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개요

chain rule
gradient descent
neural network architectures
backpropagation

memo

노트

Artificial neural networks (ANN) are a buzzword in machine learning right now — both for the technical expert and the everyday user.^[1]
Today, ANNs are growing in popularity due to the increased amount of data and computing power available.^[1]
The input layer represents the data that we feed the ANN.^[1]
ANNs are used in not only cutting edge machine learning applications, but in situations and applications that have been around for decades.^[1]
An ANN with the name of MADALINE was actually the first one ever applied to a real world problem back in 1959.^[1]
For this application, the ANN must be trained to accurately understand what people with different voices and accents are saying.^[1]
Today, there are multiple programs that can build out ANNs for you.^[1]
ANNs depend highly on activation functions, which allow them to follow a non-linear model and learn data very quickly.^[1]
A deep understanding of these concepts is needed to build and implement an ANN.^[1]
A typical artificial neural network will likely only have two or three hidden layers of nodes.^[2]
Artificial neural networks don’t use task-specific rules or linear reasoning.^[2]
So, the ANN gets lots of input along with the answers it should come up with.^[2]
Asking ‘what is an artificial neural network’ invites a host of complex answers.^[2]
Unlike other machine learning algorithms, which may organize data or crunch numbers, neural networks learn from experience.^[3]
Remember the single hidden layer in the artificial neural network?^[3]
Many of the biggest advances in AI are driven by artificial neural networks.^[4]
These ANNs are capable of extracting complex patterns from data, applying these patterns to unseen data to classify/recognize the data.^[4]
Deep neural networks take the basic form of the MLP and make it larger by adding more hidden layers in the middle of the model.^[4]
The multiple hidden layers of a deep neural network are able to interpret more complex patterns than the traditional multilayer perceptron.^[4]
Different layers of the deep neural network learn the patterns of different parts of the data.^[4]
A Convolutional Neural Network is a special type of neural network that is adept at interpreting the patterns found within images.^[4]
On the other hand, artificial neural networks are built on the principle of bio-mimicry.^[5]
The development of new algorithms to model such processes is needed, and ANNs can play a major role.^[6]
The main topic of this article will be the extended version of neural networks, known as deep learning.^[7]
ANNs consist of many interconnected computing units, called neurons, and are functional approximates that map inputs to outputs.^[7]
An artificial neural network (ANN) is similar, but a computing network in science that resembles the properties of the human brain.^[7]
Most neural networks today are organized in layers of nodes, and each node moves meaningfully within and outside the network.^[7]
ANNs are function approximators, mapping inputs to outputs, and are composed of many interconnected computational units, called neurons.^[8]
This tutorial provides an introduction to ANNs and discusses a few key features to consider.^[8]
This tutorial provides a high level overview of ANNs, an analytic technique that is currently undergoing rapid development and research.^[8]
A brief description of the biologic neuron, which ANNs attempt to mimic.^[8]
How ANNs learn: Introducing the back-propagation algorithm.^[8]
The output signal then moves to a raw output or other neurons depending on specific ANN architecture.^[8]
ANNs are often described as having an Input layer, Hidden layer, and Output layer.^[8]
Within the hidden layer is where a majority of the ‘learning’ takes place, and the output layer displays the results of the ANN.^[8]
Each of the black lines with correspond to a weight, , and describe how artificial neurons are connections to one another within the ANN.^[8]
top-left and top-right plots show two possible ANN configurations.^[8]
In the top-right ANN we have a network with two hidden layers.^[8]
Activation functions enable the ANN to learn non-linear properties present in the data.^[8]
The output ( ) can feed into the output layer of a neural network, or in deeper architectures may feed into additional hidden layers.^[8]
The choice of the activation function governs the required data scaling necessary for ANN analysis.^[8]
We have described the structure of ANNs, however, we have not touched on how these networks learn.^[8]
To begin training our notional single-layer one-neuron neural network we initially randomly assign weights.^[8]
We then run the neural network with the random weights and record the outputs generated.^[8]
Once we have our ANN output values ( ) we can compare them to the data set output values ( ).^[8]
