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Using Deep Java Library to do Machine Learning on SpringBoot

using deep java library to do machine learning on springboot

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Many AWS customers—startups and large enterprises—are on a path to adopt machine learning and deep learning in their existing applications. The reasons for machine learning adoption are dictated by the pace of innovation in the industry, with business use cases ranging from customer service (including object detection from images and video streams, sentiment analysis) to fraud detection and collaboration.However, until recently, the adoption learning curve was steep and required development of internal technical expertise in new programming languages (e.g., Python) and frameworks, with cascading effect on the whole software development lifecycle, from coding to building, testing, and deployment. The approach outlined in this blog post enables enterprises to leverage existing talent and resources (frameworks, pipelines, and deployments) to integrate machine learning capabilities.IntroductionSpring Boot, one of the most popular and widespread open source frameworks for microservices development, has simplified the implementation of distributed systems.Despite the broad appeal of this framework, there are few options to easily integrate it with Machine Learning (ML). Existing solutions such as stock APIs often do not meet customized application requirements, and developing customized solutions is time-consuming and not cost-effective.Developers approached the integration of machine learning capabilities into existing applications …

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Teachable Machine From Google Makes It Easy To Train And Deploy ML Models

teachable machine from google makes it easy to train and deploy ml models

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Teachable Machine is an experiment from Google to bring a no-code and low-code approach to training AI models. Anyone with a modern browser and webcam can quickly train a model with no prior knowledge or experience with AI.

Teachable Machine

Google

Teachable Machine is not exactly new. It was initially launched in 2017 and got revamped in 2019 with additional capabilities, including saving the model to Google Drive and exporting it to other applications. 
The community behind the project is continuously making it better. It has become so popular that education researcher Blakeley H. Payne and her teammates have been using Teachable Machine as part of an open-source curriculum that teaches middle-schoolers about AI through a hands-on learning experience. Steve Saling of ALS Residence Initiative is using Teachable Machine for improving communication for people with impaired speech. 

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The magic behind Teachable Machine is based on a popular deep learning technique called transfer learning. Most of the neural network architecture of a  fully trained model is retained while replacing a minor part of it based on the data. This approach not only requires less compute power …

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Deep learning enables rapid detection of stroke-causing blockages

deep learning enables rapid detection of stroke-causing blockages

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Examples of patients with large vessel occlusions correctly predicted by the deep-learning model. Top row: CT angiography slices; middle row: maximal intensity projection images; bottom row: heatmaps showing the most discriminative regions, which strongly correlate with occlusion location. (Courtesy: RSNA)
Strokes are life-threatening medical emergencies where urgent treatment is essential. They occur when part of the brain is cut off from its normal blood supply. The most common type of stroke (accounting for almost 85% of all cases) is an ischemic stroke, which is caused by a clot interrupting the supply of blood to the brain. Large vessel occlusion (LVO) strokes occur when such a blockage is found in one of the major arteries of the brain. As LVO strokes are more severe, they require immediate diagnosis and opening of the blocked artery as fast as possible.
In clinical practice, the most common method used to detect LVOs is an imaging modality called CT angiography. This method provides clinicians with a detailed, 3D image of the blood vessels in the patient’s brain. A newer CT technique, multiphase CT angiography, provides more information than its single-phase counterpart through the acquisition of cerebral angiograms in three distinct phases: peak arterial (phase 1), peak …

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Deep-learning-based algorithm helps radiologists detect cerebral aneurysms

deep-learning-based algorithm helps radiologists detect cerebral aneurysms

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Left: CT angiogram showing an aneurysm of 2 mm maximum diameter on the left posterior cerebral artery (arrow). Right: volume-rendered 3D reconstruction image. The aneurysm was missed in the initial report but successfully detected with the deep-learning algorithm. (Courtesy: Radiological Society of North America)

Researchers in China have developed a deep-learning-based algorithm that could help radiologists detect potentially life-threatening cerebral aneurysms on CT angiography images.
Cerebral aneurysms are weak spots in blood vessels in the brain, which can balloon out and fill with blood. If such a bulging aneurysm leaks or ruptures, it can cause serious symptoms and sometimes be fatal. The risk of rupture depends on the size, shape and location of the aneurysm, making detection and characterization of cerebral aneurysms vital.
CT angiography, which uses X-ray CT to visualize blood vessels following injection of contrast into the bloodstream, is usually the first-line imaging exam for detecting cerebral aneurysms. But this can be a challenging task: the complexity of intracranial vessels and the small size of cerebral aneurysms means that some may be missed during an initial assessment.

As such, the researchers propose that deep learning – a type of machine learning that’s increasingly used to develop algorithms for image …

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How Archaeologists Are Using Deep Learning to Dig Deeper

how archaeologists are using deep learning to dig deeper

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Finding the tomb of an ancient king full of golden artifacts, weapons and elaborate clothing seems like any archaeologist’s fantasy. But searching for them, Gino Caspari can tell you, is incredibly tedious.Dr. Caspari, a research archaeologist with the Swiss National Science Foundation, studies the ancient Scythians, a nomadic culture whose horse-riding warriors terrorized the plains of Asia 3,000 years ago. The tombs of Scythian royalty contained much of the fabulous wealth they had looted from their neighbors. From the moment the bodies were interred, these tombs were popular targets for robbers; Dr. Caspari estimates that more than 90 percent of them have been destroyed.He suspects that thousands of tombs are spread across the Eurasian steppes, which extend for millions of square miles. He had spent hours mapping burials using Google Earth images of territory in what is now Russia, Mongolia and Western China’s Xinjiang province. “It’s essentially a stupid task,” Dr. Caspari said. “And that’s not what a well-educated scholar should be doing.”As it turned out, a neighbor of Dr. Caspari’s in the International House, in the Morningside Heights neighborhood of Manhattan, had a solution. The neighbor, Pablo Crespo, at the time a graduate …

