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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

Free german audiobooks download Deep Learning FB2

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.

MIT Deep Learning
Courses on deep learning, deep reinforcement learning (deep RL), and artificial intelligence (AI) taught by Lex Fridman at MIT. Lectures, introductory tutorials  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  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  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  Deep Learning | Udacity
Deep learning is driving advances in artificial intelligence that are changing our world. Enroll now to build and apply your own deep neural networks to produce 

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