Recall that our neural network is simply a function, .^[8]
Once the weights are updated, we can re-run the neural network with the update weight values.^[8]
The back-propagation algorithm (described in the previous paragraphs) is the fundamental process by which an ANN learns.^[8]
Given a ANN, back-propagation requires operations for -hidden layers, and operations for the number of input weights.^[8]
ANN hyperparameters are settings used to control how a neural network performs.^[8]
Hyperparameters dictate how well neural networks are able to learn the underlying functions they approximate.^[8]
Generally, when ANNs are developed they are evaluated against one data set that has been split into a training data set and a test data set.^[8]
When testing ANN hyperparameters we generally see multiple ANNs created with different hyperparameters trained on the training data set.^[8]
When testing ANNs we are concerned with two types of error, under-fitting and over-fitting.^[8]
An ANN exhibiting under-fitting is a neural network in which the error rate of the training data set is very high.^[8]
An ANN exhibiting over-fitting has a large gap between the error rates on the training data set and the error rates on the test data set.^[8]
Adjusting these ANN hyperparameters is an adjustment of the neural networks capacity.^[8]
An over-capacity ANN is likely to show over-fitting when tested against the test data set.^[8]
Next you’ll learn how to apply ANNs to predict continuous and categorical outcomes.^[8]
The first artificial neural network was invented in 1958 by psychologist Frank Rosenblatt.^[9]
Artificial neural networks typically start out with randomized weights for all their neurons.^[9]
A back-propagation ANN, conversely, is trained by humans to perform specific tasks.^[9]
During the training period, the teacher evaluates whether the ANN's output is correct.^[9]
Implemented on a single computer, an artificial neural network is typically slower than a more traditional algorithmic solution.^[9]
The parallel architecture also allows ANNs to process very large amounts of data very efficiently.^[9]
Artificial neural networks have proved useful in a variety of real-world applications that deal with complex, often incomplete data.^[9]
In addition, recent programs for text-to-speech have utilized ANNs.^[9]
ANNs are used to discover other kinds of crime, too.^[9]
Bomb detectors in many U.S. airports use ANNs to analyze airborne trace elements to sense the presence of explosive chemicals.^[9]
A neural network breaks down the input into layers of abstraction.^[10]
Understanding the human brain is critical for understanding ANN.^[11]
With ANN, artificial systems mimic the same functionality of the human brain.^[11]
ANNs are used in a number of applications today.^[11]
These artificial neural networks try to replicate only the most basic elements of this complicated, versatile, and powerful organism.^[12]
They are significantly more complex than the existing artificial neurons that are built into today's artificial neural networks.^[12]
To do this, the basic unit of neural networks, the artificial neurons, simulate the four basic functions of natural neurons.^[12]
All artificial neural networks are constructed from this basic building block - the processing element or the artificial neuron.^[12]
Basically, all artificial neural networks have a similar structure or topology as shown in Figure 2.4.1.^[12]
Definition - What does Artificial Neural Network (ANN) mean?^[13]
For a more detailed introduction to neural networks, Michael Nielsen’s Neural Networks and Deep Learning is a good place to start.^[14]
We’ve open sourced it on GitHub with the hope that it can make neural networks a little more accessible and easier to learn.^[14]
Artificial neural networks (ANNs) are comprised of a node layers, containing an input layer, one or more hidden layers, and an output layer.^[15]
Most deep neural networks are feedforward, meaning they flow in one direction only, from input to output.^[15]
The perceptron is the oldest neural network, created by Frank Rosenblatt in 1958.^[15]
Feedforward neural networks, or multi-layer perceptrons (MLPs), are what we’ve primarily been focusing on within this article.^[15]
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.^[15]
The history of neural networks is longer than most people think.^[15]
Neural network, a computer program that operates in a manner inspired by the natural neural network in the brain.^[16]
The objective of such artificial neural networks is to perform such cognitive functions as problem solving and machine learning.^[16]
In 1954 Belmont Farley and Wesley Clark of the Massachusetts Institute of Technology succeeded in running the first simple neural network.^[16]
The primary appeal of neural networks is their ability to emulate the brain’s pattern-recognition skills.^[16]
