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Artificial neural networks are composed of nodes connected by edges; these networks can be composed of multiple layers. Given high volumes of quality training data, they have proven to be highly effective in solving non-linear and intractable problems. The nodes edges are adjusted while the neural network learns to produce the desired output.

A key feature of artificial neural networks that distinguishes them from other techniques is their ability to learn, adapt and generalise on data inputs. Typically, neural networks learn from a training set and are evaluated on a test set, although more modern neural networks may perform the training and evaluation in one step. For example, when combining neural networks with reinforcement learning algorithms for game playing, typically, the result is the average score improvements of the game over time.

Contemporary Neural Networks learn via an algorithm called stochastic gradient descent that adjusts the network weights based on a calculated error between the network input and output. Over time, this error reduces as the network learns, although it typically reaches an optimal point where further training does not always yield better results. After the network is trained, it can be deployed and used to determine an output based on new and unseen data by propagating it throughout the trained network.

Advantages of Artificial Neural Networks

• Ability to learn and adapt: Without explicit programming, neural networks can learn from data and enhance their performance over time.
• Modelling complex and non-linear relationships: Neural networks are particularly good at this, which makes them suitable for jobs like image recognition and natural language processing.
• Large-scale data processing: Neural networks are effective at handling large-scale data processing, which makes them suitable for tasks like predictive modelling and pattern recognition.
• The capacity of neural networks to generalise their understanding to fresh, new data makes them practical for tasks like predicting and classification.

Limitations of Artificial Neural Networks

• Results can be challenging to interpret since it can be challenging to comprehend how a neural network thinks and makes decisions.
• Need for a lot of data: Neural networks need a lot of data to learn well, which can be a problem when data is rare or challenging.
• Neural networks are susceptible to overfitting, which can cause them to perform well on training data but poorly on new, unforeseen data.
• Sensitivity to hyperparameter selection. Specific hyperparameters, including the learning rate and the number of hidden layers, can impact how well a neural network performs. Due to this, optimising the network's performance may take a lot of work.Learn Like Peo

These networks have proven effective in various applications in which classical algorithmic approaches have yielded limited results. These include natural language processing, image recognition and predictive modelling. The artificial neural network's ability to adapt is primarily due to its ability to adjust based on input data and generalise non-linear and intractable problems.

Telemus AI™ can help you and your organisation understand better how Artificial Intelligence can solve your most complex problems and challenges.

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