2nd October 2019

wikipedia

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What is ReLU activation function?

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In the context of artificial neural networks, the rectifier is an activation function defined as the positive part of its argument: , The rectifier is, as of 2018, the most popular activation function for deep neural networks. A unit employing the rectifier is also called a rectified linear unit (ReLU).

What does a neuron compute activation function?

Neuron Output, y: The artificial neuron computes its output according to the equation shown below. This is the result of applying the activation function to the weighted sum of the inputs, less the threshold. This value can be discrete or real depending on the activation function used.

Why non linear activation function is needed?

An activation function is a decision making function that determines the presence of particular neural feature. Non-linearity is needed in activation functions because its aim in a neural network is to produce a nonlinear decision boundary via non-linear combinations of the weight and inputs.

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What is the cross entropy?

In information theory, the cross entropy between two probability distributions and over the same underlying set of events measures the average number of bits needed to identify an event drawn from the set, if a coding scheme is used that is optimized for an "unnatural" probability distribution , rather than the "true"

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What is Tanh function?

The functions cosh x, sinh x and tanh x have much the same relationship to the rectangular hyperbola y² = x² - 1 as the circular functions do to the circle y² = 1 - x². They are therefore sometimes called the hyperbolic functions (h for hyperbolic).

3

What is the max pooling?

Another important concept of CNNs is pooling, which is a form of non-linear down-sampling. There are several non-linear functions to implement pooling among which max pooling is the most common. It partitions the input image into a set of non-overlapping rectangles and, for each such sub-region, outputs the maximum.

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What is batch norm?

Batch Normalization is a technique to provide any layer in a Neural Network with inputs that are zero mean/unit variance - and this is basically what they like! But BatchNorm consists of one more step which makes this algorithm really powerful.

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What is a Softmax regression?

Softmax Regression (synonyms: Multinomial Logistic, Maximum Entropy Classifier, or just Multi-class Logistic Regression) is a generalization of logistic regression that we can use for multi-class classification (under the assumption that the classes are mutually exclusive).

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What are saturated neurons?

Abstract—In the neural network context, the phenomenon of saturation refers to the state in which a neuron predominantly outputs values close to the asymptotic ends of the bounded activation function. Saturation damages both the information capacity and the learning ability of a neural network.

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What is the meaning of logit?

vote 4 vote. The logit (/ˈlo?d?t/ LOH-jit) function is the inverse of the sigmoidal "logistic" function or logistic transform used in mathematics, especially in statistics. When the function's variable represents a probability p, the logit function gives the log-odds, or the logarithm of the odds p/(1 − p).

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What does logit do?

The logit (/ˈlo?d?t/ LOH-jit) function is the inverse of the sigmoidal "logistic" function or logistic transform used in mathematics, especially in statistics. When the function's variable represents a probability p, the logit function gives the log-odds, or the logarithm of the odds p/(1 − p).

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What is the link function?

The link function provides the relationship between the linear predictor and the mean of the distribution function. There are many commonly used link functions, and their choice is informed by several considerations. When using the canonical link function, , which allows to be a sufficient statistic for .

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Is GLM machine learning?

Generalized linear model (GLM) is the basis of many machine-learning algorithms. GLM with first-order variables is basically linear regression, and can be analytically solved (meaning there is a formula that you can use to solve the GLM problems).

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What is a GLM model?

The General Linear Model (GLM) is a useful framework for comparing how several variables affect different continuous variables. In it's simplest form, GLM is described as: Data = Model + Error (Rutherford, 2001, p.3) GLM is the foundation for several statistical tests, including ANOVA, ANCOVA and regression analysis.

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What is a generalized additive model?

Generalized additive model. From Wikipedia, the free encyclopedia. In statistics, a generalized additive model (GAM) is a generalized linear model in which the linear predictor depends linearly on unknown smooth functions of some predictor variables, and interest focuses on inference about these smooth functions.

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What is the linear model of communication?

According to Businesstopia, a linear model, communication is considered one way process where sender is the only one who sends message and receiver doesn't give feedback or response. The message signal is encoded and transmitted through channel in presence of noise.

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What are the models of communication?

Models of communication are conceptual models used to explain the human communication process. The first major model for communication was developed in 1948 by Claude Elwood Shannon and published with an introduction by Warren Weaver for Bell Laboratories.

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What is the process of communication?

The communication process is the steps we take in order to successfully communicate. Components of the communication process include a sender, encoding of a message, selecting of a channel of communication, receipt of the message by the receiver and decoding of the message.

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What is the basic process of communication?

Basic communication processes. Communication is simply the sending and receiving of information between at least two people. The message may be verbal or non-verbal, but to be effective, the message sent must be in a language or code that the receiver will understand.

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What are the six elements of the communication process?

7 Major Elements of Communication Process. Article shared by : Seven major elements of communication process are: (1) sender (2) ideas (3) encoding (4) communication channel (5) receiver (6) decoding and (7) feedback.

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What are the basic elements of communication?

The Communication Process & The Elements of Communication

Communication as a Process. Human communication is interpersonal, it is purposive and it is a process.
SENDER/ENCODER. The sender also known as the encoder decides on the message to be sent, the best/most effective way that it can be sent.
MEDIUM.
CHANNEL.
RECEIVER.
FEEDBACK.
CONTEXT.
NOISE (also called interference)

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What are the basic elements of a communication system?

This system consists three basic components: transmitter, channel, and receiver. The transmitter's function is to process the message signal into a form suitable for transmission over the communication channel.

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