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Deep Learning. John D. Kelleher

Deep-Learning.pdf
ISBN: 9780262537551 | 296 pages | 8 Mb

• Deep Learning
• John D. Kelleher
• Page: 296
• Format: pdf, ePub, fb2, mobi
• ISBN: 9780262537551
• Publisher: MIT Press

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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.

CS 229 - Deep Learning Cheatsheet
Neural networks are a class of models that are built with layers. Commonly used types of neural networks include convolutional and recurrent neural networks. Deep Learning: Definition, Explanation and Applications
Deep learning is a subset of machine learning that tries to find patterns that are seamlessly hidden within data in vast quantities. Deep learning, contrary to  Deep Learning
Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its  Deep Learning - Neural Networks and Deep Learning | IBM
Get started with deep learning. Design complex neural networks then experiment at scale to deploy optimized deep learning models within Watson Studio. Deep Learning & Artificial Neural Networks
Deep Learning | Interested in learning more about deep learning and artificial neural networks? Discover exactly what deep learning is by  Deep Learning Definition | DeepAI
Deep learning is a machine learning method using multiple layers of nonlinear processing units to extract features from data. Find out more on DeepAI. Deep Learning | Coursera
Learn Deep Learning from deeplearning.ai. If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought  Why Deep Learning Works: solving a farmer's problem
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 (with TensorFlow, Keras and PyTorch) | NYC Data
Learn artificial neural networks through interactive labs, featuring the most popular open-source Deep Learning library, TensorFlow, and its high-level API,  A Selective Overview of Deep Learning
In simple words, deep learning uses the composition of many nonlinear While neural networks have a long history, recent advances have  What Is Deep Learning? AI Experts Explain How It Works | Built In
What is deep learning? How does it work? Where is it going? Instead of offering textbook answers, we went straight to the experts and asked 

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