The output of a neural network depends on the weights of the connections between neurons in different layers.^[16]
Artificial neural networks can also be thought of as learning algorithms that model the input-output relationship.^[17]
An artificial neural network transforms input data by applying a nonlinear function to a weighted sum of the inputs.^[17]
For example, a neural network performing lane detection in a car needs to have low latency and a small runtime application.^[17]
Connectionist models of human perception and cognition utilize artificial neural networks.^[17]
The first, middle, and last layers of a neural network are called the input layer, hidden layer, and output layer respectively.^[17]
Artificial neural networks use different layers of mathematical processing to make sense of the information it’s fed.^[18]
The majority of neural networks are fully connected from one layer to another.^[18]
In order for ANNs to learn, they need to have a tremendous amount of information thrown at them called a training set.^[18]
There are several ways artificial neural networks can be deployed including to classify information, predict outcomes and cluster data.^[18]
Google uses a 30-layered neural network to power Google Photos as well as to power its “watch next” recommendations for YouTube videos.^[18]
Facebook uses artificial neural networks for its DeepFace algorithm, which can recognise specific faces with 97% accuracy.^[18]
Generally, the working of a human brain by making the right connections is the idea behind ANNs.^[19]
Basically, we can consider ANN as nonlinear statistical data.^[19]
ANN stands for Artificial Neural Networks.^[19]
Although, the structure of the ANN affected by a flow of information.^[19]
In this ANN Tutorial, we will learn Artificial Neural Network.^[19]
Here, we will explore the working and structures of ANN.^[19]
As a result, we can say that ANNs are composed of multiple nodes.^[19]
In this particular Artificial Neural Network, it allows feedback loops.^[19]
Artificial Neural Network used to perform a various task.^[19]
Aerospace Generally, we use ANN a for Autopilot aircrafts.^[19]
In various ways, we use ANN an in the military.^[19]
Basically , we use an Artificial neural network in electronics in many ways.^[19]
Thus, we use an Artificial neural network in many ways.^[19]
Generally, we use an Artificial neural network in transportation in many ways.^[19]
It also uses an ANN in pattern Recognition.^[19]
Yes, that’s why there is a need to use big data in training neural networks.^[20]
A neural network is a network of artificial neurons programmed in software.^[20]
Let’s take an example of a neural network that is trained to recognize dogs and cats.^[20]
Another common question I see floating around – neural networks require a ton of computing power, so is it really worth using them?^[21]
As you can see here, ANN consists of 3 layers – Input, Hidden and Output.^[21]
Artificial Neural Network is capable of learning any nonlinear function.^[21]
ANN loses the spatial features of an image.^[21]
In this article, I have discussed the importance of deep learning and the differences among different types of neural networks.^[21]
The input layer is where rules are predetermined and representative examples are given to show the ANN what the output should look like.^[22]
Looking at an analogy may be helpful in understanding neural networks better.^[22]
Most deep learning methods use neural network architectures, which is why it is often referred to as deep neural networks.^[22]
These inputs create electric impulses, which quickly travel through the neural network.^[23]
ANNs are composed of multiple nodes, which imitate biological neurons of human brain.^[23]
ANNs are capable of learning, which takes place by altering weight values.^[23]
It involves a teacher that is scholar than the ANN itself.^[23]
The ANN comes up with guesses while recognizing.^[23]
Then the teacher provides the ANN with the answers.^[23]
The ANN makes a decision by observing its environment.^[23]
The first trainable neural network, the Perceptron, was demonstrated by the Cornell University psychologist Frank Rosenblatt in 1957.^[24]
The recent resurgence in neural networks — the deep-learning revolution — comes courtesy of the computer-game industry.^[24]
Artificial neural networks (ANNs) are computational models that are loosely inspired by their biological counterparts.^[25]
In recent years, major breakthroughs in ANN research have transformed the machine learning landscape from an engineering perspective.^[25]
At the same time, scientists have started to revisit ANNs as models of neural information processing in biological agents.^[25]
We welcome contributions that are of direct relevance to neuroscientists that use ANNs as a model of neural information processing.^[25]
As you can see, with neural networks, we’re moving towards a world of fewer surprises.^[26]
This is because a neural network is born in ignorance.^[26]
