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Artificial neural network – Wikipedia

An artificial neural network is an interconnected group of nodes, inspired by a simplification of neurons in a brain. Here, each circular node represents an …

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Tổng quan về Artificial Neural Network – Viblo

Artificial Neural Network (ANN) gồm 3 thành phần chính: Input layer và output layer chỉ gồm 1 layer , hden layer có thể có 1 hay nhiều layer tùy vào bài …

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[Cẩm nang AI] Artificial Neural Network là gì? Cấu trúc, cách …

ANN là viết tắt của Artificial Neural Networks. Về cơ bản, đây là một mô hình tính toán, chúng được xây dựng dựa trên cấu trúc và chức năng của mạng lưới nơ ron …

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Neural Network là gì? Đặc điểm và ứng dụng của … – Vietnix

Artificial Neural Network là mạng lưới nơ-ron nhân tạo hay còn gọi là mô hình toán được xây dựng dựa trên các no-rơn sinh học.

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Artificial Neural Network Tutorial – Javatpoint

The term “Artificial neural network” refers to a biologically inspired sub-field of artificial intelligence modeled after the brain. An Artificial neural …

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Artificial Neural Network – an overview | ScienceDirect Topics

Artificial neural networks (ANNs) consist of input, hden, and output layers with connected neurons (nodes) to simulate the human brain. The existing nodes …

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Tổng quan về Neural Network(mạng Nơ Ron nhân tạo) là gì?

Artificial Neural Network là mạng neural nhân tạo và là mô hình toán học hoặc mô hình toán được xây dựng thông qua các neural sinh học.

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What are Neural Networks? – IBM

Artificial neural networks (ANNs) are comprised of a node layers, containing an input layer, one or more hden layers, and an output layer.

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Thuật toán Artificial Neural Network – Tìm hiểu cách learning …

Các Thuật toán Neural Network này được sử dụng để learning Artificial Neural Network. Bài viết này cung cấp cho bạn kiến ​​thức sâu sắc về Gradient Descent, …

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주제와 관련된 이미지 artificial neural network

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Neural Network In 5 Minutes | What Is A Neural Network? | How Neural Networks Work | Simplilearn
Neural Network In 5 Minutes | What Is A Neural Network? | How Neural Networks Work | Simplilearn

주제에 대한 기사 평가 artificial neural network

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Artificial neural network

Computational model used in machine learning, based on connected, hierarchical functions

An artificial neural network is an interconnected group of nodes, inspired by a simplification of neurons in a brain . Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another.

Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets,[1] are computing systems inspired by the biological neural networks that constitute animal brains.[2]

An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron receives signals then processes them and can signal neurons connected to it. The “signal” at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called edges. Neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold.

Typically, neurons are aggregated into layers. Different layers may perform different transformations on their inputs. Signals travel from the first layer (the input layer), to the last layer (the output layer), possibly after traversing the layers multiple times.

Training [ edit ]

Neural networks learn (or are trained) by processing examples, each of which contains a known “input” and “result,” forming probability-weighted associations between the two, which are stored within the data structure of the net itself. The training of a neural network from a given example is usually conducted by determining the difference between the processed output of the network (often a prediction) and a target output. This difference is the error. The network then adjusts its weighted associations according to a learning rule and using this error value. Successive adjustments will cause the neural network to produce output which is increasingly similar to the target output. After a sufficient number of these adjustments the training can be terminated based upon certain criteria. This is known as supervised learning.

Such systems “learn” to perform tasks by considering examples, generally without being programmed with task-specific rules. For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as “cat” or “no cat” and using the results to identify cats in other images. They do this without any prior knowledge of cats, for example, that they have fur, tails, whiskers, and cat-like faces. Instead, they automatically generate identifying characteristics from the examples that they process.

History [ edit ]

Warren McCulloch and Walter Pitts[3] (1943) opened the subject by creating a computational model for neural networks.[4] In the late 1940s, D. O. Hebb[5] created a learning hypothesis based on the mechanism of neural plasticity that became known as Hebbian learning. Farley and Wesley A. Clark[6] (1954) first used computational machines, then called “calculators”, to simulate a Hebbian network. In 1958, psychologist Frank Rosenblatt invented the perceptron, the first artificial neural network,[7][8][9][10] funded by the United States Office of Naval Research.[11] The first functional networks with many layers were published by Ivakhnenko and Lapa in 1965, as the Group Method of Data Handling.[12][13][14] The basics of continuous backpropagation[12][15][16][17] were derived in the context of control theory by Kelley[18] in 1960 and by Bryson in 1961,[19] using principles of dynamic programming. Thereafter research stagnated following Minsky and Papert (1969),[20] who discovered that basic perceptrons were incapable of processing the exclusive-or circuit and that computers lacked sufficient power to process useful neural networks.

In 1970, Seppo Linnainmaa published the general method for automatic differentiation (AD) of discrete connected networks of nested differentiable functions.[21][22] In 1973, Dreyfus used backpropagation to adapt parameters of controllers in proportion to error gradients.[23] Werbos’s (1975) backpropagation algorithm enabled practical training of multi-layer networks. In 1982, he applied Linnainmaa’s AD method to neural networks in the way that became widely used.[15][24]

The development of metal–oxide–semiconductor (MOS) very-large-scale integration (VLSI), in the form of complementary MOS (CMOS) technology, enabled increasing MOS transistor counts in digital electronics. This provided more processing power for the development of practical artificial neural networks in the 1980s.[25]

In 1986 Rumelhart, Hinton and Williams showed that backpropagation learned interesting internal representations of words as feature vectors when trained to predict the next word in a sequence.[26]

From 1988 onward,[27][28] the use of neural networks transformed the field of protein structure prediction, in particular when the first cascading networks were trained on profiles (matrices) produced by multiple sequence alignments.[29]

In 1992, max-pooling was introduced to help with least-shift invariance and tolerance to deformation to aid 3D object recognition.[30][31][32] Schmidhuber adopted a multi-level hierarchy of networks (1992) pre-trained one level at a time by unsupervised learning and fine-tuned by backpropagation.[33]

Neural networks’ early successes included predicting the stock market and in 1995 a (mostly) self-driving car.[a][34]

Geoffrey Hinton et al. (2006) proposed learning a high-level representation using successive layers of binary or real-valued latent variables with a restricted Boltzmann machine[35] to model each layer. In 2012, Ng and Dean created a network that learned to recognize higher-level concepts, such as cats, only from watching unlabeled images.[36] Unsupervised pre-training and increased computing power from GPUs and distributed computing allowed the use of larger networks, particularly in image and visual recognition problems, which became known as “deep learning”.[37]

Ciresan and colleagues (2010)[38] showed that despite the vanishing gradient problem, GPUs make backpropagation feasible for many-layered feedforward neural networks.[39] Between 2009 and 2012, ANNs began winning prizes in image recognition contests, approaching human level performance on various tasks, initially in pattern recognition and handwriting recognition.[40][41] For example, the bi-directional and multi-dimensional long short-term memory (LSTM)[42][43] of Graves et al. won three competitions in connected handwriting recognition in 2009 without any prior knowledge about the three languages to be learned.[42][43]

Ciresan and colleagues built the first pattern recognizers to achieve human-competitive/superhuman performance[44] on benchmarks such as traffic sign recognition (IJCNN 2012).

Models [ edit ]

Neuron and myelinated axon, with signal flow from inputs at dendrites to outputs at axon terminals

ANNs began as an attempt to exploit the architecture of the human brain to perform tasks that conventional algorithms had little success with. They soon reoriented towards improving empirical results, mostly abandoning attempts to remain true to their biological precursors. Neurons are connected to each other in various patterns, to allow the output of some neurons to become the input of others. The network forms a directed, weighted graph.[45]

An artificial neural network consists of a collection of simulated neurons. Each neuron is a node which is connected to other nodes via links that correspond to biological axon-synapse-dendrite connections. Each link has a weight, which determines the strength of one node’s influence on another.[46]

Artificial neurons [ edit ]

ANNs are composed of artificial neurons which are conceptually derived from biological neurons. Each artificial neuron has inputs and produces a single output which can be sent to multiple other neurons.[47] The inputs can be the feature values of a sample of external data, such as images or documents, or they can be the outputs of other neurons. The outputs of the final output neurons of the neural net accomplish the task, such as recognizing an object in an image.

