Neural Networks And Learning Machines by Simon Haykin
A Beginner's Guide to Neural Networks and Deep Learning
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Haykin Statistical learning theory, including VC, regularization, and Bayesian theories. Algorithms for multilayer perceptrons, kernel-based learning machines, self-organizing maps, principal components analysis, and blind source separation. Sequential state estimation algorithms, including extended Kalman filter, unscented Kalman filter, and particle filters; applications to learning machines. Skip to main content. Directory for Information. Officers of Administration.
Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated. Neural networks help us cluster and classify. You can think of them as a clustering and classification layer on top of the data you store and manage. They help to group unlabeled data according to similarities among the example inputs, and they classify data when they have a labeled dataset to train on. Neural networks can also extract features that are fed to other algorithms for clustering and classification; so you can think of deep neural networks as components of larger machine-learning applications involving algorithms for reinforcement learning , classification and regression.
This post is intended for complete beginners and assumes ZERO prior knowledge of machine learning. Note: I recommend reading this post on victorzhou. First, we have to talk about neurons, the basic unit of a neural network.
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Neural Networks and Learning Machines (3rd Edition)
We need machine learning for tasks that are too complex for humans to code directly, i. So instead, we provide a machine learning algorithm with a large amount of data and let it explore and search for a model that will work out what the programmers have set out to achieve. Then comes the Machine Learning Approach : instead of writing a program by hand for each specific task, we collect lots of examples that specify the correct output for a given input. A machine learning algorithm then takes these examples and produces a program that does the job. The program produced by the learning algorithm may look very different from a typical hand-written program — it may contain millions of numbers. If we do it right, the program works for new cases, as well as the ones we trained it on.