Artificial neural network An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one neuron to the input of another. For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons. This process is repeated until finally, an output neuron is activated. This determines which character was read. Like other machine learning methods - systems that learn from data - neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition. Background[edit] History[edit] Farley and Wesley A. Models[edit] or both
Cellular neural network In computer science and machine learning, cellular neural networks (CNN) are a parallel computing paradigm similar to neural networks, with the difference that communication is allowed between neighbouring units only. Typical applications include image processing, analyzing 3D surfaces, solving partial differential equations, reducing non-visual problems to geometric maps, modelling biological vision and other sensory-motor organs. CNN architecture[edit] Due to their number and variety of architectures, it is difficult to give a precise definition for a CNN processor. Cells are defined in a normed space, commonly a two-dimensional Euclidean geometry, like a grid. Most CNN architectures have cells with the same relative interconnect, but there are applications that require, Multiple-Neighborhood-Size CNN (MNS-CNN), consisting of spatially variant topology. Literature review[edit] There are several overviews of CNN processors. Related processing architectures[edit] Model of computation[edit]
Restricted Boltzmann machine Diagram of a restricted Boltzmann machine with three visible units and four hidden units (no bias units). A restricted Boltzmann machine (RBM) is a generative stochastic neural network that can learn a probability distribution over its set of inputs. RBMs were initially invented under the name Harmonium by Paul Smolensky in 1986,[1] but only rose to prominence after Geoffrey Hinton and collaborators invented fast learning algorithms for them in the mid-2000s. RBMs have found applications in dimensionality reduction,[2] classification,[3] collaborative filtering, feature learning[4] and topic modelling.[5] They can be trained in either supervised or unsupervised ways, depending on the task. Restricted Boltzmann machines can also be used in deep learning networks. In particular, deep belief networks can be formed by "stacking" RBMs and optionally fine-tuning the resulting deep network with gradient descent and backpropagation.[7] Structure[edit] and visible unit for the visible units and where
Deep learning Branch of machine learning Deep learning is the subset of machine learning methods based on artificial neural networks with representation learning. The adjective "deep" refers to the use of multiple layers in the network. Artificial neural networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems. Definition[edit] Deep learning is a class of machine learning algorithms that[9]: 199–200 uses multiple layers to progressively extract higher-level features from the raw input. From another angle to view deep learning, deep learning refers to "computer-simulate" or "automate" human learning processes from a source (e.g., an image of dogs) to a learned object (dogs). Overview[edit] The word "deep" in "deep learning" refers to the number of layers through which the data is transformed. Deep learning algorithms can be applied to unsupervised learning tasks. Interpretations[edit] History[edit] Deep learning revolution[edit] Neural networks[edit]
Multilayer Perceptron Neural Networks A Brief History of Neural Networks Neural networks are predictive models loosely based on the action of biological neurons. The selection of the name “neural network” was one of the great PR successes of the Twentieth Century. It certainly sounds more exciting than a technical description such as “A network of weighted, additive values with nonlinear transfer functions”. However, despite the name, neural networks are far from “thinking machines” or “artificial brains”. A typical artifical neural network might have a hundred neurons. The original “Perceptron” model was developed by Frank Rosenblatt in 1958. Interest in neural networks was revived in 1986 when David Rumelhart, Geoffrey Hinton and Ronald Williams published “Learning Internal Representations by Error Propagation”. Types of Neural Networks When used without qualification, the terms “Neural Network” (NN) and “Artificial Neural Network” (ANN) usually refer to a Multilayer Perceptron Network. Multilayer Perceptron Architecture
Self-organizing map A self-organizing map (SOM) or self-organizing feature map (SOFM) is a type of artificial neural network (ANN) that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional), discretized representation of the input space of the training samples, called a map. Self-organizing maps are different from other artificial neural networks in the sense that they use a neighborhood function to preserve the topological properties of the input space. This makes SOMs useful for visualizing low-dimensional views of high-dimensional data, akin to multidimensional scaling. The model was first described as an artificial neural network by the Finnish professor Teuvo Kohonen, and is sometimes called a Kohonen map or network.[1][2] Like most artificial neural networks, SOMs operate in two modes: training and mapping. A self-organizing map consists of components called nodes or neurons. Large SOMs display emergent properties. Learning algorithm[edit] Variables[edit]
Machine Learning Project at the University of Waikato in New Zealand Neural Network Applications An Artificial Neural Network is a network of many very simple processors ("units"), each possibly having a (small amount of) local memory. The units are connected by unidirectional communication channels ("connections"), which carry numeric (as opposed to symbolic) data. The units operate only on their local data and on the inputs they receive via the connections. The design motivation is what distinguishes neural networks from other mathematical techniques: A neural network is a processing device, either an algorithm, or actual hardware, whose design was motivated by the design and functioning of human brains and components thereof. There are many different types of Neural Networks, each of which has different strengths particular to their applications. The abilities of different networks can be related to their structure, dynamics and learning methods. 2.0 Applications There are abundant materials, tutorials, references and disparate list of demos on the net.