To find the output of the neuron we take the weighted sum of all the inputs, weighted by the weights of the connections from the inputs to the neuron. We add a bias term to this sum.[48] This weighted sum is sometimes called the activation. This weighted sum is then passed through a (usually nonlinear) activation function to produce the output. The initial inputs are external data, such as images and documents. The ultimate outputs accomplish the task, such as recognizing an object in an image.[49]

Organization [ edit ]

The neurons are typically organized into multiple layers, especially in deep learning. Neurons of one layer connect only to neurons of the immediately preceding and immediately following layers. The layer that receives external data is the input layer. The layer that produces the ultimate result is the output layer. In between them are zero or more hidden layers. Single layer and unlayered networks are also used. Between two layers, multiple connection patterns are possible. They can be ‘fully connected’, with every neuron in one layer connecting to every neuron in the next layer. They can be pooling, where a group of neurons in one layer connect to a single neuron in the next layer, thereby reducing the number of neurons in that layer.[50] Neurons with only such connections form a directed acyclic graph and are known as feedforward networks.[51] Alternatively, networks that allow connections between neurons in the same or previous layers are known as recurrent networks.[52]

Hyperparameter [ edit ]

A hyperparameter is a constant parameter whose value is set before the learning process begins. The values of parameters are derived via learning. Examples of hyperparameters include learning rate, the number of hidden layers and batch size.[53] The values of some hyperparameters can be dependent on those of other hyperparameters. For example, the size of some layers can depend on the overall number of layers.

Learning [ edit ]

Learning is the adaptation of the network to better handle a task by considering sample observations. Learning involves adjusting the weights (and optional thresholds) of the network to improve the accuracy of the result. This is done by minimizing the observed errors. Learning is complete when examining additional observations does not usefully reduce the error rate. Even after learning, the error rate typically does not reach 0. If after learning, the error rate is too high, the network typically must be redesigned. Practically this is done by defining a cost function that is evaluated periodically during learning. As long as its output continues to decline, learning continues. The cost is frequently defined as a statistic whose value can only be approximated. The outputs are actually numbers, so when the error is low, the difference between the output (almost certainly a cat) and the correct answer (cat) is small. Learning attempts to reduce the total of the differences across the observations. Most learning models can be viewed as a straightforward application of optimization theory and statistical estimation.[45][54]

Learning rate [ edit ]

The learning rate defines the size of the corrective steps that the model takes to adjust for errors in each observation.[55] A high learning rate shortens the training time, but with lower ultimate accuracy, while a lower learning rate takes longer, but with the potential for greater accuracy. Optimizations such as Quickprop are primarily aimed at speeding up error minimization, while other improvements mainly try to increase reliability. In order to avoid oscillation inside the network such as alternating connection weights, and to improve the rate of convergence, refinements use an adaptive learning rate that increases or decreases as appropriate.[56] The concept of momentum allows the balance between the gradient and the previous change to be weighted such that the weight adjustment depends to some degree on the previous change. A momentum close to 0 emphasizes the gradient, while a value close to 1 emphasizes the last change.

Cost function [ edit ]

While it is possible to define a cost function ad hoc, frequently the choice is determined by the function’s desirable properties (such as convexity) or because it arises from the model (e.g. in a probabilistic model the model’s posterior probability can be used as an inverse cost).

Backpropagation [ edit ]

Backpropagation is a method used to adjust the connection weights to compensate for each error found during learning. The error amount is effectively divided among the connections. Technically, backprop calculates the gradient (the derivative) of the cost function associated with a given state with respect to the weights. The weight updates can be done via stochastic gradient descent or other methods, such as Extreme Learning Machines,[57] “No-prop” networks,[58] training without backtracking,[59] “weightless” networks,[60][61] and non-connectionist neural networks.[citation needed]

Learning paradigms [ edit ]

Machine learning is commonly separated into three main learning paradigms, supervised learning, unsupervised learning and reinforcement learning.[62] Each corresponds to a particular learning task.

Supervised learning [ edit ]

Supervised learning uses a set of paired inputs and desired outputs. The learning task is to produce the desired output for each input. In this case the cost function is related to eliminating incorrect deductions.[63] A commonly used cost is the mean-squared error, which tries to minimize the average squared error between the network’s output and the desired output. Tasks suited for supervised learning are pattern recognition (also known as classification) and regression (also known as function approximation). Supervised learning is also applicable to sequential data (e.g., for hand writing, speech and gesture recognition). This can be thought of as learning with a “teacher”, in the form of a function that provides continuous feedback on the quality of solutions obtained thus far.

Unsupervised learning [ edit ]

In unsupervised learning, input data is given along with the cost function, some function of the data x {\displaystyle \textstyle x} and the network’s output. The cost function is dependent on the task (the model domain) and any a priori assumptions (the implicit properties of the model, its parameters and the observed variables). As a trivial example, consider the model f ( x ) = a {\displaystyle \textstyle f(x)=a} where a {\displaystyle \textstyle a} is a constant and the cost C = E [ ( x − f ( x ) ) 2 ] {\displaystyle \textstyle C=E[(x-f(x))^{2}]} . Minimizing this cost produces a value of a {\displaystyle \textstyle a} that is equal to the mean of the data. The cost function can be much more complicated. Its form depends on the application: for example, in compression it could be related to the mutual information between x {\displaystyle \textstyle x} and f ( x ) {\displaystyle \textstyle f(x)} , whereas in statistical modeling, it could be related to the posterior probability of the model given the data (note that in both of those examples those quantities would be maximized rather than minimized). Tasks that fall within the paradigm of unsupervised learning are in general estimation problems; the applications include clustering, the estimation of statistical distributions, compression and filtering.

Reinforcement learning [ edit ]

In applications such as playing video games, an actor takes a string of actions, receiving a generally unpredictable response from the environment after each one. The goal is to win the game, i.e., generate the most positive (lowest cost) responses. In reinforcement learning, the aim is to weight the network (devise a policy) to perform actions that minimize long-term (expected cumulative) cost. At each point in time the agent performs an action and the environment generates an observation and an instantaneous cost, according to some (usually unknown) rules. The rules and the long-term cost usually only can be estimated. At any juncture, the agent decides whether to explore new actions to uncover their costs or to exploit prior learning to proceed more quickly.

Formally the environment is modeled as a Markov decision process (MDP) with states s 1 , . . . , s n ∈ S {\displaystyle \textstyle {s_{1},…,s_{n}}\in S} and actions a 1 , . . . , a m ∈ A {\displaystyle \textstyle {a_{1},…,a_{m}}\in A} . Because the state transitions are not known, probability distributions are used instead: the instantaneous cost distribution P ( c t | s t ) {\displaystyle \textstyle P(c_{t}|s_{t})} , the observation distribution P ( x t | s t ) {\displaystyle \textstyle P(x_{t}|s_{t})} and the transition distribution P ( s t + 1 | s t , a t ) {\displaystyle \textstyle P(s_{t+1}|s_{t},a_{t})} , while a policy is defined as the conditional distribution over actions given the observations. Taken together, the two define a Markov chain (MC). The aim is to discover the lowest-cost MC.

ANNs serve as the learning component in such applications.[64][65] Dynamic programming coupled with ANNs (giving neurodynamic programming)[66] has been applied to problems such as those involved in vehicle routing,[67] video games, natural resource management[68][69] and medicine[70] because of ANNs ability to mitigate losses of accuracy even when reducing the discretization grid density for numerically approximating the solution of control problems. Tasks that fall within the paradigm of reinforcement learning are control problems, games and other sequential decision making tasks.

Self-learning in neural networks was introduced in 1982 along with a neural network capable of self-learning named Crossbar Adaptive Array (CAA).[71] It is a system with only one input, situation s, and only one output, action (or behavior) a. It has neither external advice input nor external reinforcement input from the environment. The CAA computes, in a crossbar fashion, both decisions about actions and emotions (feelings) about encountered situations. The system is driven by the interaction between cognition and emotion.[72] Given the memory matrix, W =||w(a,s)||, the crossbar self-learning algorithm in each iteration performs the following computation:

In situation s perform action a; Receive consequence situation s’; Compute emotion of being in consequence situation v(s’); Update crossbar memory w'(a,s) = w(a,s) + v(s’).

