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Demystifying the Future Of Self-Supervised Deep Learning

demystifying the future of self-supervised deep learning

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Self-supervised Learning is the solution for the limitations of Deep learning like requirement of large amount of data for huge computation process.
Over the years the integration of AI technology in day to day life has rendered humans to function feasibly. Applications such as Chatbots, Virtual Assistance, online translators are heavily influenced by the concept of Deep Learning. Amazon’s Alexa, Apple’s Siri, Google’s assistant are some of the examples heavily governed by deep learning.
However, despite its everyday use, Deep Learning tend to have shortcomings, which have been discussed by experts over the years.

What is Deep Learning?
Deep Learning is a branch of machine learning where artificial neural networks in the form of algorithms and inspired by human brain learn from large amount of data that requires high computation process.
The Neural Network algorithm , is a network of functions that understands and translates the input data of one form into that of desired output. This works in the form of neuron of the brain.
Though, Deep Learning does not require human assistance, but it does require strong computing processes that requires large amount of data, that has been viewed as one of the limitations by experts.

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Deep Learning Software Market including top key players Artelnics, Bright Computing, BAIR

deep learning software market including top key players artelnics, bright computing, bair

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JCMR recently announced market survey which covers overall in-depth study including additional study on COVID-19 impacted market situation on Global Deep Learning Software Market. The Research Article Entitled Global Deep Learning Software Market provides very useful reviews & strategic assessment including the generic market trends, upcoming & innovative technologies, industry drivers, challenges, regulatory policies that propel this Universal market place, and major players profile and strategies. The research study provides forecasts for Deep Learning Software investments till 2029.

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Following Key Segments covers in the Global Deep Learning Software Market

Deep Learning Software Segment
Details

Market Analysis By Companies
Artelnics, Bright Computing, BAIR, Intel, Cognex, IBM, Keras, Microsoft, VLFeat, NIVIDA, PaddlePaddle, Torch, SignalBox, Wolfram ,

Market Analysis By Type
Cloud based, On premise,

Market Analysis By Applications  
Large Enterprise, SMB,

Market Analysis By Regions along with their respective countries        
North America, Europe, China, Japan, Rest of the World

Geographically, this report is segmented into several key Regions along with their respective countries, with production, consumption, revenue (million USD), and market share and growth rate of Deep Learning Software in these regions, from 2012 to 2029 (forecast), covering
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New approach for earlier detection of Alzheimer’s

new approach for earlier detection of alzheimer’s

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IMAGE: Won Hwa Kim
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Credit: UT Arlington

Won Hwa Kim, an assistant professor of computer science at The University of Texas at Arlington, is using a two-year, $175,000 grant from the National Science Foundation to use machine learning for earlier detection of Alzheimer’s disease.
In previous studies, researchers have attempted to use brain scans to find which regions of the brain might be related to the disease. Kim’s study will develop a novel deep learning technique that uses algorithms that mimic the structure and function of neural networks in the brain.
It is difficult to apply conventional deep learning techniques to brain network analysis. Kim hopes to create a deep learning pipeline that can help him design a new convolution neural network for graph classification. The pipeline must be capable of making accurate predictions based on small amounts of data, because dataset size is often limited in the neuroimaging field.
Convolution neural networks have been used in image recognition and classification, but not for graph data. Kim’s new convolution will explore the spatial relationship between graph nodes in a dual space to learn topological features for brain networks.
“By developing a novel convolution neural network, we will be able …

