https://bergstrom.org/categories/neural-networks/Neural Networks - Category - Jorgen BergstromHugo -- gohugo.ioen-usJorgen BergstromSat, 16 Nov 2024 00:00:00 -0500https://bergstrom.org/posts/backpropagation_part_3/Sat, 16 Nov 2024 00:00:00 -0500Jorgen Bergstromhttps://bergstrom.org/posts/backpropagation_part_3/[1 Input] + [1 Hidden Layer with N Perceptrons] + [1 Output] In this example I will extend my previous code to be able to handle a hidden layer with N perceptrons. As before, I will demonstrate the use of the neural network by fitting it to the following mathematical function: $y = 0.1 + 0.1 \cdot x^2$ over the range $x \in [0, 10]$. The C++ implementation for this example can be found in my github account.https://bergstrom.org/posts/backpropagation_part_2/Tue, 22 Oct 2024 00:00:00 -0500Jorgen Bergstromhttps://bergstrom.org/posts/backpropagation_part_2/https://bergstrom.org/posts/backpropagation_part_1/Sun, 20 Oct 2024 00:00:00 -0500Jorgen Bergstromhttps://bergstrom.org/posts/backpropagation_part_1/One Network Layer with 1 Input and 1 Output The simplest neural network architecture consists of a single perceptron. This network has only one input and one output, making it a highly streamlined model. While suitable for theoretical understanding, it is insufficient for real-world applications due to its limited capacity. However, it serves as a fundamental building block for studying the principles of backpropagation and training neural networks. Code Location You can find the source code for this example here.