Introduction to Conceptual Overview of Neural Networks

Tarun Gupta
2 min readNov 11, 2022

For Data Science & Machine Learning

Futuristic image generated by an artificial neural network.
Photo by Tengyart on Unsplash | Click the image to read the full-length article

To solve real-world problems (like Object Recognition, Audio Analysis, Medial Image Segmentation etc.) using advanced Deep Learning (DL), understanding the fundamentals of Neural Networks (NN) is a necessity.

NNs are a robust approach to approximate real-valued, discrete-valued, and vector-valued target functions. NNs are built using simple units that receive input(s) and provide output(s) with these simple units connected to each other in different capacities.

Left: A neuron in the Human brain | Right: A simple ANN representation | Source: Wikipedia

One simple unit in a NN is called a perceptron. The perceptron combines the weights of the input vector linearly which is passed through an activation function and then compared to a threshold value.

Source: stackexchange.com

The combined perceptrons in a NN are used to search the best-fit hypothesis space of all possible vectors of the target concept. This search is performed using gradient descent (which is the basis of the backpropagation algorithm).

Image by Author

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Tarun Gupta

A simple fellow writing stories, sharing experiences, sharing his perspective, trying to do his share of humanity.