How to build a Simple Neural Network in Python (One Layer) Part I

Example is from the book “Machine Learning for Finance“ by Jannes Klaas. In this video we are building a simple one layer Neural Network from scratch in Python. In specific we are setting up the input layer and initialize random weights and feed this data to the activation function (sigmoid). The output is then compared with the actual output (y) and measured with the binary cross entropy loss. If you found this interesting I will continue with optimizing the network using Gradient Descent and Parameter update using Backpropagation and provide details on how to proceed. Link to Book (Not affiliated btw) Mentioned articles: Bias and weights Cross Entropy Loss: 00:00 - 01:36 Introduction / Resources 01:36 - 02:31 Input Layer and output (y) 02:31 - 04:20