An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones, machine translation, AI games, driverless cars, and other applications. When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution. Kelleher explains that deep learning enables data-driven decisions by identifying and extracting patterns from large datasets; its ability to learn from complex data makes deep learning ideally suited to take advantage of the rapid growth in big data and computational power. Kelleher also explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art. He describes the most important deep learning architectures, including autoencoders, recurrent neural networks, and long short-term networks, as well as such recent developments as Generative Adversarial Networks and capsule networks. He also provides a comprehensive (and comprehensible) introduction to the two fundamental algorithms in deep learning: gradient descent and backpropagation. Finally, Kelleher considers the future of deep learning—major trends, possible developments, and significant challenges.
What Is Deep Learning? | News & Opinion | PCMag.com
Deep learning is a subset of machine learning, a branch of artificial intelligence that configures computers to perform tasks through experience.
Deep Learning tagged stories - MIT Technology Review
News and reporting on deep learning algorithms and their applications, from MIT Technology Review.
The Science of Deep Learning - National Academy of Sciences
The Science of Deep Learning. March 13 - 14, 2019. National Academy of Sciences, Washington, D.C.. Organized by: David Donoho, Maithra Raghu, Ali Rahimi
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Description: This tutorial will teach you the main ideas of Unsupervised Feature Learning and Deep Learning. By working through it, you will also get to
Deep learning - Wikipedia
Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on artificial neural networks. Learning can be supervised, semi-supervised or unsupervised.
A simple way to understand machine learning vs deep learning
Machine learning and deep learning - you've heard of these terms that describe artificial intelligence. Here's a simple way to understand the
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This article is for those who miss a painless journey through the black box of deep learning. We motivate and engage the readers with a story about a farmer
Deep Learning Techniques for Text Classification - Data Driven
Deep Neural Networks architectures are designed to learn through multiple connections of layers where every single layer only receives a
Deep Learning
Deep Learning is a rapidly growing area of machine learning. To learn more, check out our deep learning tutorial. (There is also an older version, which has also
What Is Deep Learning? | How It Works, Techniques & Applications
Deep learning is a machine learning technique that teaches computers to learn by example. Learn more about deep learning with MATLAB examples and tools.
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