Now, that form of multiple linear regression is happening at every node of a neural network.^[26]
While neural networks working with labeled data produce binary output, the input they receive is often continuous.^[26]
Artificial intelligence (AI) pyramid illustrates the evolution of ML approach to ANN and leading to deep learning (DL).^[27]
The basic concept of node and neutron has been explained, with the help of diagrams, leading to the ANN model and its operation.^[27]
Literature depicts that ML, ANN and deep learning (DL) falls under the pyramid of AI and shown in Figure 1.^[27]
Under ANN, DL has gained much importance among researchers.^[27]
DL is a complex network set of ANN with various layers of processing, which improves the results by developing high levels of insight.^[27]
The origins of all the work on ANN are in neurobiological studies that date back to about a century ago.^[27]
A brief overview of evolution in ANN and significant milestones are shown in the timeline, as shown in Figures 3 and 4.^[27]
Literature depicts that, in the 1980s, very few researchers were working on deep NNs, and it gained popularity in the early 1990s.^[27]
Since then, a large number of research articles have been published on applications of ANN and this journey is on-going.^[27]
The architecture of ANN is stimulated by the framework of biological neurons, like in the human brain.^[27]
Likewise, the ANN is a framework of interlinked nodes, similar to neurons, forming a network model.^[27]
ANN operations are not based on explicit rules and outputs are generated by trial and error procedures through sequential computations.^[27]
To comprehend the basic structure of ANN, firstly, the understanding of ‘node’ is necessary.^[27]
Figure 6 represents the general model of ANN, which is stimulated by a biological neuron.^[27]
Figure 6 shows that there are three layers in ANN called the input layer, the output layer and the hidden layer.^[27]
In the ANN, the processing part is performed in the hidden layer.^[27]
Generally speaking, each ANN has three main components, i.e., node character, network topology and the learning rules.^[27]
The interconnecting network model, between the nodes of ANN, with each other, is called the topology (or architecture).^[27]
ANN is composed of input layers, hidden layers and output layers, as already discussed in Figure 6.^[27]
A single-layer ANN, with a single output, is known as Perceptron.^[27]
A conceptual model for layers and ANN topology is shown in Figure 7.^[27]
Also, it can be seen that there is L number of hidden layers in the ANN model.^[27]
‘i’ as node number, i.e., from 1 to i. Y is the output for the mentioned ANN model.^[27]
3.2.1 Perceptron and multi-layer architectures A single-layered ANN, with a single output, is known as the perceptron.^[27]
Multi-layer perceptrons (MLPs) are the most commonly used architecture for ANN.^[27]
Figure 9 shows the ANN model for feedback network connections.^[27]
The training of the ANN is accomplished through a learning process.^[27]
In this process, the ANN model adjusts its weights, against the supplied inputs, thus producing outputs similar to inputs.^[27]
Unsupervised ANN models are used in diagnosing diseases, image segmentation and many more.^[27]
The primary reason for ANN popularity is due to approximated data output.^[27]
There are five main steps for the approximation function in the ANN model, as given below.^[27]
During the training process, ANN might suffer from the overfitting and underfitting.^[27]
5.4 Simulation Simulation is the ultimate goal of applying ANN networks.^[27]
It is the representation of predicted output data for an ANN model.^[27]
The validation set is used to inform the ANN when training is to be terminated (when the minimum error point is achieved).^[27]
The test set provides an entirely independent way of examining the precision of the ANN.^[27]
The test set is a set of sample data that is used for the evaluation of the ANN model.^[27]
The biases and weights are the parameters of the network that are required to be adjusted before operating an ANN.^[27]
These parameters can be modified by using either supervised or unsupervised approach for any ANN model.^[27]
For training purpose, the supervised learning process is generally considered for determining biases and weights of an ANN network.^[27]
The supervised training process of an ANN network could be attained by using delta rule.^[27]
The backpropagation algorithm is mostly used for the application of delta rule for the training process of an ANN.^[27]
The ANN training can be achieved either by batch training or incremental training.^[27]
CNNs are very much similar to ANN that can be observed as the acyclic graph in the form of a well-arranged collection of neurons.^[27]
7.2 Recurrent neural network (RNN) RNNs are used for the tasks that require consecutive sequential inputs for processing.^[27]