The backpropagated value (secondary reinforcement) is the emotion toward the consequence situation. The CAA exists in two environments, one is behavioral environment where it behaves, and the other is genetic environment, where from it initially and only once receives initial emotions about to be encountered situations in the behavioral environment. Having received the genome vector (species vector) from the genetic environment, the CAA will learn a goal-seeking behavior, in the behavioral environment that contains both desirable and undesirable situations.[73]

Neuroevolution [ edit ]

Neuroevolution can create neural network topologies and weights using evolutionary computation. It is competitive with sophisticated gradient descent approaches[citation needed]. One advantage of neuroevolution is that it may be less prone to get caught in “dead ends”.[74]

Stochastic neural network [ edit ]

Stochastic neural networks originating from Sherrington–Kirkpatrick models are a type of artificial neural network built by introducing random variations into the network, either by giving the network’s artificial neurons stochastic transfer functions, or by giving them stochastic weights. This makes them useful tools for optimization problems, since the random fluctuations help the network escape from local minima.[75]

Other [ edit ]

In a Bayesian framework, a distribution over the set of allowed models is chosen to minimize the cost. Evolutionary methods,[76] gene expression programming,[77] simulated annealing,[78] expectation-maximization, non-parametric methods and particle swarm optimization[79] are other learning algorithms. Convergent recursion is a learning algorithm for cerebellar model articulation controller (CMAC) neural networks.[80][81]

Modes [ edit ]

Two modes of learning are available: stochastic and batch. In stochastic learning, each input creates a weight adjustment. In batch learning weights are adjusted based on a batch of inputs, accumulating errors over the batch. Stochastic learning introduces “noise” into the process, using the local gradient calculated from one data point; this reduces the chance of the network getting stuck in local minima. However, batch learning typically yields a faster, more stable descent to a local minimum, since each update is performed in the direction of the batch’s average error. A common compromise is to use “mini-batches”, small batches with samples in each batch selected stochastically from the entire data set.

Types [ edit ]

ANNs have evolved into a broad family of techniques that have advanced the state of the art across multiple domains. The simplest types have one or more static components, including number of units, number of layers, unit weights and topology. Dynamic types allow one or more of these to evolve via learning. The latter are much more complicated, but can shorten learning periods and produce better results. Some types allow/require learning to be “supervised” by the operator, while others operate independently. Some types operate purely in hardware, while others are purely software and run on general purpose computers.

Some of the main breakthroughs include: convolutional neural networks that have proven particularly successful in processing visual and other two-dimensional data;[82][83] long short-term memory avoid the vanishing gradient problem[84] and can handle signals that have a mix of low and high frequency components aiding large-vocabulary speech recognition,[85][86] text-to-speech synthesis,[87][15][88] and photo-real talking heads;[89] competitive networks such as generative adversarial networks in which multiple networks (of varying structure) compete with each other, on tasks such as winning a game[90] or on deceiving the opponent about the authenticity of an input.[91]

Network design [ edit ]

Neural architecture search (NAS) uses machine learning to automate ANN design. Various approaches to NAS have designed networks that compare well with hand-designed systems. The basic search algorithm is to propose a candidate model, evaluate it against a dataset and use the results as feedback to teach the NAS network.[92] Available systems include AutoML and AutoKeras.[93]

Design issues include deciding the number, type and connectedness of network layers, as well as the size of each and the connection type (full, pooling, …).

Hyperparameters must also be defined as part of the design (they are not learned), governing matters such as how many neurons are in each layer, learning rate, step, stride, depth, receptive field and padding (for CNNs), etc.[94]

Use [ edit ]

Using Artificial neural networks requires an understanding of their characteristics.

Choice of model: This depends on the data representation and the application. Overly complex models are slow learning.

Learning algorithm: Numerous trade-offs exist between learning algorithms. Almost any algorithm will work well with the correct hyperparameters for training on a particular data set. However, selecting and tuning an algorithm for training on unseen data requires significant experimentation.

Robustness: If the model, cost function and learning algorithm are selected appropriately, the resulting ANN can become robust.

ANN capabilities fall within the following broad categories:[citation needed]

Applications [ edit ]

Because of their ability to reproduce and model nonlinear processes, artificial neural networks have found applications in many disciplines. Application areas include system identification and control (vehicle control, trajectory prediction,[96] process control, natural resource management), quantum chemistry,[97] general game playing,[98] pattern recognition (radar systems, face identification, signal classification,[99] 3D reconstruction,[100] object recognition and more), sensor data analysis,[101] sequence recognition (gesture, speech, handwritten and printed text recognition[102]), medical diagnosis, finance[103] (e.g. automated trading systems), data mining, visualization, machine translation, social network filtering[104] and e-mail spam filtering. ANNs have been used to diagnose several types of cancers[105][106] and to distinguish highly invasive cancer cell lines from less invasive lines using only cell shape information.[107][108]

ANNs have been used to accelerate reliability analysis of infrastructures subject to natural disasters[109][110] and to predict foundation settlements.[111] ANNs have also been used for building black-box models in geoscience: hydrology,[112][113] ocean modelling and coastal engineering,[114][115] and geomorphology.[116] ANNs have been employed in cybersecurity, with the objective to discriminate between legitimate activities and malicious ones. For example, machine learning has been used for classifying Android malware,[117] for identifying domains belonging to threat actors and for detecting URLs posing a security risk.[118] Research is underway on ANN systems designed for penetration testing, for detecting botnets,[119] credit cards frauds[120] and network intrusions.

ANNs have been proposed as a tool to solve partial differential equations in physics[121][122][123] and simulate the properties of many-body open quantum systems.[124][125][126][127] In brain research ANNs have studied short-term behavior of individual neurons,[128] the dynamics of neural circuitry arise from interactions between individual neurons and how behavior can arise from abstract neural modules that represent complete subsystems. Studies considered long-and short-term plasticity of neural systems and their relation to learning and memory from the individual neuron to the system level.

Theoretical properties [ edit ]

Computational power [ edit ]

The multilayer perceptron is a universal function approximator, as proven by the universal approximation theorem. However, the proof is not constructive regarding the number of neurons required, the network topology, the weights and the learning parameters.

A specific recurrent architecture with rational-valued weights (as opposed to full precision real number-valued weights) has the power of a universal Turing machine,[129] using a finite number of neurons and standard linear connections. Further, the use of irrational values for weights results in a machine with super-Turing power.[130]

Capacity [ edit ]

A model’s “capacity” property corresponds to its ability to model any given function. It is related to the amount of information that can be stored in the network and to the notion of complexity. Two notions of capacity are known by the community. The information capacity and the VC Dimension. The information capacity of a perceptron is intensively discussed in Sir David MacKay’s book[131] which summarizes work by Thomas Cover.[132] The capacity of a network of standard neurons (not convolutional) can be derived by four rules[133] that derive from understanding a neuron as an electrical element. The information capacity captures the functions modelable by the network given any data as input. The second notion, is the VC dimension. VC Dimension uses the principles of measure theory and finds the maximum capacity under the best possible circumstances. This is, given input data in a specific form. As noted in,[131] the VC Dimension for arbitrary inputs is half the information capacity of a Perceptron. The VC Dimension for arbitrary points is sometimes referred to as Memory Capacity.[134]

Convergence [ edit ]

Models may not consistently converge on a single solution, firstly because local minima may exist, depending on the cost function and the model. Secondly, the optimization method used might not guarantee to converge when it begins far from any local minimum. Thirdly, for sufficiently large data or parameters, some methods become impractical.

Another issue worthy to mention is that training may cross some Saddle point which may lead the convergence to the wrong direction.

The convergence behavior of certain types of ANN architectures are more understood than others. When the width of network approaches to infinity, the ANN is well described by its first order Taylor expansion throughout training, and so inherits the convergence behavior of affine models.[135][136] Another example is when parameters are small, it is observed that ANNs often fits target functions from low to high frequencies. This behavior is referred to as the spectral bias, or frequency principle, of neural networks.[137][138][139][140] This phenomenon is the opposite to the behavior of some well studied iterative numerical schemes such as Jacobi method. Deeper neural networks have been observed to be more biased towards low frequency functions.[141]

Generalization and statistics [ edit ]

Applications whose goal is to create a system that generalizes well to unseen examples, face the possibility of over-training. This arises in convoluted or over-specified systems when the network capacity significantly exceeds the needed free parameters. Two approaches address over-training. The first is to use cross-validation and similar techniques to check for the presence of over-training and to select hyperparameters to minimize the generalization error.

The second is to use some form of regularization. This concept emerges in a probabilistic (Bayesian) framework, where regularization can be performed by selecting a larger prior probability over simpler models; but also in statistical learning theory, where the goal is to minimize over two quantities: the ’empirical risk’ and the ‘structural risk’, which roughly corresponds to the error over the training set and the predicted error in unseen data due to overfitting.

