Bayesian philosophy is based on the idea that more may be known about a physical situation than is contained in the data from a single experiment. Bayesian methods can be used to combine results from different experiments, for example. But often the data are scarce or noisy or biased, or all of these.

Bayesian networks are a type of Probabilistic Graphical Model that can be used to build models from data and/or expert opinion. They can be used for a wide range of tasks including prediction, anomaly detection, diagnostics, automated insight, reasoning, time series prediction and decision making under uncertainty.

Bayesian network models capture both conditionally dependent and conditionally independent relationships between random variables. Models can be prepared by experts or learned from data, then used for inference to estimate the probabilities for causal or subsequent events.

One, because the model encodes dependencies among all variables, it readily handles situations where some data entries are missing. Two, a Bayesian network can be used to learn causal relationships, and hence can be used to gain understanding about a problem domain and to predict the consequences of intervention.

How to train a Bayesian Network (BN) using expert knowledge?

1. First, identify which are the main variable in the problem to solve. Each variable corresponds to a node of the network.
2. Second, define structure of the network, that is, the causal relationships between all the variables (nodes).
3. Third, define the probability rules governing the relationships between the variables.

Where does the bayes rule can be used? Explanation: Bayes rule can be used to answer the probabilistic queries conditioned on one piece of evidence.

Bayesian networks are a widely-used class of probabilistic graphical models. A Bayesian network is a compact, flexible and interpretable representation of a joint probability distribution. It is also an useful tool in knowledge discovery as directed acyclic graphs allow representing causal relations between variables.

Bayesian neural networks marginalize over the distribution of parameters in order to make predictions. So the Bayesian approach allows different models to be compared (e.g. no of hidden units). A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest.

Bayesian networks (BN) and Bayesian classifiers (BC) are traditional probabilistic techniques that have been successfully used by various machine learning methods to help solving a variety of problems in many different domains.

Bayesian deep learning It offers principled uncertainty estimates from deep learning architectures. These deep architectures can model complex tasks by leveraging the hierarchical representation power of deep learning, while also being able to infer complex multi-modal posterior distributions.

Convolutional Neural Networks (CNN) is one of the most popular architectures used in deep learning. Binarized Neural Network (BNN) is also a neural network which consists of binary weights and activations. Neural Networks has large number of parameters and overfitting is a common problem to these networks.

As Business News Network (BNN)

Dentrites are the tree-like structure that receives the signal from surrounding neurons, where each line is connected to one neuron. Axon is a thin cylinder that transmits the signal from one neuron to others. At the end of axon, the contact to the dendrites is made through a synapse.

A Perceptron is an algorithm for supervised learning of binary classifiers. This algorithm enables neurons to learn and processes elements in the training set one at a time. There are two types of Perceptrons: Single layer and Multilayer. Single layer Perceptrons can learn only linearly separable patterns.

The perceptron is a simplified model of the real neuron that attempts to imitate it by the following process: it takes the input signals, let’s call them x1, x2, …, xn, computes a weighted sum z of those inputs, then passes it through a threshold function ϕ and outputs the result.

The perceptron is a mathematical model of a biological neuron. While in actual neurons the dendrite receives electrical signals from the axons of other neurons, in the perceptron these electrical signals are represented as numerical values. As in biological neural networks, this output is fed to other perceptrons.

The Perceptron is a linear classification algorithm. This means that it learns a decision boundary that separates two classes using a line (called a hyperplane) in the feature space.

This set of Artificial Intelligence Multiple Choice Questions & Answers (MCQs) focuses on “Neural Networks – 1”. Explanation: The perceptron is a single layer feed-forward neural network.

A single layer perceptron (SLP) is a feed-forward network based on a threshold transfer function. SLP is the simplest type of artificial neural networks and can only classify linearly separable cases with a binary target (1 , 0).

Weights are used so that we can scale individual inputs. If input x3 for example isn’t contributing enough to the right classification the perceptron will assign a small value to diminish it’s output signal. Weights are initialized like that because it’s faster to train this way.

It is based on the McCulloch–Pitts neuron. It consists of a weight, a bias and a summation function. In the standard perceptron, the net is passed to the activation (transfer) function and the function’s output is used for adjusting the weights. A multilayer network of ADALINE units is known as a MADALINE.

Eventually, we can apply a simultaneous weight update similar to the perceptron rule: w:=w+Δw. Although, the learning rule above looks identical to the perceptron rule, we shall note the two main differences: Here, the output “o” is a real number and not a class label as in the perceptron learning rule.

Explanation: The objective of backpropagation algorithm is to to develop learning algorithm for multilayer feedforward neural network, so that network can be trained to capture the mapping implicitly.

Backpropagation is a short form for “backward propagation of errors.” It is a standard method of training artificial neural networks. This method helps to calculate the gradient of a loss function with respects to all the weights in the network. In this tutorial, you will learn: Best practice Backpropagation.

k – NN is a prediction method for classification – as well as regression – type prediction problems….CH06

• Initialize weights with random values and set other parameters.
• Read in the input vector and the desired output.
• Compute the actual output via the calculations, working forward through the layers.

The Backpropagation algorithm is a supervised learning method for multilayer feed-forward networks from the field of Artificial Neural Networks. Feed-forward neural networks are inspired by the information processing of one or more neural cells, called a neuron.