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New method brings physics to deep learning to better simulate turbulence

new method brings physics to deep learning to better simulate turbulence

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Deep learning, also called machine learning, reproduces data to model problem scenarios and offer solutions. However, some problems in physics are unknown or cannot be represented in detail mathematically on a computer. Researchers at the University of Illinois Urbana-Champaign developed a new method that brings physics into the machine learning process to make better predictions.
The researchers used turbulence to test their method.
“We don’t know how to mathematically write down all of turbulence in a useful way. There are unknowns that cannot be represented on the computer, so we used a machine learning model to figure out the unknowns. We trained it on both what it sees and the physical governing equations at the same time as a part of the learning process. That’s what makes it magic and it works,” said Willett Professor and Head of the Department of Aerospace Engineering Jonathan Freund.
Freund said the need for this method was pervasive.
“It’s an old problem. People have been struggling to simulate turbulence and to model the unrepresented parts of it for a long time,” Freund said.
Then he and his colleague Justin Sirignano had an epiphany.

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“We learned that if you try to do the machine …

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System brings deep learning to ‘internet of things’ devices: Advance could enable artificial intelligence on household appliances while enhancing data security and energy efficiency

system brings deep learning to ‘internet of things’ devices: advance could enable artificial intelligence on household appliances while enhancing data security and energy efficiency

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Deep learning is everywhere. This branch of artificial intelligence curates your social media and serves your Google search results. Soon, deep learning could also check your vitals or set your thermostat. MIT researchers have developed a system that could bring deep learning neural networks to new — and much smaller — places, like the tiny computer chips in wearable medical devices, household appliances, and the 250 billion other objects that constitute the “internet of things” (IoT).
The system, called MCUNet, designs compact neural networks that deliver unprecedented speed and accuracy for deep learning on IoT devices, despite limited memory and processing power. The technology could facilitate the expansion of the IoT universe while saving energy and improving data security.
The research will be presented at next month’s Conference on Neural Information Processing Systems. The lead author is Ji Lin, a PhD student in Song Han’s lab in MIT’s Department of Electrical Engineering and Computer Science. Co-authors include Han and Yujun Lin of MIT, Wei-Ming Chen of MIT and National University Taiwan, and John Cohn and Chuang Gan of the MIT-IBM Watson AI Lab.
The Internet of Things
The IoT was born in the early 1980s. Grad students at Carnegie Mellon University, …

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Optimizing AI and Deep Learning Performance

optimizing ai and deep learning performance

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(DesignRage/Shutterstock)

As AI and deep learning uses skyrocket, organizations are finding they are running these systems on similar resource as they do with high-performance computing (HPC) systems – and wondering if this is the path to peak efficiency.
Ostensibly AI and HPC architectures have a lot in common, as AI has evolved into even more data-intensive machine learning (ML) and deep learning (DL) domains (Figure 1). Workloads often require multiple GPU systems as a cluster, and share those systems in a coordinated way among multiple data scientists. Secondly, both AI and HPC workloads require shared access to data at a high level of performance and communicate over a fast RDMA-enabled network. Especially in scientific research, the classic HPC systems nowadays tend to have GPUs added to the compute nodes to have the same cluster suitable for classic HPC and new AI/DL workloads.
Yet AI and DL are different from HPC, their applications needs are different, and the deep learning process in particular (Figure 2) has requirements that simply buying more GPU servers won’t fix.
Three Critical Phases in Deep Learning
Phase 1: Data Preparation – Extract, Transform, Load (ETL)
Often data arrives in a format that is not suitable for training – perhaps …

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Deep Learning in CT Scanners Market: Key Players, Growth, Analysis, 2020-2025

deep learning in ct scanners market: key players, growth, analysis, 2020-2025

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The recent report on the Deep Learning in CT Scanners market predicts the industry’s performance for the upcoming years to help stakeholders in making the right decisions that can potentially garner strong returns. Further, the document provides comprehensive analysis of the key industry trends as well as the opportunities that will ensure an upward growth trajectory in the coming years. It also focuses on developing strategies for challenges faced by the industry. Moreover, an exhaustive discussion of the latest updates including the impact of COVID-19 pandemic is furnished in the study.Key highlights from COVID-19 impact analysis:Economic impact of COVID-19 at a global scale.Shifts in supply and demand.Predictions regarding long-term effects of COVID-19 pandemic on the growth trend.An overview of the regional landscape: Geographically, the Deep Learning in CT Scanners market is segmented into North America, Europe, Asia-Pacific, South America, Middle East & Africa, South East Asia.Regional contribution to the overall market growth is measured in the study.Revenue generated, sales garnered, and growth rate attained by each region during forecast period are cited.Request Sample Copy of this Report @ https://www.express-journal.com/request-sample/267208Other important takeaways from the Deep Learning in CT Scanners …

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