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.

Deep Learning

Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

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Reinforcement Learning: An Introduction

From the Publisher:
This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modelling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100 exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.

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Neural networks for pattern recognition

This paper proposes a paradigm for managing the dynamic aspects of organizational knowledge creating processes. Its central theme is that organizational knowledge is created through a continuous dialogue between tacit and explicit knowledge. The nature of this dialogue is examined and four patterns of interaction involving tacit and explicit knowledge are identified. It is argued that while new knowledge is developed by individuals, organizations play a critical role in articulating and amplifying that knowledge. A theoretical framework is developed which provides an analytical perspective on the constituent dimensions of knowledge creation. This framework is then applied in two operational models for facilitating the dynamic creation of appropriate organizational knowledge.

/pdf/a-dynamic-theory-of-organizational-knowledge-creation-22t9dxu55o.pdf

A Dynamic Theory of Organizational Knowledge Creation

https://www2.econ.iastate.edu/tesfatsi/DeepLearningInNeuralNetworksOverview.JSchmidhuber2015.pdf

Deep learning in neural networks

Ein Logikkalkül für die der Nerventätigkeit immanenten Gedanken

Despite the amount of work that has been done since Herter1 first studied convulsant properties of indole, several points seemed worth clarifying. To this end, experiments were undertaken with the surgical assistance of Dr. Edward Davis.In the first place, indole directly applied to the cortex of dog or cat produces a-localized discharge resembling in many ways that seen following local strychninization. This is apparent from Fig. 1. As with strychnine, the concentration required for local application to elicit these results is great.On the other hand, rapid intravenous injections of 20 mg per kg into dogs, cats and rabbits bring on convulsions which differ from fits induced by cortical excitation in that the motions of various muscles are not synchronized, and in that the axial and axio-appendicular, not the appendicular, muscles are most involved. Yanai2 has reported that seizures occur after decerebration, to which we can add that they are not significantly altered by decerebration.After transection of...

Site of Action of Indole in the Central Nervous System

SummaryFor the first 15 minutes the rate of fall in reducing substances in the blood expressed in per cent of fasting value is not much affected by decreasing the size of the intravenous dose of insulin below 0.1 u/kg or by increasing it in oneirophrenics, At 15 minutes the separation is statistically valid despite a bump of unknown significance on this descending limb of the curve. Therefore, it is possible, in patients suspected of oneiro-phrenia, to estimate the resistance to insulin with a statistical validity as great as can be obtained from much longer tests.

Warren S. McCulloch

Papers

Ein Logikkalkül für die der Nerventätigkeit immanenten Gedanken

Site of Action of Indole in the Central Nervous System

A Brief lnsulil Tolerance Test

Committee on Mathematical Biology.

Cybernetic problems of learning. Conditioning of control, of command, and of expediency.