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How deep learning can advance study of neural degeneration

how deep learning can advance study of neural degeneration

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Researchers from North Carolina State University have demonstrated the utility of artificial intelligence (AI) in identifying and categorizing neural degeneration in the model organism C. elegans. The tool uses deep learning, a form of AI, and should facilitate and expedite research into neural degeneration.
“Researchers want to study the mechanisms that drive neural degeneration, with the long-term goal of finding ways to slow or prevent the degeneration associated with age or disease,” says Adriana San Miguel, corresponding author of a paper on the work and an assistant professor of chemical and biomolecular engineering at NC State. “Our work here shows that deep learning can accurately identify physical symptoms of neural degeneration; can do it more quickly than humans; and can distinguish between neural degeneration caused by different factors.
“Having tools that allow us to identify these patterns of neural degeneration will help us determine the role that different genes play in these processes,” San Miguel says. “It will also help us evaluate the effect of various pharmaceutical interventions on neural degeneration in the model organism. This is one way we can identify promising candidates for therapeutic drugs to address neurological disorders.”
For this study, researchers focused on C. elegans, or …

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Book Review: Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning

book review: artificial intelligence engines: a tutorial introduction to the mathematics of deep learning

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We’re seeing a rising number of new books on the mathematics of data science, machine learning, AI and deep learning, which I view as a very positive trend because of the importance for data scientists to understand the theoretical foundations for these technologies. In the coming months, I plan to review a number of these titles, but for now, I’d like to introduce a real gem: “Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning,” by James V. Stone, 2019 Sebtel Press. Dr. Stone is an Honorary Reader in Vision and Computational Neuroscience at the University of Sheffield, England.

The author provides a GitHub repo containing Python code examples based on the topics found in the book. You can also download Chapter 1 for free HERE.

The main reason why I like this book so much is because of its tutorial format. It’s not a formal text on the subject matter, but rather a relatively short and succinct (only 200 pages) guide book for understanding the mathematical fundamentals of deep learning. I was able to skim it’s content in about 2 hours, and a thorough reading could be achieved in a few days depending on your math background. …

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Trends Of Deep Learning Chipsets Market Reviewed For 2020 With Industry Outlook To 2026 – The Market Records

trends of deep learning chipsets market reviewed for 2020 with industry outlook to 2026 – the market records

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A new research study has been presented by Dataintelo.com offering a comprehensive analysis on the Global Deep Learning Chipsets Market where user can benefit from the complete market research report with all the required useful information about this market. This is a latest report, covering the current COVID-19 impact on the market. The pandemic of Coronavirus (COVID-19) has affected every aspect of life globally. This has brought along several changes in market conditions. The rapidly changing market scenario and initial and future assessment of the impact is covered in the report. The report discusses all major market aspects with expert opinion on current market status along with historic data. This market report is a detailed study on the growth, investment opportunities, market statistics, growing competition analysis, major key players, industry facts, important figures, sales, prices, revenues, gross margins, market shares, business strategies, top regions, demand, and developments.
The Deep Learning Chipsets Market report provides a detailed analysis of the global market size, regional and country-level market size, segment growth, market share, competitive landscape, sales analysis, impact of domestic and global market players, value chain optimization, trade regulations, recent developments, opportunity analysis, strategic market growth analysis, product launches, and technological …

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Physna Launches Thangs: Google + GitHub For The World Of 3D Models, Using Deep Learning | MarkTechPost

physna launches thangs: google + github for the world of 3d models, using deep learning | marktechpost

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Image by PIRO4D from Pixabay

Physna, an industry leader in ‘Geometric Deep Learning’ technology, has recently launched a geometric search engine named Thangs. Physna is calling Thangs the 3D world’s Google x GitHub crossover, i.e., it is supposed to be that powerful platform for 3D models. 

Thangs uses Deep Learning search algorithms that focus on the polygons or triangles, making up the 3D model’s volumes to be indexed. Also, ‘version control functionality’ and ‘compatible part predictions’ are plus points for the 3D community. Thangs 3D model search engine launches with more than a million searchable objects and aims to be a ‘3D Google’.

Engineers, industrial designers, and 3D-printing enthusiasts face major challenges of working with 3D data. As it is easy to search for 2D data like text or image in Google, but dealing with 3D data needs another platform, which Thangs provides.

