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Data distribution in statistics.

In a world of statistics a data distribution is a function which presents all the possible values of the data. It also shows how often each value occurs.
From this distribution it is possible to calculate the probability of any one particular observation in the sample space, or the likelihood that an observation will have a value which is less than (or greater than) a point of interest.

The function of a distribution that presents the density of the values of our data is called a probability density function or simply pdf.

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Artificial Neural Networks (ANN) in Python and R.

Artificial neural networks, usually simply called neural networks NNs, are computing models mainly inspired by the biological neural networks that constitute human brains logic.

An ANN is based on a collection of connected nodes called neurons, modelling the neurons in a biological brain. Each connection, like the synapses in a biological brain, transmits a signal to other neurons. A neuron receiving a signal then processes it and transmits signal to 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 also 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. Neurons are aggregated into layers which perform different transformations on their inputs. Signals travel from the input layer to the output layer through number of hidden layers.

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Convolutional Neural Networks (CNN) with Python.

Python programming language and its libraries combined together form the powerful tools for solving Convolutional Neural Networks tasks.

In deep learning, a convolutional neural network or simply CNN is a class of deep neural networks usually applied to analyzing images.

The name β€œconvolutional neural network” indicates that the network performs a mathematical operation called convolution. Convolutional networks are a specialized type of neural networks that use convolution in place of general matrix multiplication in at least one of their layers.
Most common tasks are image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, natural language processing, brain-computer interfaces, and financial time series.

A CNN consists of an input layer, hidden layers and an output layer. In any feed-forward neural network, any middle layers are called hidden because their inputs and outputs are masked by the activation function and final convolution. In a convolutional neural network, the hidden layers include layers that perform convolutions. Typically this includes a layer that does multiplication or other dot product, and its activation function is commonly ReLU. This is followed by other convolution layers such as pooling layers, fully connected layers and normalization layers.
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