Feedforward Explained - Neural Networks From Scratch Part 1

First I discuss how a neural network learns. Then I talk about some basics and notations used to denote the input, weights and biases. Finally I explain the feedforward equation and explain the general form of the equation, which can be extended to any number of samples as well. This is the first part of my series - Neural Networks from Scratch, where my goal is to explain what happens in a neural network, explain the math behind it and how you can implement it from scratch in python. This is for beginners and for people who are interested in the inner workings of a neural network. Part 2 : Gradient Descent & Backpropagation Explained https://youtu.be/jVXJnN-kIqU Part 3: Building a Neural Network from Scratch in Python https://youtu.be/HNwcN3RHcNI References: https://www.coursera.org/learn/neural-networks-deep-learning/ https://towardsdatascience.com/understanding-backpropagation-algorithm-7bb3aa2f95fd https://towardsdatascience.com/math-neural-network-from-scratch-in-python-d6da9f29ce65 https://towardsdatascience.com/how-to-build-your-own-neural-network-from-scratch-in-python-68998a08e4f6 Subscribe to my Newsletter ! https://adarsh1021.github.io/#newsletter Having trouble ? Need help ? Connect with me ! Email: [email protected] Twitter : https://twitter.com/adarsh_menon_ LinkedIn: https://www.linkedin.com/in/adarsh-me... Github : https://github.com/adarsh1021 #neuralnetworks #deeplearning #python