A simple ANN model was developed using Python.^[27]
Conclusions Operation of the ANN model is the simulation of the human brain, and they fall under the knowledge domain of AI.^[27]
The popularity of ANN models were increased in the early 1990s, and many studies have been done since.^[27]
The basic ANN model has three main layers, and the main process is performed in the middle layer known as the hidden layer.^[27]
The output of the ANN model is very much dependent on the characteristics and function it carries under the hidden layer.^[27]
The ANN models can perform supervised learning as well as unsupervised learning depending upon the task.^[27]
Output accuracy of the ANN models is very much dependent on the number of hidden layers and the number of epochs.^[27]
This ANN technology, combined with other advanced and AI knowledge areas, is making life easier in almost every domain.^[27]
In other words, the neural network uses the examples to automatically infer rules for recognizing handwritten digits.^[28]
In this chapter we'll write a computer program implementing a neural network that learns to recognize handwritten digits.^[28]
We're focusing on handwriting recognition because it's an excellent prototype problem for learning about neural networks in general.^[28]
But how can we devise such algorithms for a neural network?^[28]
In the next section I'll introduce a neural network that can do a pretty good job classifying handwritten digits.^[28]
Up to now, we've been discussing neural networks where the output from one layer is used as input to the next layer.^[28]
Having defined neural networks, let's return to handwriting recognition.^[28]
To understand why we do this, it helps to think about what the neural network is doing from first principles.^[28]
Now that we have a design for our neural network, how can it learn to recognize digits?^[28]
We'll use the test data to evaluate how well our neural network has learned to recognize digits.^[28]
So for now we're going to forget all about the specific form of the cost function, the connection to neural networks, and so on.^[28]
the biggest neural networks have cost functions which depend on billions of weights and biases in an extremely complicated way.^[28]
How can we apply gradient descent to learn in a neural network?^[28]
In online learning, a neural network learns from just one training input at a time (just as human beings do).^[28]
The centerpiece is a Network class, which we use to represent a neural network.^[28]
"""Train the neural network using mini-batch stochastic gradient descent.^[28]
""Return the number of test inputs for which the neural network outputs the correct result.^[28]
The transcript shows the number of test images correctly recognized by the neural network after each epoch of training.^[28]
We might worry not only about the learning rate, but about every other aspect of our neural network.^[28]
Or maybe it's impossible for a neural network with this architecture to learn to recognize handwritten digits?^[28]
You need to learn that art of debugging in order to get good results from neural networks.^[28]
This is a nice data format, but for use in neural networks it's helpful to modify the format of the ``training_data`` a little.^[28]
Based on ``load_data``, but the format is more convenient for use in our implementation of neural networks.^[28]
Indeed, it means that the SVM is performing roughly as well as our neural networks, just a little worse.^[28]
At present, well-designed neural networks outperform every other technique for solving MNIST, including SVMs.^[28]
While our neural network gives impressive performance, that performance is somewhat mysterious.^[28]
To put these questions more starkly, suppose that a few decades hence neural networks lead to artificial intelligence (AI).^[28]
Processing units make up ANNs, which in turn consist of inputs and outputs.^[29]
Artificial neural networks are built like the human brain, with neuron nodes interconnected like a web.^[29]
An ANN has hundreds or thousands of artificial neurons called processing units, which are interconnected by nodes.^[29]
An ANN initially goes through a training phase where it learns to recognize patterns in data, whether visually, aurally, or textually.^[29]
Artificial neural networks are paving the way for life-changing applications to be developed for use in all sectors of the economy.^[29]
Artificial intelligence platforms that are built on ANNs are disrupting the traditional ways of doing things.^[29]
Artificial neural networks have been applied in all areas of operations.^[29]
ANNs have been highly efficient in offering solutions to problems, where traditional models have failed or are very complicated to build.^[30]
Due to the nonlinear nature of the ANNs, they are able to express much more complex phenomena than some linear modeling techniques.^[30]
