Deep Learning MIT Press Adaptive Computation Machine TOP99

Language Published: English Binding: hardcover It ensures you get the best usage for a longer period

Language Published: English Binding: hardcover It ensures you get the best usage for a longer period

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. "Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Geschrieben von drei Experten auf dem Gebiet, ist "Deep Learning" das einzige umfassende Buch zu diesem Thema. Deep Learning ist eine Form des maschinellen Lernens, die es Computern ermöglicht, aus Erfahrungen zu lernen und die Welt in Form einer Hierarchie von Konzepten zu verstehen. Da der Computer Wissen aus Erfahrungen sammelt, ist es nicht notwendig, dass ein menschlicher Computeroperator alle Kenntnisse, die der Computer benötigt, formell angibt. Die Hierarchie der Konzepte ermöglicht es dem Computer, komplizierte Konzepte zu lernen, indem er sie aus einfacheren aufbaut; ein Diagramm dieser Hierarchien wäre viele Schichten tief. Dieses Buch führt in eine breite Palette von Themen im Deep Learning ein. Der Text bietet mathematische und konzeptionelle Grundlagen und behandelt relevante Konzepte der linearen Algebra, Wahrscheinlichkeitstheorie und Informationstheorie, numerische Berechnungen und maschinelles Lernen. Es beschreibt Deep-Learning-Techniken, die von Praktikern in der Industrie verwendet werden, einschliesslich tiefen Feedforward-Netzwerken, Regularisierung, Optimierungsalgorithmen, konvolutionalen Netzwerken, Sequenzmodellierung und praktischer Methodik; und es gibt einen Überblick über Anwendungen wie natürliche Sprachverarbeitung, Spracherkennung, Computer Vision, Online-Empfehlungssysteme, Bioinformatik und Videospiele. Schliesslich bietet das Buch Forschungsperspektiven und behandelt theoretische Themen wie lineare Faktormodelle, Autoencoder, Repräsentationslernen, strukturierte probabilistische Modelle, Monte-Carlo-Methoden, die Partitionierungsfunktion, approximative Inferenz und tiefe generative Modelle. "Deep Learning" kann von Bachelor- oder Masterstudenten genutzt werden, die Karrieren in der Industrie oder Forschung anstreben, sowie von Softwareingenieuren, die beginnen möchten, Deep Learning in ihren Produkten oder Plattformen zu verwenden. Eine Website bietet zusätzliches Material für Leser und Dozenten.

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. &#34,Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI, cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones, a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology, and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. "Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.