Thangs is the first open product of Physna, and it is believed that the industry is trying to democratize some of the technologies at the core of its enterprise products. It is easy-to-use for a layman, and powerful enough for a leading aerospace CAD engineer, as claimed by Paul Powers, CEO of Physna.

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Causal Machine Learning Represents Next Evolution of AI (And ESG & Innovation Issues) – WatersTechnology.com

causal machine learning represents next evolution of ai (and esg & innovation issues) – waterstechnology.com

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The inaugural WatersTechnology Innovation Exchange is behind us. Lots more ESG, machine learning, and innovation strategy material from the last week to discuss, but be sure to check out some of our other coverage of low-code applications, zero-trust architectures, and the Open Digital Rights Language (ODRL)—these just might be the hot topics at Innovation Exchanges of the future.

Cause & Effect

The Covid era is a proving ground. For example, alternative data providers have been espousing the wonders of their unique datasets, but if you can’t prove that your datasets are valuable in these volatile markets, the oxygen in the room is going to escape real quick.

Now stretching into its ninth month here in the US, the pandemic has also turned up the heat on machine-learning models that have historically relied on correlations between different types of datasets. Some very interesting work underway by IBM and Refinitiv could help brace these models for the future.

To address correlational problems that challenged firms even before the pandemic, Refinitiv Labs and MIT–IBM Watson AI Lab have been working together on an emerging field of machine learning study called causal inference.

“Rather than trying to predict something based on correlations, …

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How Deep Learning Is Used For Tuberculosis Detection In City Of Nagpur

how deep learning is used for tuberculosis detection in city of nagpur

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Tuberculosis or TB has remained one of the world’s most infectious diseases, responsible for more fatalities than HIV and malaria combined. Across the globe, TB has reached epidemic proportions affecting more than 27 lakh people annually in India alone. The shortage of healthcare specialists exacerbates the problem in rural regions and for people below the poverty line, who are a huge participant in the significant growth of TB cases in India.

Case in point — the city of Nagpur in Maharashtra, one of the most populated cities in India, has the highest incidence of tuberculosis, with 35% of the population infected. The community in slums primarily leverage informal healthcare providers, which are although accessible and affordable, have limited awareness and diagnostic tools for TB detection. Thus, the goal was to reduce the diagnostic delay and effectively employ these healthcare providers to increase TB detection in the city.

Also Read: How Is Indore Municipal Corporation Using Geospatial Technology

Qure.ai’s AI-based Detection To The Rescue

TB is a curable disease; however, to make it work, it requires early detection for doctors to start their treatment. But, with a shortage of healthcare providers for testing and detecting the disease, the whole process of …

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Adobe Research Proposes HDMatt, A Deep Learning-Based Image Matting Approach | MarkTechPost

adobe research proposes hdmatt, a deep learning-based image matting approach | marktechpost

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Source: https://syncedreview.com/2020/09/22/adobes-dl-based-hdmatt-handles-image-details-thinner-than-hair/

Image matting is an essential technique to estimate the foreground objects in images and videos for editing and composition. The conventional deep learning approach takes the input image and associated trimap to get the alpha matte using convolution neural networks. But since the real-world input images for matting are mostly of very high resolution, such approaches efficiency suffers in real-world matting applications due to hardware limitations.

Deep learning-based ‘HD-Matt’: Image matting for high-resolution images

To address the issue mentioned above, HD-Matt, the first deep learning-based image matting approach for high-resolution image inputs, is proposed by a group of researchers from UIUC (University of Illinois, Urbana Champaign), Adobe Research, and the University of Oregon. 

HD-Matt works on the ‘divide-and-conquer’ principle. More concretely, it works in a patch-based crop-and-stitch manner to matt high-resolution inputs such as 5000 x 5000 pixels. It crops the input into different patches and then estimates the alpha value of each of the patches. It uses a novel Cross-Patch Context module (CPC) to solve the issue of the prediction inconsistency between different patches. This module helps leverage cross-patch information for each current patch, thus reducing the information loss while using a single patch independently. Then …

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