AI News, BOOK REVIEW: Difference between revisions of "Artificial Neural Networks/Neural Network Basics"

Difference between revisions of "Artificial Neural Networks/Neural Network Basics"

Artificial Neural Networks, also known as “Artificial neural nets”, “neural nets”, or ANN for short, are a computational tool modeled on the interconnection of the neuron in the nervous systems of the human brain and that of other organisms.

Artificial neural networks are very different from biological networks, although many of the concepts and characteristics of biological systems are faithfully reproduced in the artificial systems. Artificial

neural nets are a type of non-linear processing system that is ideally suited for a wide range of tasks, especially tasks where there is no existing algorithm for task completion.

With proper training, ANN are capable of generalization, the ability to recognize similarities among different input patterns, especially patterns that have been corrupted by noise.

The term “Neural Net” refers to both the biological and artificial variants, although typically the term is used to refer to artificial systems only.

Each neuron is a multiple-input, multiple-output (MIMO) system that receives signals from the inputs, produces a resultant signal, and transmits that signal to all outputs.

However, to reproduce the effect of the synapse, the connections between PE are assigned multiplicative weights, which can be calibrated or “trained” to produce the proper system output.

Where ζ is the weighted sum of the inputs (the inner product of the input vector and the tap-weight vector), and σ(ζ) is a function of the weighted sum.

If we recognize that the weight and input elements form vectors w and x, the ζ weighted sum becomes a simple dot product:

The dotted line in the center of the neuron represents the division between the calculation of the input sum using the weight vector, and the calculation of the output value using the activation function.

Neural networks tend to have one input per degree of freedom in the input space, and one output per degree of freedom in the output space.

Expert systems, by contrast, are used in situations where there is insufficient data and theoretical background to create any kind of a reliable problem model.

Expert systems emulate the deduction processes of a human expert, by collecting information and traversing the solution space in a directed manner.

Though such assumptions are not required, it has been found that the addition of such a priori information as the statistical distribution of the input space can help to speed training.

During training, the neural network performs the necessary analytical work, which would require non-trivial effort on the part of the analyst if other methods were to be used.

During training, care must be taken not to provide too many input examples and different numbers of training examples could produce very different results in the quality and robustness of the network.

Lecture 6 | Training Neural Networks I

In Lecture 6 we discuss many practical issues for training modern neural networks. We discuss different activation functions, the importance of data ...

Phase-Functioned Neural Networks for Character Control

We present a real-time character control mechanism using a novel neural network architecture called a Phase-Functioned Neural Network. In this network ...

But what *is* a Neural Network? | Deep learning, chapter 1

Subscribe to stay notified about new videos: Support more videos like this on Patreon: Or don'

Artificial Neural Network Tutorial | Deep Learning With Neural Networks | Edureka

TensorFlow Training - ) This Edureka "Neural Network Tutorial" video (Blog: will .

Perceptron Training

Watch on Udacity: Check out the full Advanced Operating Systems course for free ..

Recurrent Neural Networks (RNN) | RNN LSTM | Deep Learning Tutorial | Tensorflow Tutorial | Edureka

TensorFlow Training - ) This Edureka Recurrent Neural Networks tutorial video (Blog: ..

Deep Belief Nets - Ep. 7 (Deep Learning SIMPLIFIED)

An RBM can extract features and reconstruct input data, but it still lacks the ability to combat the vanishing gradient. However, through a clever combination of ...

Intro - Training a neural network to play a game with TensorFlow and Open AI

This tutorial mini series is focused on training a neural network to play the Open AI environment called CartPole. The idea of CartPole is that there is a pole ...

Deep learning networks are sensitive to small changes to their input - Ice bear

Video is taken from BBC Earth:

Build a Neural Net in 4 Minutes

How does a Neural network work? Its the basis of deep learning and the reason why image recognition, chatbots, self driving cars, and language translation ...