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Deep-Learning in Radiology


Radiology plays a major role in the diagnosis and treatment of various diseases. Deep-learning, also known as hierarchical learning, is a type of machine learning involving algorithms and based on learning data representations. Deep-learning is used in the field of medicine, particularly in radiology. Deep-learning is a type of machine learning method that helps machines and computers learn by example. In deep-learning, the machine or computer learns about classification tasks directly from sound, text, or image input.

What Is Deep Learning?


Deep learning is a subset of machine learning, a branch of artificial intelligence that configures computers to perform tasks through experience. Deep learning, an advanced artificial intelligence technique, has become increasingly popular in the past few years, thanks to abundant data and increased computing power. It's the main technology behind many of the applications we use every day, including online language translation and automated face-tagging in social media. This technology has also proved useful in healthcare: Earlier this year, computer scientists at the Massachusetts Institute of Technology (MIT) used deep learning to create a new computer program for detecting breast cancer. Classic models had required engineers to manually define the rules and logic for detecting cancer, but for this new model, the scientists gave a deep-learning algorithm 90,000 full-resolution mammogram scans from 60,000 patients and let it find the common patterns between scans of patients who ended up with breast cancer and those who didn't.

Computer science: The learning machines

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Three years ago, researchers at the secretive Google X lab in Mountain View, California, extracted some 10 million still images from YouTube videos and fed them into Google Brain -- a network of 1,000 computers programmed to soak up the world much as a human toddler does. After three days looking for recurring patterns, Google Brain decided, all on its own, that there were certain repeating categories it could identify: human faces, human bodies and … cats1. Google Brain's discovery that the Internet is full of cat videos provoked a flurry of jokes from journalists. But it was also a landmark in the resurgence of deep learning: a three-decade-old technique in which massive amounts of data and processing power help computers to crack messy problems that humans solve almost intuitively, from recognizing faces to understanding language. Deep learning itself is a revival of an even older idea for computing: neural networks.

Introduction to deep learning coursera answers


Deep learning capstone project coursera Economics extended essay topic. Source: Coursera Deep Learning course The input layer and hidden layer are density connected, because every input feature is connected to every hidden layer feature.

How Is Deep Learning Used In Practice? 10 Examples Everyone Must Read


You may have heard about deep learning and felt like it was an area of data science that is incredibly intimidating. How could you possibly get machines to learn like humans?