Confidence analysis of a neural network

Supervised neural networks that use a mean squared error (MSE) cost function can use formal statistical methods to determine the confidence of the trained model. The MSE on a validation set can be used as an estimate for variance. This value can then be used to calculate the confidence interval of network output, assuming a normal distribution. A confidence analysis made this way is statistically valid as long as the output probability distribution stays the same and the network is not modified.

By assigning a softmax activation function, a generalization of the logistic function, on the output layer of the neural network (or a softmax component in a component-based network) for categorical target variables, the outputs can be interpreted as posterior probabilities. This is useful in classification as it gives a certainty measure on classifications.

The softmax activation function is:

y i = e x i ∑ j = 1 c e x j {\displaystyle y_{i}={\frac {e^{x_{i}}}{\sum _{j=1}^{c}e^{x_{j}}}}}

Criticism [ edit ]

Training [ edit ]

A common criticism of neural networks, particularly in robotics, is that they require too much training for real-world operation.[citation needed] Potential solutions include randomly shuffling training examples, by using a numerical optimization algorithm that does not take too large steps when changing the network connections following an example, grouping examples in so-called mini-batches and/or introducing a recursive least squares algorithm for CMAC.[80]

Theory [ edit ]

A central claim of ANNs is that they embody new and powerful general principles for processing information. These principles are ill-defined. It is often claimed that they are emergent from the network itself. This allows simple statistical association (the basic function of artificial neural networks) to be described as learning or recognition. In 1997, Alexander Dewdney commented that, as a result, artificial neural networks have a “something-for-nothing quality, one that imparts a peculiar aura of laziness and a distinct lack of curiosity about just how good these computing systems are. No human hand (or mind) intervenes; solutions are found as if by magic; and no one, it seems, has learned anything”.[142] One response to Dewdney is that neural networks handle many complex and diverse tasks, ranging from autonomously flying aircraft[143] to detecting credit card fraud to mastering the game of Go.

Technology writer Roger Bridgman commented:

Neural networks, for instance, are in the dock not only because they have been hyped to high heaven, (what hasn’t?) but also because you could create a successful net without understanding how it worked: the bunch of numbers that captures its behaviour would in all probability be “an opaque, unreadable table…valueless as a scientific resource”. In spite of his emphatic declaration that science is not technology, Dewdney seems here to pillory neural nets as bad science when most of those devising them are just trying to be good engineers. An unreadable table that a useful machine could read would still be well worth having.[144]

Biological brains use both shallow and deep circuits as reported by brain anatomy,[145] displaying a wide variety of invariance. Weng[146] argued that the brain self-wires largely according to signal statistics and therefore, a serial cascade cannot catch all major statistical dependencies.

Hardware [ edit ]

Large and effective neural networks require considerable computing resources.[147] While the brain has hardware tailored to the task of processing signals through a graph of neurons, simulating even a simplified neuron on von Neumann architecture may consume vast amounts of memory and storage. Furthermore, the designer often needs to transmit signals through many of these connections and their associated neurons – which require enormous CPU power and time.

Schmidhuber noted that the resurgence of neural networks in the twenty-first century is largely attributable to advances in hardware: from 1991 to 2015, computing power, especially as delivered by GPGPUs (on GPUs), has increased around a million-fold, making the standard backpropagation algorithm feasible for training networks that are several layers deeper than before.[12] The use of accelerators such as FPGAs and GPUs can reduce training times from months to days.[147]

Neuromorphic engineering or a physical neural network addresses the hardware difficulty directly, by constructing non-von-Neumann chips to directly implement neural networks in circuitry. Another type of chip optimized for neural network processing is called a Tensor Processing Unit, or TPU.[148]

Practical counterexamples [ edit ]

Analyzing what has been learned by an ANN is much easier than analyzing what has been learned by a biological neural network. Furthermore, researchers involved in exploring learning algorithms for neural networks are gradually uncovering general principles that allow a learning machine to be successful. For example, local vs. non-local learning and shallow vs. deep architecture.[149]

Hybrid approaches [ edit ]

Advocates of hybrid models (combining neural networks and symbolic approaches), claim that such a mixture can better capture the mechanisms of the human mind.[150]

Gallery [ edit ]

A single-layer feedforward artificial neural network. Arrows originating from x 2 {\displaystyle \scriptstyle x_{2}} are omitted for clarity. There are p inputs to this network and q outputs. In this system, the value of the qth output, y q {\displaystyle \scriptstyle y_{q}} would be calculated as y q = K ∗ ( ∑ ( x i ∗ w i q ) − b q ) {\displaystyle \scriptstyle y_{q}=K*(\sum (x_{i}*w_{iq})-b_{q})}

A two-layer feedforward artificial neural network.

An artificial neural network.

An ANN dependency graph.

A single-layer feedforward artificial neural network with 4 inputs, 6 hidden and 2 outputs. Given position state and direction outputs wheel based control values.

A two-layer feedforward artificial neural network with 8 inputs, 2×8 hidden and 2 outputs. Given position state, direction and other environment values outputs thruster based control values.

Parallel pipeline structure of CMAC neural network. This learning algorithm can converge in one step.

See also [ edit ]

Notes [ edit ]

References [ edit ]

Bibliography [ edit ]

Neural Network là gì? Cách sử dụng của Neural Network

Neural Network là khái niệm được nhắc đến nhiều trong trí thông minh nhân tạo. Đây là chuỗi thuật toán, hỗ trợ tìm kiếm những thông tin trong tập hợp dữ liệu. Cùng Vietnix khám phá Neural Network là gì và lưu ý để áp dụng công nghệ này hiệu quả.

Neural Network là gì?

Neural Network là mạng lưới Nơ-ron nhân tạo. Đây là chuỗi thuật toán nhằm tìm kiếm quan hệ trong tập hợp dữ liệu hệ thống dựa theo cách thức hoạt động não bộ con người.

Mạng lưới Nơ-ron nhân tạo được xem là hệ thống liên kết các tế bào thần kinh nhân tạo về bản chất hoặc hữu cơ. Neural Networks thích ứng với mọi điều chỉnh từ đầu vào, cho ra kết quả đầu ra tốt nhất. Khái niệm này xuất phát từ lĩnh vực trí tuệ nhân tạo, phổ biến trong hệ thống các giao dịch điện tử hiện nay.

Neural Network là gì

>> Xem thêm: Deep Learning là gì? Tổng quan về Deep Learning từ A – Z

Đặc điểm của Neural Network

Neural Network có những đặc điểm đặc thù, chi tiết như sau:

Mạng lưới nơ-ron nhân tạo hoạt động như nơ-ron trong não bộ con người. Trong đó, mỗi nơ-ron là một hàm toán học, có chức năng thu thập và phân loại dữ liệu, thông tin theo cấu trúc chi tiết.

Neural Network tương đồng với những phương pháp thống kê theo đồ thị đường cong hoặc phân tích hồi quy. Để giải thích đơn giản nhất, bạn hãy hình dung Neural Network bao hàm các nút mạng liên kết với nhau.

Mỗi nút là một tập hợp tri giác, cấu tạo tương tự hàm hồi quy đa tuyến tính, được sắp xếp liên kết với nhau. Các lớp này sẽ thu thập thông tin, sau đó phân loại và phát tín hiệu đầu ra tương ứng.

Nắm rõ đặc điểm neural network để ứng dụng thành công

Kiến trúc mạng Neural Network

Mỗi một mạng lưới Nơ-ron nhân tạo là một Perceptron đa tầng, một Neural Network thường bao gồm 3 kiểu tầng cụ thể như sau:

Input Layer (tầng đầu vào): Nằm bên trái của hệ thống, bao gồm dữ liệu thông tin đầu vào.

Nằm bên trái của hệ thống, bao gồm dữ liệu thông tin đầu vào. Output Layer (tầng đầu ra): Nằm bên phải của hệ thống, bao gồm dữ liệu thông tin đầu ra.

Nằm bên phải của hệ thống, bao gồm dữ liệu thông tin đầu ra. Hidden Layer (tầng ẩn): Nằm ở giữa tầng đầu vào và đầu ra, thể hiện quá trình suy luận và xử lý thông tin của hệ thống.

Lưu ý: Mỗi Neural Network chỉ có một tầng input (tầng đầu vào) và output (tầng đầu ra) nhưng sẽ có nhiều hidden layer.

Cấu trúc mạng Neural Network

Mỗi nút mạng trong Neural Network là một Sigmoid neural. Thường các nút mạng này sẽ có hàm kích hoạt khác nhưng hiện tại đang áp dụng thuật toán đồng nhất để dễ dàng hoạt động hơn.

