Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence [Audiobook] download free by Sandro Skansi

Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence Audiobook download free by Sandro Skansi
  • Listen audiobook: Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence
  • Author: Sandro Skansi
  • Release date: 2018/12/27
  • Publisher: SPRINGER INTERNATIONAL PUBLISHING AG
  • Language: English
  • Genre or Collection: Computing
  • ISBN: 9783319730035
  • Rating: 9.51 of 10
  • Votes: 722
  • Review by: Caroline Lo
  • Review rating: 9.02 of 10
  • Review Date: 2018/9/2
  • Duration: 2H26M33S in 256 kbps (38.2 MB)
  • Date of creation of the audiobook: 2018-07-15
  • You can listen to this audiobook in formats: WavPack, WAV, FLAC, MPEG4, MP3, WMA, AAC (compression TAR.LZO, DMG, RPM, RAR, ZIP, ARJ)
  • Total pages original book: 191
  • Includes a PDF summary of 16 pages
  • Duration of the summary (audio): 11M17S (3.2 MB)
  • Description or summary of the audiobook: This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website.Topics and features: introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks, and the recurrent connections to a feed-forward neural network; describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning; presents a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism.This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.
  • Other categories, genre or collection: Coding Theory & Cryptology, Pattern Recognition, Applied Mathematics, Computer Vision, Expert Systems / Knowledge-based Systems, Information Theory, Graphical & Digital Media Applications, Mathematical Modelling, Image Processing, Data Mining
  • Download servers: Google Drive, pCloud, 4Shared, Microsoft OneDrive, ADrive, Torrent, MEGA, Hightail, Uploaded. Compressed in TAR.LZO, DMG, RPM, RAR, ZIP, ARJ
  • Format: Paperback
  • Approximate value: 36.72 USD
  • Dimensions: 155x235x11.18mm
  • Weight: 326g
  • Printed by: Not Available
  • Published in: Cham, Switzerland

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