machine learning algorithms (including generalized additive models for location, scale and shape, random forests, boosting and deep learning algorithms) that are more flexible by nature. The review of the progress in the field, expedites our understanding on how to develop new algorithms tailored to users' needs, since the latest advancements are based on some fundamental concepts applied to more complex algorithms. We conclude by classifying the material and discussing challenges that are becoming a hot topic of research.

Andrew Walker and Dinkar Juyal are Machine Learning Engineers at PathAI.

Follow along with me as I learn artificial intelligence (ai) for free from the fast.ai Fast.ai provides a great website to start out and learn if you only have a year of coding experience. Go to the homepage and then just click on practical deep learning for coders. At the bottom of the page the forums are linked and it says " wonderful online community" so click on that for one of the key areas you will learn from. Scroll down or just hit the search icon and type in Live Coding and you will find a post listing all 18 forum posts and videos.

NLP for audio data is not getting enough recognition, compared to NLP for text and computer vision tasks. Emotion recognition -- recognize whether spoken audio exhibits anger, happiness, sadness, disgust, surprise, or neutral emotions. Note: Once we are through with the tutorial, you should be able to reuse the code for any audio classification task. For this tutorial, we will use the publicly available Crema-D dataset on Kaggle. So go ahead and click the Download button on this link.

You're looking for a complete Convolutional Neural Network (CNN) course that teaches you everything you need to create a Image Recognition model in Python, right? You've found the right Convolutional Neural Networks course! How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Convolutional Neural networks course. If you are an Analyst or an ML scientist, or a student who wants to learn and apply Deep learning in Real world image recognition problems, this course will give you a solid base for that by teaching you some of the most advanced concepts of Deep Learning and their implementation in Python without getting too Mathematical.