Connectionism Connectionism is a set of approaches in the fields of artificial intelligence, cognitive psychology, cognitive science, neuroscience, and philosophy of mind, that models mental or behavioral phenomena as the emergent processes of interconnected networks of simple units. There are many forms of connectionism, but the most common forms use neural network models. Basic principles[edit] The central connectionist principle is that mental phenomena can be described by interconnected networks of simple and often uniform units. The form of the connections and the units can vary from model to model. For example, units in the network could represent neurons and the connections could represent synapses. Spreading activation[edit] In most connectionist models, networks change over time. Neural networks[edit] Most of the variety among neural network models comes from: Biological realism[edit] Learning[edit] The weights in a neural network are adjusted according to some learning rule or algorithm.
Dimensionality reduction In machine learning and statistics, dimensionality reduction or dimension reduction is the process of reducing the number of random variables under consideration,[1] and can be divided into feature selection and feature extraction.[2] Feature selection[edit] Feature extraction[edit] The main linear technique for dimensionality reduction, principal component analysis, performs a linear mapping of the data to a lower-dimensional space in such a way that the variance of the data in the low-dimensional representation is maximized. Principal component analysis can be employed in a nonlinear way by means of the kernel trick. An alternative approach to neighborhood preservation is through the minimization of a cost function that measures differences between distances in the input and output spaces. Dimension reduction[edit] See also[edit] Notes[edit] Jump up ^ Roweis, S. References[edit] Fodor,I. (2002) "A survey of dimension reduction techniques". External links[edit]
Feed-forward Feedforward may refer to: Feedforward neural network In a feed forward network information always moves one direction; it never goes backwards. A feedforward neural network is an artificial neural network where connections between the units do not form a directed cycle. This is different from recurrent neural networks. The feedforward neural network was the first and simplest type of artificial neural network devised. In this network, the information moves in only one direction, forward, from the input nodes, through the hidden nodes (if any) and to the output nodes. Single-layer perceptron[edit] The simplest kind of neural network is a single-layer perceptron network, which consists of a single layer of output nodes; the inputs are fed directly to the outputs via a series of weights. A perceptron can be created using any values for the activated and deactivated states as long as the threshold value lies between the two. Perceptrons can be trained by a simple learning algorithm that is usually called the delta rule. (times See also[edit]
Online machine learning Online machine learning is used in the case where the data becomes available in a sequential fashion, in order to determine a mapping from the dataset to the corresponding labels. The key difference between online learning and batch learning (or "offline" learning) techniques, is that in online learning the mapping is updated after the arrival of every new datapoint in a scalable fashion, whereas batch techniques are used when one has access to the entire training dataset at once. Online learning could be used in the case of a process occurring in time, for example the value of a stock given its history and other external factors, in which case the mapping updates as time goes on and we get more and more samples. Ideally in online learning, the memory needed to store the function remains constant even with added datapoints, since the solution computed at one step is updated when a new datapoint becomes available, after which that datapoint can then be discarded. , where on . , such that .
Backpropagation Backpropagation, an abbreviation for "backward propagation of errors", is a common method of training artificial neural networks. From a desired output, the network learns from many inputs, similar to the way a child learns to identify a dog from examples of dogs. It is a supervised learning method, and is a generalization of the delta rule. It requires a dataset of the desired output for many inputs, making up the training set. This article provides details on how backpropagation works. Motivation[edit] Summary[edit] The backpropagation learning algorithm can be divided into two phases: propagation and weight update. Phase 1: Propagation[edit] Each propagation involves the following steps: Phase 2: Weight update[edit] For each weight-synapse follow the following steps: Multiply its output delta and input activation to get the gradient of the weight.Subtract a ratio (percentage) of the gradient from the weight. Repeat phase 1 and 2 until the performance of the network is satisfactory. ) where and