Last time I promised to cover the graph-guided fused LASSO (GFLASSO) in a subsequent post. In the meantime, I wrote a GFLASSO R tutorial for DataCamp that you can freely access here, so give it a try! The plan here is to experiment with convolutional neural networks (CNNs), a form of deep learning. CNNs underlie most advanced recognition algorithms used by the major tech giants. The recent development of back-end optimization tools and hardware (from Intel, NVIDIA and Google to name a few) now enables training CNNs on conventional laptop machines, hence accessible to a broader audience. Today you will construct a binary classifier that can distinguish between dogs and cats from a set of 25,000 pictures, using the Keras R interface powered by the TensorFlow back-end engine.

As soon as abstract mathematical computations were adapted to computation on digital computers, the problem of efficient representation, manipulation, and communication of the numerical values in those computations arose. Strongly related to the problem of numerical representation is the problem of quantization: in what manner should a set of continuous real-valued numbers be distributed over a fixed discrete set of numbers to minimize the number of bits required and also to maximize the accuracy of the attendant computations? This perennial problem of quantization is particularly relevant whenever memory and/or computational resources are severely restricted, and it has come to the forefront in recent years due to the remarkable performance of Neural Network models in computer vision, natural language processing, and related areas. Moving from floating-point representations to low-precision fixed integer values represented in four bits or less holds the potential to reduce the memory footprint and latency by a factor of 16x; and, in fact, reductions of 4x to 8x are often realized in practice in these applications. Thus, it is not surprising that quantization has emerged recently as an important and very active sub-area of research in the efficient implementation of computations associated with Neural Networks.

Comparing the visual quality of generated frames. From Diverse Generation from a Single Video Made Possible. Reproduced under a CC BY 4.0 license. Here are the most tweeted papers that were uploaded onto arXiv during September 2021. Results are powered by Arxiv Sanity Preserver. Abstract: Generative adversary network (GAN) generated high-realistic human faces have been used as profile images for fake social media accounts and are visually challenging to discern from real ones.

Computer vision is a field of study which enables computers to replicate the human visual system. It's a subset of artificial intelligence which collects information from digital images or videos and processes them to define the attributes. The entire process involves image acquiring, screening, analysing, identifying and extracting information. This extensive processing helps computers to understand any visual content and act on it accordingly. Computer vision projects translate digital visual content into explicit descriptions to gather multi-dimensional data.

Computer scientists at DeepMind and the University of Exeter in England teamed up with meteorologists from the Met Office to build an AI model capable of predicting whether it will rain up to 90 minutes beforehand. Traditional forecasting methods rely on solving complex equations that take into account various weather conditions, such as air pressure, moisture, and the temperature of Earth's atmosphere. The trouble is, at least in Blighty, these systems tend to predict what lies in store for us whole days or weeks ahead. Deep-learning models are better suited for making more near-term forecasts – such as within the next couple of hours – according to a paper published by the aforementioned boffins in Nature on Wednesday. There are advantages to using AI algorithms; they don't have to solve thermodynamic equations and are less computationally intensive than other predictive techniques.

The Golden Jubilee National Hospital in Clydebank, Scotland, has updated its cardiac CT scanning equipment. The Aquilion One/Prism Edition CT scanner introduces next-generation reconstruction technology to assist with high cardiac imaging case loads. "We are delighted with the new CT scanner," said CT superintendent radiographer Julie Morrison. "It is easy to operate and our radiologists have been delighted with the fantastic quality of cardiac images at low dose to support our heart and lung patient services. The wide area detector will enable us to obtain an entire heart in a fraction of a second and the AI technology further enhances the ability to gain low dose, high quality images at speed. We feel ready for the future."

This whitepaper looks at the events and market data of 2020 and discusses the possible takeaways for the risk professional in planning for future economic crises.

Such analysis is used in many different industries like Industrial Safety, Industrial automation, Healthcare verticals like Pathology and Radiology, Human Safety use cases like PPE compliance tracking, Human gesture analysis for ergonomics safety, and Human-Machine Interaction (HMI). Chatbots can be trained to interpret and answer a wide range of questions for enhancing data accuracy, domain-specific and near human-like cognition. This is Google's cutting-edge platform for vision intelligence. We have extensive knowledge in using Mediapipe train and deploy machine learning models in Industrial Safety, Ergonomics assessment, Sign-language recognition, Gesture recognition, etc., DeepStream is Nvidia's platform to build and deploy AI-powered Intelligent Video Analytics apps and services. DeepStream offers a multi-platform scalable framework with TLS security to deploy on the edge and connect to any cloud.

Digital Art created via Convolutional Neural Networks and Deep Learning.

DeepMind and UK's Met Office use AI to improve weather forecasts