Ở mỗi tầng, số lượng sigmoid neural khác nhau tùy thuộc vào cách thức xử lý dữ liệu. Trong quá trình hoạt động, các chuyên gia sẽ để các tầng ẩn – hidden layer với số lượng nơ-ron khác nhau.

Các nơ-ron ở những tầng khác cũng sẽ liên kết với nhau tạo thành mạng lưới chặt chẽ và đầy đủ nhất. Khi đó, người dũng sẽ biết được độ lớn của mạng lưới dựa trên số lượng tầng và số lượng nơ-ron.

Các nốt mạng kết hợp theo một chiều duy nhất từ tầng đầu vào đến tầng đầu ra. Mỗi nốt ở một tầng sẽ nhận thông tin đầu của các nốt ở tầng trước đó.

Ứng dụng của Neural Network

Mạng Neuron được áp dụng trong nhiều lĩnh vực khác nhau như: Machine learning – máy học, tài chính, kinh doanh, lập kế hoạch mục tiêu, bảo trì sản phẩm, dự báo thời tiết, nghiên cứu tiếp thị, đánh giá rủi ro, phòng chống gian lận,…

Cụ thể trong lĩnh vực tài chính, Neural Network hỗ trợ các tác vụ như: Thuật toán giao dịch, dự đoán chuỗi thời gian, phân loại thống kê chứng khoán, xây dựng mô hình giảm rủi ro tín dụng, thiết lập các chỉ báo độc quyền hoặc các công cụ kiểm soát giá cả.

Ngoài ra, nơ-ron nhân tạo còn được sử dụng để phân tích giao dịch dựa trên dữ liệu lịch sử. Tuy sự chính xác của các dự báo còn cần thời gian nghiên cứu và kiểm chứng, nhưng không thể phủ nhận vai trò của Neural network hiện nay.

Ứng dụng neural network trong cuộc sống

Cách sử dụng Neural Network

Để ứng dụng thành công, ngoài việc bạn cần hiểu cách thức hoạt động Neural Network thì bạn còn cần có kinh nghiệm và kiến thức chuyên môn thực tế. Sau đây Vietnix sẽ hướng dẫn cách sử dụng Neural Network hiệu quả:

Lựa chọn mô hình phù hợp dựa theo đầu vào dữ liệu và các ứng dụng.

Lựa chọn thuật toán để xử lý thông tin giữa các nút mạng, không cần quá nhiều thời gian để thử nghiệm hoặc điều chỉnh.

Kết hợp hai lưu ý trên với ngân sách đầu tư hợp lý thì bạn có thể ứng dụng mạng Neuron thành công, ngay cả đối với các tập dữ liệu lớn.

Cách sử dụng Neural Network trong đời sống

Phân biệt các định nghĩa Neural Network

Nếu hoạt động trong lĩnh vực công nghệ thông tin, bạn sẽ thấy nhiều khái niệm tương tự nhau. Do vậy, bạn cần hiểu các khái niệm để không bị nhầm lẫn.

Convolutional Neural Network là gì?

Convolutional Neural Network (CNN) là khái niệm để chỉ mạng lưới nơ-ron tích chập, được sử dụng phổ biến trong mô hình Deep Learning (học sâu) để xử lý hệ thống thông tin chính xác. CNN được dùng phổ biến để nhận diện đối tượng trong ảnh.

Mạng lưới nơ-ron tích chập Convolution Neural Network

Artificial Neural Network là gì?

Artificial Neural Network là mạng lưới nơ-ron nhân tạo hay còn gọi là mô hình toán được xây dựng dựa trên các no-rơn sinh học.

Mạng lưới bao gồm nhiều nhóm làm việc, trong đó các nơ-ron sẽ kết nối và xử lý thông tin và tính toán dữ liệu tại các nút mạng.

Trong nhiều trường hợp, mạng lưới này có thể tự thay đổi cấu trúc dựa trên thông tin bên ngoài và bên trong. Ngoài ra, mạng lưới nơ-ron nhân tạo còn giúp mô hình hóa các dữ liệu thống kê phi tuyến tính, có mối quan hệ phức tạp.

Mạng lưới nơ – ron nhân tạo Artificial Neural Network

Ví dụ về mạng Neural là gì? Mạng lưới thần kinh được thiết kế để hoạt động giống như bộ não con người .

Trong trường hợp nhận dạng chữ viết tay hoặc nhận dạng khuôn mặt, bộ não rất nhanh chóng đưa ra một số quyết định.

Ví dụ, trong trường hợp nhận dạng khuôn mặt, não có thể bắt đầu bằng “Đó là nữ hay nam? Thiết bị có màu đen hay trắng? Nhân vật nhận diện già hay trẻ? Ai đang sử dụng mạng Neural? Mạng Neural Network là một chuỗi các thuật toán bắt chước các hoạt động của não người để nhận ra mối quan hệ giữa một lượng lớn dữ liệu.

Chúng được sử dụng trong nhiều ứng dụng khác nhau trong các dịch vụ tài chính, từ dự báo và nghiên cứu tiếp thị đến phát hiện gian lận và đánh giá rủi ro.

Lời kết

Với những thông tin hữu ích nhằm giải thích khái niệm neural network là gì, có thể khẳng định tiến bộ công nghệ này là một phần không thể thiếu trong cuộc sống. Nếu nắm bắt được cốt lõi của hệ thống, bạn sẽ ứng dụng và quản lý dễ dàng. Mọi thắc mắc liên quan, bạn vui lòng bình luận bên dưới để được hỗ trợ giải pháp nhé!