One of the most critical aspects of the use of ANN as a modeling tool is the level of knowledge needed.^[30]
In general, limited expertise exists in modeling with ANN for practical applications.^[30]
The ANN is one of many versatile tools to meet the demand in drug discovery modeling.^[31]
Compared to a traditional regression approach, the ANN is capable of modeling complex nonlinear relationships.^[31]
If you’ve spent any time reading about artificial intelligence, you’ll almost certainly have heard about artificial neural networks.^[32]
Artificial neural networks are one of the main tools used in machine learning.^[32]
There are multiple types of neural network, each of which come with their own specific use cases and levels of complexity.^[32]
In the same way that we learn from experience in our lives, neural networks require data to learn.^[32]
In most cases, the more data that can be thrown at a neural network, the more accurate it will become.^[32]
When researchers or computer scientists set out to train a neural network, they typically divide their data into three sets.^[32]
The biggest issue, however, is that neural networks are “black boxes,” in which the user feeds in data and receives answers.^[32]
A hyperparameter is a setting that affects the structure or operation of the neural network.^[33]
When training neural networks, like in other machine learning techniques, we try to balance between bias and variance.^[33]
Another meaning of bias is a “ bias neuron ” which is used in every layer of the neural network.^[33]
Source data fed into the neural network, with the goal of making a decision or prediction about the data.^[33]
Artificial Neural Networks (ANN) is a supervised learning system built of a large number of simple elements, called neurons or perceptrons.^[33]
However, for large neural networks, a training algorithm is needed that is very computationally efficient.^[33]
In a neural network, inputs, which are typically real values, are fed into the neurons in the network.^[33]
An activation function is a mathematical equation that determines the output of each element (perceptron or neuron) in the neural network.^[33]
Classic activation functions used in neural networks include the step function (which has a binary input), sigmoid and tanh.^[33]
Underfitting happens when the neural network is not able to accurately predict for the training set, not to mention for the validation set.^[33]
A high bias means the neural network is not able to generate correct predictions even for the examples it trained on.^[33]
In each layer of the neural network, a bias neuron is added, which simply stores a value of 1.^[33]
To understand classification with neural networks let’s cover some other common classification algorithms.^[33]
For certain classification problems, neural networks can provide improved performance compared to other algorithms.^[33]
Essentially, any regression equation can be modeled by a neural network.^[33]
Can you use a neural network to run a regression?^[33]
The short answer is yes – neural networks can generate a model that approximates any regression function.^[33]
A Recurrent Neural Network (RNN) helps neural networks deal with input data that is sequential in nature.^[33]
An artificial neural network is an interconnected group of nodes, inspired by a simplification of neurons in a brain .^[34]
Successive adjustments will cause the neural network to produce output which is increasingly similar to the target output.^[34]
An artificial neural network consists of a collection of simulated neurons.^[34]
ANNs are composed of artificial neurons which are conceptually derived from biological neurons.^[34]
ANNs have evolved into a broad family of techniques that have advanced the state of the art across multiple domains.^[34]
Neural architecture search (NAS) uses machine learning to automate ANN design.^[34]
Because of their ability to reproduce and model nonlinear processes, Artificial neural networks have found applications in many disciplines.^[34]
The convergence behavior of certain types of ANN architectures are more understood than others.^[34]
A fundamental objection is that ANNs do not sufficiently reflect neuronal function.^[34]
A central claim of ANNs is that they embody new and powerful general principles for processing information.^[34]
This allows simple statistical association (the basic function of artificial neural networks) to be described as learning or recognition.^[34]
Analyzing what has been learned by an ANN, is much easier than to analyze what has been learned by a biological neural network.^[34]
A single-layer feedforward artificial neural network with 4 inputs, 6 hidden and 2 outputs.^[34]
A two-layer feedforward artificial neural network with 8 inputs, 2x8 hidden and 2 outputs.^[34]

소스

↑ ^1.0 ^1.1 ^1.2 ^1.3 ^1.4 ^1.5 ^1.6 ^1.7 ^1.8 Artificial Neural Networks for Machine Learning
↑ ^2.0 ^2.1 ^2.2 ^2.3 ELI5: what is an artificial neural network?
