What exactly is an artificial neuron

Can someone explain me where an artificial neuron is constructed of? Do i have to see it as a piece of algorithm?

Hi @bbos

Welcome to the community.

The artificial neuron it is process unit on the neural network constructed with Three main components:

  1. Inputs: An artificial neuron receives multiple inputs, which represent numerical values corresponding to the features or attributes of the data being processed. These inputs can be continuous or binary numerical values.
  2. Weights: Each input is associated with a weight, which represents the relative importance of that input to the neuron’s output. The weights are adjustable and control how each input contributes to the neuron’s activation. During the training process, these weights are updated to allow the neuron to learn how to perform its tasks properly.
  3. Bias: In addition to inputs and weights, an artificial neuron also has a parameter called bias. The bias is a numerical value that is added to the weighted sum of the inputs. It allows the neuron to adjust its operating point, making it more flexible and capable of learning patterns and complex relationships in the data.

The functioning of an artificial neuron is relatively simple: the inputs are multiplied by their respective weights, and the results of these multiplications are summed with the bias. Then, this sum is passed through a non-linear activation function, which determines whether the neuron will be activated (producing a non-zero output) or not activated (producing an output close to zero).

Thus, the output of the neuron is determined by the activation function applied to the weighted sum of the inputs, weights, and bias. The choice of the activation function is crucial as it introduces non-linearity to the model, enabling the neurons to learn complex relationships between the inputs and the output, making the neural network capable of solving more complex problems.

Here is the general mathematical formula for an artificial neuron:

Output = Activation_Function(Weighted_Sum_of_Inputs_and_Weights + Bias)

In this context, an artificial neuron can be seen as a basic processing unit that transforms a set of inputs into an output using weights and an activation function. However, the true power of neural networks comes from the combination of multiple neurons into layers, creating more complex architectures that can learn patterns and perform sophisticated machine learning tasks.

There is no need to see it on your algorithm.

best regards

1 Like

Thanks Elirod. Do i understand it correct that in the end the outcome of a artificial neural network depends on the mathematical formula set up by humans and thus the outcome is always a result of this initial formula? If so , isnt it very difficult on large networks to find out whether the outcomes are “correct”. ( i mean, in the end it is creating a “reality” that is in the basis derived from this initial formulas. Or do i see it now too complex :wink:

Hi @bbos

Yep! You are partly correct in your understanding, but there are a few important nuances to consider in the context of artificial neural networks (ANNs).

In an artificial neural network, the outcome does depend on the initial setup, which includes the architecture of the network, the choice of activation functions, and the initialization of the weights and biases. However, during the training process, the network learns from the data and updates its weights and biases through optimization techniques like backpropagation. The learning process adjusts the parameters to minimize the error between the predicted outputs and the true labels in the training data.

While the initial setup influences the network’s ability to learn and the general architecture of the learned model, the final outcome is not solely determined by the initial formulas. The training process plays a crucial role in shaping the network’s behavior and performance. The goal of training is to generalize well to unseen data, meaning the network can make accurate predictions on new, previously unseen examples.

That being said, there are some challenges in assessing the correctness and generalization of large neural networks:

  • Overfitting: Large neural networks, especially with many parameters, have the risk of overfitting the training data. Overfitting occurs when the model memorizes the training data instead of learning general patterns. This can lead to poor performance on new data.

  • Model Evaluation: Evaluating the performance of large neural networks can be challenging, especially when the datasets are imbalanced, have noise, or lack sufficient ground truth labels. Various evaluation techniques like cross-validation and hold-out validation are used to assess model performance.

  • Interpretability: Large neural networks can be complex and difficult to interpret. Understanding the exact reasons behind a specific prediction may be challenging due to their black-box nature.

  • Hyperparameter Tuning: The performance of large networks is influenced by various hyperparameters like learning rate, batch size, and network architecture. Optimizing these hyperparameters can be time-consuming and computationally intensive.

To mitigate some of these challenges, researchers and practitioners in the field of machine learning and deep learning have been working on techniques like regularization, transfer learning, and explainability methods to improve model robustness, interpretability, and generalization.

In summary, while the initial setup and architecture influence the behavior of an artificial neural network, the learning process plays a crucial role in adapting the model to the data. Large neural networks can indeed be complex, but with careful design, regularization, and evaluation, it is possible to build models that generalize well and perform accurately on new, unseen data.

Neural Networks is quite easy to understand, but has a lot a complex mathematical techniques involve in they process that is required to guarantee a good prediction/classification fit.

For now, just keep in mind that the main goal is to minimize the prediction error by adjusting their weights during the training process.

Hi @bbos

thanks for your question.

Please have a look at a graphical visualization which contains the relevant elements:

see also: Choice of activation function - #8 by Christian_Simonis

In the end we can use ground truth data (labels) to fit the neural network. So the purpose is always that the model learns the model parameters during the fitting (which serves an optimization to model the labels well) so that the model learns abstract patterns sufficiently and generalizes well to predict the label, also on new / unseen data of course.

In reality this prediction has to be „good enough“ to solve the business problem, see also: How does a Deep Neural Network work? - #4 by Christian_Simonis

I think if you want to dive deeper with more practice and technical depth, you could consider other courses to follow-up after AI4everyone, see also: Please help with course selection - #2 by Christian_Simonis

A quite classic sequence which seems to be popular among fellow learners seems to be:

  • AI for everyone (if you are a beginner)
  • machine learning specialization for the basics and core concepts
  • deep learning specialization if this suits your plans and you work rather with big unstructured data and want to apply or work with CV, NLP, LLM etc.
  • (MLOps, LLM specialization or TF specialization dependent on your requirements and plans)

Hope that helps!


1 Like

thanks for your reply. It helps a lot to understand it better!

1 Like