Artificial Neural Network Tutorial

next → Artificial Neural Network Tutorial Artificial Neural Network Tutorial provides basic and advanced concepts of ANNs. Our Artificial Neural Network tutorial is developed for beginners as well as professions. The term “Artificial neural network” refers to a biologically inspired sub-field of artificial intelligence modeled after the brain. An Artificial neural network is usually a computational network based on biological neural networks that construct the structure of the human brain. Similar to a human brain has neurons interconnected to each other, artificial neural networks also have neurons that are linked to each other in various layers of the networks. These neurons are known as nodes. Artificial neural network tutorial covers all the aspects related to the artificial neural network. In this tutorial, we will discuss ANNs, Adaptive resonance theory, Kohonen self-organizing map, Building blocks, unsupervised learning, Genetic algorithm, etc. What is Artificial Neural Network? The term “Artificial Neural Network” is derived from Biological neural networks that develop the structure of a human brain. Similar to the human brain that has neurons interconnected to one another, artificial neural networks also have neurons that are interconnected to one another in various layers of the networks. These neurons are known as nodes. The given figure illustrates the typical diagram of Biological Neural Network. The typical Artificial Neural Network looks something like the given figure. Dendrites from Biological Neural Network represent inputs in Artificial Neural Networks, cell nucleus represents Nodes, synapse represents Weights, and Axon represents Output. Relationship between Biological neural network and artificial neural network: Biological Neural Network Artificial Neural Network Dendrites Inputs Cell nucleus Nodes Synapse Weights Axon Output An Artificial Neural Network in the field of Artificial intelligence where it attempts to mimic the network of neurons makes up a human brain so that computers will have an option to understand things and make decisions in a human-like manner. The artificial neural network is designed by programming computers to behave simply like interconnected brain cells. There are around 1000 billion neurons in the human brain. Each neuron has an association point somewhere in the range of 1,000 and 100,000. In the human brain, data is stored in such a manner as to be distributed, and we can extract more than one piece of this data when necessary from our memory parallelly. We can say that the human brain is made up of incredibly amazing parallel processors. We can understand the artificial neural network with an example, consider an example of a digital logic gate that takes an input and gives an output. “OR” gate, which takes two inputs. If one or both the inputs are “On,” then we get “On” in output. If both the inputs are “Off,” then we get “Off” in output. Here the output depends upon input. Our brain does not perform the same task. The outputs to inputs relationship keep changing because of the neurons in our brain, which are “learning.” The architecture of an artificial neural network: To understand the concept of the architecture of an artificial neural network, we have to understand what a neural network consists of. In order to define a neural network that consists of a large number of artificial neurons, which are termed units arranged in a sequence of layers. Lets us look at various types of layers available in an artificial neural network. Artificial Neural Network primarily consists of three layers: Input Layer: As the name suggests, it accepts inputs in several different formats provided by the programmer. Hidden Layer: The hidden layer presents in-between input and output layers. It performs all the calculations to find hidden features and patterns. Output Layer: The input goes through a series of transformations using the hidden layer, which finally results in output that is conveyed using this layer. The artificial neural network takes input and computes the weighted sum of the inputs and includes a bias. This computation is represented in the form of a transfer function. It determines weighted total is passed as an input to an activation function to produce the output. Activation functions choose whether a node should fire or not. Only those who are fired make it to the output layer. There are distinctive activation functions available that can be applied upon the sort of task we are performing. Advantages of Artificial Neural Network (ANN) Parallel processing capability: Artificial neural networks have a numerical value that can perform more than one task simultaneously. Storing data on the entire network: Data that is used in traditional programming is stored on the whole network, not on a database. The disappearance of a couple of pieces of data in one place doesn’t prevent the network from working. Capability to work with incomplete knowledge: After ANN training, the information may produce output even with inadequate data. The loss of performance here relies upon the significance of missing data. Having a memory distribution: For ANN is to be able to adapt, it is important to determine the examples and to encourage the network according to the desired output by demonstrating these examples to the network. The succession of the network is directly proportional to the chosen instances, and if the event can’t appear to the network in all its aspects, it can produce false output. Having fault tolerance: Extortion of one or more cells of ANN does not prohibit it from generating output, and this feature makes the network fault-tolerance. Disadvantages of Artificial Neural Network: Assurance of proper network structure: There is no particular guideline for determining the structure of artificial neural networks. The appropriate network structure is accomplished through experience, trial, and error. Unrecognized behavior of the network: It is the most significant issue of ANN. When ANN produces a testing solution, it does not provide insight concerning why and how. It decreases trust in the network. Hardware dependence: Artificial neural networks need processors with parallel processing power, as per their structure. Therefore, the realization of the equipment is dependent. Difficulty of showing the issue to the network: ANNs can work with numerical data. Problems must be converted into numerical values before being introduced to ANN. The presentation mechanism to be resolved here will directly impact the performance of the network. It relies on the user’s abilities. The duration of the network is unknown: The network is reduced to a specific value of the error, and this value does not give us optimum results. Science artificial neural networks that have steeped into the world in the mid-20th century are exponentially developing. In the present time, we have investigated the pros of artificial neural networks and the issues encountered in the course of their utilization. It should not be overlooked that the cons of ANN networks, which are a flourishing science branch, are eliminated individually, and their pros are increasing day by day. It means that artificial neural networks will turn into an irreplaceable part of our lives progressively important. How do artificial neural networks work? Artificial Neural Network can be best represented as a weighted directed graph, where the artificial neurons form the nodes. The association between the neurons outputs and neuron inputs can be viewed as the directed edges with weights. The Artificial Neural Network receives the input signal from the external source in the form of a pattern and image in the form of a vector. These inputs are then mathematically assigned by the notations x(n) for every n number of inputs. Afterward, each of the input is multiplied by its corresponding weights ( these weights are the details utilized by the artificial neural networks to solve a specific problem ). In general terms, these weights normally represent the strength of the interconnection between neurons inside the artificial neural network. All the weighted inputs are summarized inside the computing unit. If the weighted sum is equal to zero, then bias is added to make the output non-zero or something else to scale up to the system’s response. Bias has the same input, and weight equals to 1. Here the total of weighted inputs can be in the range of 0 to positive infinity. Here, to keep the response in the limits of the desired value, a certain maximum value is benchmarked, and the total of weighted inputs is passed through the activation function. The activation function refers to the set of transfer functions used to achieve the desired output. There is a different kind of the activation function, but primarily either linear or non-linear sets of functions. Some of the commonly used sets of activation functions are the Binary, linear, and Tan hyperbolic sigmoidal activation functions. Let us take a look at each of them in details: Binary: In binary activation function, the output is either a one or a 0. Here, to accomplish this, there is a threshold value set up. If the net weighted input of neurons is more than 1, then the final output of the activation function is returned as one or else the output is returned as 0. Sigmoidal Hyperbolic: The Sigmoidal Hyperbola function is generally seen as an “S” shaped curve. Here the tan hyperbolic function is used to approximate output from the actual net input. The function is defined as: F(x) = (1/1 + exp(-????x)) Where ???? is considered the Steepness parameter. Types of Artificial Neural Network: There are various types of Artificial Neural Networks (ANN) depending upon the human brain neuron and network functions, an artificial neural network similarly performs tasks. The majority of the artificial neural networks will have some similarities with a more complex biological partner and are very effective at their expected tasks. For example, segmentation or classification. Feedback ANN: In this type of ANN, the output returns into the network to accomplish the best-evolved results internally. As per the University of Massachusetts, Lowell Centre for Atmospheric Research. The feedback networks feed information back into itself and are well suited to solve optimization issues. The Internal system error corrections utilize feedback ANNs. Feed-Forward ANN: A feed-forward network is a basic neural network comprising of an input layer, an output layer, and at least one layer of a neuron. Through assessment of its output by reviewing its input, the intensity of the network can be noticed based on group behavior of the associated neurons, and the output is decided. The primary advantage of this network is that it figures out how to evaluate and recognize input patterns. Prerequisite No specific expertise is needed as a prerequisite before starting this tutorial. Audience Our Artificial Neural Network Tutorial is developed for beginners as well as professionals, to help them understand the basic concept of ANNs. Problems We assure you that you will not find any problem in this Artificial Neural Network tutorial. But if there is any problem or mistake, please post the problem in the contact form so that we can further improve it. Next Topic Adaptive Resonance Theory

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What are Neural Networks?

Neural networks reflect the behavior of the human brain, allowing computer programs to recognize patterns and solve common problems in the fields of AI, machine learning, and deep learning.

What are neural networks?

Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.

Artificial neural networks (ANNs) are comprised of a node layers, containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network.

Neural networks rely on training data to learn and improve their accuracy over time. However, once these learning algorithms are fine-tuned for accuracy, they are powerful tools in computer science and artificial intelligence, allowing us to classify and cluster data at a high velocity. Tasks in speech recognition or image recognition can take minutes versus hours when compared to the manual identification by human experts. One of the most well-known neural networks is Google’s search algorithm.

How do neural networks work?

Think of each individual node as its own linear regression model, composed of input data, weights, a bias (or threshold), and an output. The formula would look something like this:

