Deep Learning

Ian Goodfellow is a Google Research Scientist. Yoshua Bengio is a computer science professor at the Université de Montréal. Aaron Courville is a computer science assistant professor at the Université de Montréal.

Deep learning is a type of machine learning that allows computers to learn from experience and comprehend the world in terms of a concept hierarchy. Because the computer learns via experience, a human computer operator is not required to expressly specify all of the knowledge that the computer requires. The concept hierarchy enables the computer to learn complex concepts by constructing them from smaller ones; a graph of these hierarchies would be many layers thick. Deep Learning covers a wide spectrum of deep learning topics.

The text provides a mathematical and conceptual foundation, covering topics such as linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by industry practitioners, such as deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, Deep Learning provides theoretical perspectives on linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximation inference, and deep generative models.

Undergraduate and graduate students pursuing jobs in industry or research, as well as software developers interested in incorporating deep learning into their products or platforms, can benefit from deep learning. A website provides additional material for both readers and instructors.

Author: Ian Goodfellow, Yoshua Bengio and Aaron Courville

Link to buy: https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618/

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