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The Massachusetts Institute of Technology – Deep Learning: MIT Press Essential Knowledge Series 2019

Updated August 10, 2026 1.6 MB
The Massachusetts Institute of Technology – Deep Learning: MIT Press Essential Knowledge Series 2019

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In Deep Learning: MIT Press Essential Knowledge Series, computer scientist John D. Kelleher offers a very affordable, concise, yet comprehensive guide to deep learning. The technology forms the core of modern artificial intelligence and has enabled features such as machine vision, speech recognition on mobile phones, machine translation, and self-driving cars. The author explains how these systems enable data-driven decision-making by extracting patterns from big data.

Without going into the tedious mathematical complexities, the audiobook explains the enduring concepts, the history of developments, and the current state of the science. Kalhor describes the basic structures of neural networks, such as autoencoders and recurrent neural networks, and moves on to new phenomena, such as generative adversarial networks (GANs). In this work, the listener is introduced to the two fundamental algorithms, “gradient descent” and “back propagation,” in a completely understandable language, and finally examines the challenges and future prospects of this technology.

Book Features

  • Providing specialized but simple explanations that do not require advanced mathematical knowledge to understand the concepts of artificial intelligence.
  • Comprehensive review of the main neural network architectures including “Autoencoders” and “RNN”.
  • A detailed and understandable explanation of key deep learning algorithms such as “Gradient Descent” and “Backpropagation”.
  • Explaining the everyday applications of this technology in the products of major companies such as Google, Apple, and Microsoft.
  • Analysis of trends, future opportunities, and serious challenges facing the ethical development of deep learning.

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Deep Learning: MIT Press Essential Knowledge Series

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Deep Learning: MIT Press Essential Knowledge Series

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What is included

  • Providing specialized but simple explanations that do not require advanced mathematical knowledge to understand the concepts of artificial intelligence.
  • Comprehensive review of the main neural network architectures including “Autoencoders” and “RNN”.
  • A detailed and understandable explanation of key deep learning algorithms such as “Gradient Descent” and “Backpropagation”.
  • Explaining the everyday applications of this technology in the products of major companies such as Google, Apple, and Microsoft.
  • Analysis of trends, future opportunities, and serious challenges facing the ethical development of deep learning.

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