∑wixi + bias = w1x1 + w2x2 + w3x3 + bias

output = f(x) = 1 if ∑w1x1 + b>= 0; 0 if ∑w1x1 + b < 0 Once an input layer is determined, weights are assigned. These weights help determine the importance of any given variable, with larger ones contributing more significantly to the output compared to other inputs. All inputs are then multiplied by their respective weights and then summed. Afterward, the output is passed through an activation function, which determines the output. If that output exceeds a given threshold, it “fires” (or activates) the node, passing data to the next layer in the network. This results in the output of one node becoming in the input of the next node. This process of passing data from one layer to the next layer defines this neural network as a feedforward network. Let’s break down what one single node might look like using binary values. We can apply this concept to a more tangible example, like whether you should go surfing (Yes: 1, No: 0). The decision to go or not to go is our predicted outcome, or y-hat. Let’s assume that there are three factors influencing your decision-making: Are the waves good? (Yes: 1, No: 0) Is the line-up empty? (Yes: 1, No: 0) Has there been a recent shark attack? (Yes: 0, No: 1) Then, let’s assume the following, giving us the following inputs: X1 = 1, since the waves are pumping X2 = 0, since the crowds are out X3 = 1, since there hasn’t been a recent shark attack Now, we need to assign some weights to determine importance. Larger weights signify that particular variables are of greater importance to the decision or outcome. W1 = 5, since large swells don’t come around often W2 = 2, since you’re used to the crowds W3 = 4, since you have a fear of sharks Finally, we’ll also assume a threshold value of 3, which would translate to a bias value of –3. With all the various inputs, we can start to plug in values into the formula to get the desired output. Y-hat = (1*5) + (0*2) + (1*4) – 3 = 6 If we use the activation function from the beginning of this section, we can determine that the output of this node would be 1, since 6 is greater than 0. In this instance, you would go surfing; but if we adjust the weights or the threshold, we can achieve different outcomes from the model. When we observe one decision, like in the above example, we can see how a neural network could make increasingly complex decisions depending on the output of previous decisions or layers. In the example above, we used perceptrons to illustrate some of the mathematics at play here, but neural networks leverage sigmoid neurons, which are distinguished by having values between 0 and 1. Since neural networks behave similarly to decision trees, cascading data from one node to another, having x values between 0 and 1 will reduce the impact of any given change of a single variable on the output of any given node, and subsequently, the output of the neural network. As we start to think about more practical use cases for neural networks, like image recognition or classification, we’ll leverage supervised learning, or labeled datasets, to train the algorithm. As we train the model, we’ll want to evaluate its accuracy using a cost (or loss) function. This is also commonly referred to as the mean squared error (MSE). In the equation below, i represents the index of the sample, y-hat is the predicted outcome, y is the actual value, and m is the number of samples. 𝐶𝑜𝑠𝑡 𝐹𝑢𝑛𝑐𝑡𝑖𝑜𝑛= 𝑀𝑆𝐸=1/2𝑚 ∑129_(𝑖=1)^𝑚▒(𝑦 ̂^((𝑖) )−𝑦^((𝑖) ) )^2 Ultimately, the goal is to minimize our cost function to ensure correctness of fit for any given observation. As the model adjusts its weights and bias, it uses the cost function and reinforcement learning to reach the point of convergence, or the local minimum. The process in which the algorithm adjusts its weights is through gradient descent, allowing the model to determine the direction to take to reduce errors (or minimize the cost function). With each training example, the parameters of the model adjust to gradually converge at the minimum. See this IBM Developer article for a deeper explanation of the quantitative concepts involved in neural networks. Most deep neural networks are feedforward, meaning they flow in one direction only, from input to output. However, you can also train your model through backpropagation; that is, move in the opposite direction from output to input. Backpropagation allows us to calculate and attribute the error associated with each neuron, allowing us to adjust and fit the parameters of the model(s) appropriately. Types of neural networks Neural networks can be classified into different types, which are used for different purposes. While this isn’t a comprehensive list of types, the below would be representative of the most common types of neural networks that you’ll come across for its common use cases: The perceptron is the oldest neural network, created by Frank Rosenblatt in 1958. It has a single neuron and is the simplest form of a neural network: Feedforward neural networks, or multi-layer perceptrons (MLPs), are what we’ve primarily been focusing on within this article. They are comprised of an input layer, a hidden layer or layers, and an output layer. While these neural networks are also commonly referred to as MLPs, it’s important to note that they are actually comprised of sigmoid neurons, not perceptrons, as most real-world problems are nonlinear. Data usually is fed into these models to train them, and they are the foundation for computer vision, natural language processing, and other neural networks. Convolutional neural networks (CNNs) are similar to feedforward networks, but they’re usually utilized for image recognition, pattern recognition, and/or computer vision. These networks harness principles from linear algebra, particularly matrix multiplication, to identify patterns within an image. Recurrent neural networks (RNNs) are identified by their feedback loops. These learning algorithms are primarily leveraged when using time-series data to make predictions about future outcomes, such as stock market predictions or sales forecasting. Neural networks vs. deep learning Deep Learning and neural networks tend to be used interchangeably in conversation, which can be confusing. As a result, it’s worth noting that the “deep” in deep learning is just referring to the depth of layers in a neural network. A neural network that consists of more than three layers—which would be inclusive of the inputs and the output—can be considered a deep learning algorithm. A neural network that only has two or three layers is just a basic neural network. To learn more about the differences between neural networks and other forms of artificial intelligence, like machine learning, please read the blog post “AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What’s the Difference?” History of neural networks The history of neural networks is longer than most people think. While the idea of “a machine that thinks” can be traced to the Ancient Greeks, we’ll focus on the key events that led to the evolution of thinking around neural networks, which has ebbed and flowed in popularity over the years: 1943: Warren S. McCulloch and Walter Pitts published “A logical calculus of the ideas immanent in nervous activity (PDF, 1 MB) (link resides outside IBM)” This research sought to understand how the human brain could produce complex patterns through connected brain cells, or neurons. One of the main ideas that came out of this work was the comparison of neurons with a binary threshold to Boolean logic (i.e., 0/1 or true/false statements). 1958: Frank Rosenblatt is credited with the development of the perceptron, documented in his research, “The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain” (PDF, 1.6 MB) (link resides outside IBM). He takes McCulloch and Pitt’s work a step further by introducing weights to the equation. Leveraging an IBM 704, Rosenblatt was able to get a computer to learn how to distinguish cards marked on the left vs. cards marked on the right. 1974: While numerous researchers contributed to the idea of backpropagation, Paul Werbos was the first person in the US to note its application within neural networks within his PhD thesis (PDF, 8.1 MB) (link resides outside IBM). 1989: Yann LeCun published a paper (PDF, 5.7 MB) (link resides outside IBM) illustrating how the use of constraints in backpropagation and its integration into the neural network architecture can be used to train algorithms. This research successfully leveraged a neural network to recognize hand-written zip code digits provided by the U.S. Postal Service. Neural networks and IBM Cloud For decades now, IBM has been a pioneer in the development of AI technologies and neural networks, highlighted by the development and evolution of IBM Watson. Watson is now a trusted solution for enterprises looking to apply advanced natural language processing and deep learning techniques to their systems using a proven tiered approach to AI adoption and implementation. Watson uses the Apache Unstructured Information Management Architecture (UIMA) framework and IBM’s DeepQA software to make powerful deep learning capabilities available to applications. Utilizing tools like IBM Watson Studio, your enterprise can seamlessly bring open source AI projects into production while deploying and running models on any cloud. For more information on how to get started with deep learning technology, explore IBM Watson Studio and the Deep Learning service. Sign up for an IBMid and create your IBM Cloud account.

Thuật toán Artificial Neural Network

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Trong hướng dẫn machine learning này, chúng tôi sẽ đề cập đến các Thuật toán Mạng Neural hàng đầu. Các Thuật toán Neural Network này được sử dụng để learning Artificial Neural Network. Bài viết này cung cấp cho bạn kiến ​​thức sâu sắc về Gradient Descent, Evolution Algorithm và Genetic Algorithm trong Neural Network.

Các bài viết liên quan:

Vì vậy, chúng ta hãy bắt đầu tìm hiểu về các thuật toán Artificial Neural Network.

Các thuật toán Artificial Neural Network hàng đầu

Việc học Neural Network diễn ra trên cơ sở một mẫu dân số đang được nghiên cứu. Trong quá trình học, hãy so sánh giá trị mà đơn vị đầu ra mang lại với giá trị thực tế. Sau đó, điều chỉnh trọng số của tất cả các đơn vị để cải thiện dự đoán.

Có rất nhiều Thuật toán Neural Network có sẵn để learning Artificial Neural Network. Bây giờ chúng ta hãy xem một số Thuật toán quan trọng để learning Artificial Neural Network:

Gradient Descent – Được sử dụng để tìm điểm tối thiểu cục bộ của một hàm.

Evolution Algorithm – Dựa trên khái niệm chọn lọc tự nhiên hoặc sự tồn tại của những sinh vật khỏe mạnh nhất trong Sinh học.

Genetic Algorithm – Bật các quy tắc thích hợp nhất cho giải pháp của một vấn đề và chọn nó. Vì vậy, họ gửi ‘vật liệu di truyền’ của mình tới các quy tắc ‘con’. Chúng ta cùng tìm hiểu chi tiết về chúng qua bài viết dưới đây.

Xem phần giới thiệu về các quy tắc học tập trong Neural Network để hiểu thêm về các Thuật toán Neural Network.

Xem thêm Convolutional Neural Networks trong machine learning

Gradient Descent

Chúng tôi sử dụng thuật toán Gradient Descent để tìm giá trị nhỏ nhất cục bộ của một hàm. Thuật toán Neural Network hội tụ về giá trị nhỏ nhất cục bộ. Bằng cách tiếp cận tỷ lệ với âm của gradient của hàm. Để tìm cực đại cục bộ, hãy thực hiện các bước tỷ lệ với gradient dương của hàm. Đây là một quá trình tăng dần độ dốc.

Trong mô hình tuyến tính, bề mặt lỗi được xác định rõ ràng và đối tượng toán học nổi tiếng có hình dạng của một parabol. Sau đó tìm điểm nhỏ nhất bằng phép tính. Không giống như mô hình tuyến tính, mạng nơron là mô hình phi tuyến phức tạp. Ở đây, bề mặt lỗi có bố cục không đều, đan xen với những ngọn đồi, thung lũng, cao nguyên và khe núi sâu. Để tìm điểm cuối cùng trên bề mặt này mà không có bản đồ nào có sẵn, người dùng phải khám phá nó.