↑ ^3.0 ^3.1 Artificial Neural Networks: What Every Marketer Should Know
↑ ^4.0 ^4.1 ^4.2 ^4.3 ^4.4 ^4.5 What are Neural Networks?
↑ What does Training Neural Networks mean?
↑ Artificial Neural Networks in Biological and Environmental Analysis
↑ ^7.0 ^7.1 ^7.2 ^7.3 Artificial Neural Networks (ANN)
↑ ^8.00 ^8.01 ^8.02 ^8.03 ^8.04 ^8.05 ^8.06 ^8.07 ^8.08 ^8.09 ^8.10 ^8.11 ^8.12 ^8.13 ^8.14 ^8.15 ^8.16 ^8.17 ^8.18 ^8.19 ^8.20 ^8.21 ^8.22 ^8.23 ^8.24 ^8.25 ^8.26 ^8.27 ^8.28 ^8.29 ^8.30 ^8.31 Artificial Neural Network Fundamentals · UC Business Analytics R Programming Guide
↑ ^9.0 ^9.1 ^9.2 ^9.3 ^9.4 ^9.5 ^9.6 ^9.7 ^9.8 ^9.9 Artificial Neural Networks
↑ What Is a Neural Network?
↑ ^11.0 ^11.1 ^11.2 What is an artificial neural network?
↑ ^12.0 ^12.1 ^12.2 ^12.3 ^12.4 What are Artificial Neural Networks
↑ What is an Artificial Neural Network (ANN)?
↑ ^14.0 ^14.1 A Neural Network Playground
↑ ^15.0 ^15.1 ^15.2 ^15.3 ^15.4 ^15.5 What are Neural Networks?
↑ ^16.0 ^16.1 ^16.2 ^16.3 ^16.4 Neural network | computing
↑ ^17.0 ^17.1 ^17.2 ^17.3 ^17.4 Artificial Neural Network
↑ ^18.0 ^18.1 ^18.2 ^18.3 ^18.4 ^18.5 What Are Artificial Neural Networks - A Simple Explanation For Absolutely Anyone
↑ ^19.00 ^19.01 ^19.02 ^19.03 ^19.04 ^19.05 ^19.06 ^19.07 ^19.08 ^19.09 ^19.10 ^19.11 ^19.12 ^19.13 ^19.14 What is Artificial Neural Network
↑ ^20.0 ^20.1 ^20.2 What is a neural network? A computer scientist explains
↑ ^21.0 ^21.1 ^21.2 ^21.3 ^21.4 Types of Neural Networks
↑ ^22.0 ^22.1 ^22.2 Artificial Neural Networks
↑ ^23.0 ^23.1 ^23.2 ^23.3 ^23.4 ^23.5 ^23.6 Artificial Intelligence
↑ ^24.0 ^24.1 Explained: Neural networks
↑ ^25.0 ^25.1 ^25.2 ^25.3 Artificial Neural Networks as Models of Neural Information Processing
↑ ^26.0 ^26.1 ^26.2 ^26.3 A Beginner's Guide to Neural Networks and Deep Learning
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↑ ^29.0 ^29.1 ^29.2 ^29.3 ^29.4 ^29.5 ^29.6 Artificial Neural Network (ANN)
↑ ^30.0 ^30.1 ^30.2 ^30.3 Artificial Neural Network - an overview
↑ ^31.0 ^31.1 Overview of Artificial Neural Networks
↑ ^32.0 ^32.1 ^32.2 ^32.3 ^32.4 ^32.5 ^32.6 What is an artificial neural network? Here’s everything you need to know
↑ ^33.00 ^33.01 ^33.02 ^33.03 ^33.04 ^33.05 ^33.06 ^33.07 ^33.08 ^33.09 ^33.10 ^33.11 ^33.12 ^33.13 ^33.14 ^33.15 ^33.16 ^33.17 Complete Guide to Artificial Neural Network Concepts & Models
↑ ^34.00 ^34.01 ^34.02 ^34.03 ^34.04 ^34.05 ^34.06 ^34.07 ^34.08 ^34.09 ^34.10 ^34.11 ^34.12 ^34.13 Artificial neural network

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