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spaCy Version 3.0 Released: All Features & Specifications

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The 3.0 version has state of the art transformer-based pipelines and pre-trained models in seventeen languages. The first version of spaCy was a preliminary version with little support for deep-learning workflows. The second version, however, introduced convoluted neural network models in seven different languages. The third version is a massive improvement over both of these versions. The 3.0 version has completed dropped support for Python 2 and only works on Python 3.6.


Real-World Blind Face Restoration with Generative Facial Prior

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Technology and Technological developments in this decade have led to some of the most awe-inspiring discoveries. With rapidly changing technology and systems to support them and provide back-end processing power, the world seems to be becoming a better place to live day by day. Technology has reached such new heights that nothing our ingenious mind today thinks about looks impossible to accomplish. The driving factor of such advancements in this new era of technological and computational superiority seems to be wrapped around two of the most highly debated domains and topics, namely Machine Learning & Artificial Intelligence. The canvas and ideal space that these two domains provide are unfathomable.


DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity - Docwire News

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Protein-ligand binding prediction has extensive biological significance. Binding affinity helps in understanding the degree of protein-ligand interactions and is a useful measure in drug design. Protein-ligand docking using virtual screening and molecular dynamic simulations are required to predict the binding affinity of a ligand to its cognate receptor. Performing such analyses to cover the entire chemical space of small molecules requires intense computational power. Recent developments using deep learning have enabled us to make sense of massive amounts of complex data sets where the ability of the model to "learn" intrinsic patterns in a complex plane of data is the strength of the approach.


Real-time Interpretation: The next frontier in radiology AI - MedCity News

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In the nine years since AlexNet spawned the age of deep learning, artificial intelligence (AI) has made significant technological progress in medical imaging, with more than 80 deep-learning algorithms approved by the U.S. FDA since 2012 for clinical applications in image detection and measurement. A 2020 survey found that more than 82% of imaging providers believe AI will improve diagnostic imaging over the next 10 years and the market for AI in medical imaging is expected to grow 10-fold in the same period. Despite this optimistic outlook, AI still falls short of widespread clinical adoption in radiology. A 2020 survey by the American College of Radiology (ACR) revealed that only about a third of radiologists use AI, mostly to enhance image detection and interpretation; of the two thirds who did not use AI, the majority said they saw no benefit to it. In fact, most radiologists would say that AI has not transformed image reading or improved their practices.


Artificial intelligence

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The intelligence demonstrated by machines is known as artificial intelligence. Artificial intelligence makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Using these, machines can be designed to accomplish different tasks in different fields. Email filters, digital calls, data analysis are all examples of NLP. Machines can accurately identify and locate objects then react to what they "see" using digital images from cameras, videos, and deep learning models.


ARTIFICIAL INTELLIGENCE

#artificialintelligence

The intelligence demonstrated by machines is known as artificial intelligence. Artificial intelligence makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Using these, machines can be designed to accomplish different tasks in different fields. Email filters, digital calls, data analysis are all examples of NLP. Machines can accurately identify and locate objects then react to what they "see" using digital images from cameras, videos, and deep learning models.


Deep Learning

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This article would try to address the basic aspects of deep learning. Deep learning attempts to copy the working mechanism of the human brain by combining data inputs, weights, and biases. The basic mechanism of deep learning is to cluster data and make predictions with a high degree of accuracy. Deep learning involves layers that form a neural network. The layers help in improving accuracy and better prediction.


Algorithm Helps Artificial Intelligence Systems Dodge Adversarial Inputs - ELE Times

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In a perfect world, what you see is what you get. If this were the case, the job of Artificial Intelligence systems would be refreshingly straightforward. Take collision avoidance systems in self-driving cars. If visual input to on-board cameras could be trusted entirely, an AI system could directly map that input to an appropriate action--steer right, steer left, or continue straight--to avoid hitting a pedestrian that its cameras see in the road. But what if there's a glitch in the cameras that slightly shifts an image by a few pixels? If the car blindly trusted so-called'adversarial inputs,' it might take unnecessary and potentially dangerous action.


The Basic Idea of Machine learning, Deep Learning, and Artificial Intelligence

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The basic idea of machine learning, deep learning, and AI is to abstract real-life problems into computerized models, then use mathematical methods, statical analysis, or computer algorithms to solve real-life problems.


Natural Language Processing: NLP In Python with Projects

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We have covered each and every topic in detail and also learned to apply them to real-world problems. There are lots and lots of exercises for you to practice and also 2 bonus NLP Projects "Sentiment analyzer" and "Drugs Prescription using Reviews". In this Sentiment analyzer project, you will learn how to Extract and Scrap Data from Social Media Websites and Extract out Beneficial Information from these Data for Driving Huge Business Insights. In this Drugs Prescription using Reviews project, you will learn how to Deal with Data having Textual Features, you will also learn NLP Techniques to transform and Process the Data to find out Important Insights. You will make use of all the topics read in this course. You will also have access to all the resources used in this course. Enroll now and become a master in machine learning.