Trong Thuật toán Artificial Neural Network này, bạn di chuyển qua bề mặt lỗi bằng cách đi theo đường có độ dốc lớn nhất. Nó cũng cung cấp khả năng đạt đến điểm thấp nhất có thể. Sau đó, bạn phải tính ra ở tốc độ tối ưu mà bạn nên đi xuống dốc.

Tốc độ chính xác tỷ lệ với độ dốc của bề mặt và tốc độ học. Tỷ lệ học tập kiểm soát mức độ thay đổi của các trọng số trong quá trình học tập.

Do đó, thời điểm của Neural Network có thể ảnh hưởng đến hiệu suất của perceptron nhiều lớp.

Xem thêm Computer Network là gì? kiến thức cơ bản

Evolution Algorithm

Thuật toán này dựa trên khái niệm chọn lọc tự nhiên hoặc sự tồn tại của những sinh vật khỏe mạnh nhất trong Sinh học. Khái niệm chọn lọc tự nhiên nói rằng – đối với một quần thể nhất định, các điều kiện môi trường sử dụng một áp lực dẫn đến sự gia tăng của những người khỏe mạnh nhất trong quần thể đó.

Để đo lường mức độ phù hợp nhất trong một tập hợp nhất định, bạn có thể áp dụng một hàm làm thước đo trừu tượng.

Trong ngữ cảnh của các thuật toán tiến hóa, hãy gọi sự tái tổ hợp như một toán tử. Sau đó, áp dụng nó cho hai hoặc nhiều ứng viên được gọi là cha mẹ, và kết quả là một trong những ứng viên mới được gọi là trẻ em. Áp dụng đột biến trên một ứng cử viên duy nhất và kết quả là tạo ra một ứng cử viên mới. Bằng cách áp dụng tái tổ hợp và đột biến, chúng ta có thể có được một tập hợp các ứng cử viên mới để xếp vào thế hệ tiếp theo dựa trên số đo phù hợp nhất của họ.

Hai yếu tố cơ bản của các thuật toán tiến hóa trong Artificial Neural Network là:

Toán tử biến thể (tái tổ hợp và đột biến)

Quá trình lựa chọn (lựa chọn phù hợp nhất)

Các đặc điểm chung của thuật toán tiến hóa là:

Các thuật toán tiến hóa dựa trên dân số.

Các thuật toán tiến hóa sử dụng các ứng viên hỗn hợp tái tổ hợp của một quần thể và tạo ra các ứng cử viên mới.

Dựa trên thuật toán tiến hóa lựa chọn ngẫu nhiên.

Do đó, trên cơ sở các chi tiết và các vấn đề áp dụng, chúng tôi sử dụng các định dạng khác nhau của các thuật toán tiến hóa.

Một số thuật toán tiến hóa phổ biến là:

Genetic Algorithm Genetic Algorithm – Nó cung cấp giải pháp cho các bài toán tối ưu hóa. Nó cung cấp giải pháp với sự trợ giúp của các quá trình tiến hóa tự nhiên. Như đột biến, tái tổ hợp, trao đổi chéo và di truyền.

Genetic Programming – Lập trình di truyền cung cấp một giải pháp dưới dạng chương trình máy tính. Bằng khả năng giải các bài toán tính toán độ chính xác của một chương trình.

Evolutionary Programming – Trong một môi trường giả lập để phát triển AI, chúng tôi sử dụng nó.

Evolution Strategy Nó là một thuật toán tối ưu hóa. Dựa trên các khái niệm về sự thích nghi và sự tiến hóa trong khoa học sinh học.

Neuroevolution – Để learning mạng lưới thần kinh, chúng tôi sử dụng Neuroevolution. Bằng cách xác định cấu trúc và trọng số kết nối mà bộ gen sử dụng để phát triển Neural Network.

Trong tất cả các Thuật toán Mạng Neural này, một Genetic Algorithm là thuật toán tiến hóa phổ biến nhất.

Xem thêm Network Layer trong TCP/IP hay OSI

Genetic Algorithm

Các Genetic Algorithm, được phát triển bởi nhóm của John Holland từ đầu những năm 1970. Nó cho phép các quy tắc thích hợp nhất để giải pháp của một vấn đề được lựa chọn. Để họ gửi “vật liệu di truyền” (các biến và danh mục của chúng) tới các quy tắc “con”.

Ở đây tham khảo một tập hợp các loại biến tương tự. Ví dụ, khách hàng trong độ tuổi từ 36 đến 50, có tài sản tài chính dưới 20.000 đô la và thu nhập hàng tháng trên 2000 đô la.

Quy tắc là phần bằng nhau của một nhánh của cây quyết định; nó cũng tương tự như một gen. Bạn có thể hiểu gen là đơn vị bên trong tế bào điều khiển cách sinh vật sống thừa hưởng các đặc điểm của bố mẹ. Vì vậy, các Genetic Algorithm nhằm mục đích tái tạo các cơ chế của chọn lọc tự nhiên. Bằng cách chọn các quy tắc phù hợp nhất với dự đoán và bằng cách lai và biến đổi chúng cho đến khi có được một mô hình dự đoán.

Cùng với Neural Network, chúng tạo thành loại thuật toán thứ hai. Cơ chế nào bắt chước các cơ chế tự nhiên để giải thích các hiện tượng không nhất thiết tự nhiên.

Các bước để thực hiện các Genetic Algorithm là:

Bước 1: Tạo ngẫu nhiên các quy tắc ban đầu – Tạo các quy tắc đầu tiên với điều kiện ràng buộc là chúng phải khác biệt. Mỗi quy tắc chứa một số biến ngẫu nhiên do người dùng chọn.

Bước 2: Lựa chọn các quy tắc tốt nhất – Kiểm tra các Quy tắc xem mục đích theo chức năng thể dục để hướng dẫn sự tiến hóa theo các quy tắc tốt nhất. Các quy tắc tốt nhất tối đa hóa chức năng thể dục và giữ lại với xác suất tăng khi quy tắc được cải thiện. Một số quy tắc sẽ biến mất trong khi những quy tắc khác chọn nhiều lần.

Bước 3: Tạo ra các quy tắc mới bằng cách đột biến hoặc lai – Đầu tiên, chuyển sang bước 2 cho đến khi việc thực thi thuật toán dừng lại. Các quy tắc được lựa chọn bị đột biến hoặc vượt qua một cách ngẫu nhiên. Đột biến là sự thay thế một biến hoặc một phạm trù của quy tắc gốc bằng một biến khác.

Sự giao nhau giữa 2 quy tắc là việc trao đổi một số biến hoặc danh mục của chúng để tạo ra 2 quy tắc mới. Phép lai phổ biến hơn đột biến.

Thuật toán Artificial Neural Network kết thúc khi 1 trong 2 điều kiện sau đáp ứng:

Đã đạt đến số lần lặp được chỉ định.

Bắt đầu từ thế hệ thứ hạng n, các quy tắc của thế hệ n, n-1 và n-2 là (gần như) giống hệt nhau.

Vì vậy, đây là tất cả về Thuật toán Artificial Neural Network. Hy vọng bạn thích giải thích của chúng tôi.

Kết luận

Do đó, Mạng Neural Nhân tạo thường khó định cấu hình và learning chậm, nhưng một khi đã chuẩn bị thì ứng dụng sẽ rất nhanh. Chúng thường được thiết kế như các mô hình để vượt qua các vấn đề toán học, tính toán và kỹ thuật. Kể từ đó, có rất nhiều nghiên cứu trong toán học, sinh học thần kinh và khoa machine learning tính.

Nếu bạn muốn chia sẻ ý kiến ​​của mình và có bất kỳ câu hỏi nào về Thuật toán mạng nơron nhân tạo, vui lòng thực hiện trong phần bình luận.

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키워드에 대한 정보 artificial neural network

다음은 Bing에서 artificial neural network 주제에 대한 검색 결과입니다. 필요한 경우 더 읽을 수 있습니다.

이 기사는 인터넷의 다양한 출처에서 편집되었습니다. 이 기사가 유용했기를 바랍니다. 이 기사가 유용하다고 생각되면 공유하십시오. 매우 감사합니다!

사람들이 주제에 대해 자주 검색하는 키워드 Neural Network In 5 Minutes | What Is A Neural Network? | How Neural Networks Work | Simplilearn

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YouTube에서 artificial neural network 주제의 다른 동영상 보기

주제에 대한 기사를 시청해 주셔서 감사합니다 Neural Network In 5 Minutes | What Is A Neural Network? | How Neural Networks Work | Simplilearn | artificial neural network, 이 기사가 유용하다고 생각되면 공유하십시오, 매우 감사합니다.

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