# AI News, How to build your own Neural Network from scratch in Python

- On Tuesday, June 5, 2018
- By Read More

## How to build your own Neural Network from scratch in Python

Motivation: As part of my personal journey to gain a better understanding of Deep Learning, I’ve decided to build a Neural Network from scratch without a deep learning library like TensorFlow.

Without delving into brain analogies, I find it easier to simply describe Neural Networks as a mathematical function that maps a given input to a desired output.

Neural Networks consist of the following components The diagram below shows the architecture of a 2-layer Neural Network (note that the input layer is typically excluded when counting the number of layers in a Neural Network) Creating a Neural Network class in Python is easy.

Training the Neural Network The output ŷ of a simple 2-layer Neural Network is: You might notice that in the equation above, the weights W and the biases b are the only variables that affects the output ŷ.

As we’ve seen in the sequential graph above, feedforward is just simple calculus and for a basic 2-layer neural network, the output of the Neural Network is: Let’s add a feedforward function in our python code to do exactly that.

In order to know the appropriate amount to adjust the weights and biases by, we need to know the derivative of the loss function with respect to the weights and biases.

However, we can’t directly calculate the derivative of the loss function with respect to the weights and biases because the equation of the loss function does not contain the weights and biases.

Although Deep Learning libraries such as TensorFlow and Keras makes it easy to build deep nets without fully understanding the inner workings of a Neural Network, I find that it’s beneficial for aspiring data scientist to gain a deeper understanding of Neural Networks.

- On Thursday, September 19, 2019

**Normalized Inputs and Initial Weights**

This video is part of the Udacity course "Deep Learning". Watch the full course at

**But what *is* a Neural Network? | Chapter 1, deep learning**

Subscribe to stay notified about new videos: Support more videos like this on Patreon: Special .

**Backpropagation in 5 Minutes (tutorial)**

Let's discuss the math behind back-propagation. We'll go over the 3 terms from Calculus you need to understand it (derivatives, partial derivatives, and the chain ...

**Lecture 3 | Loss Functions and Optimization**

Lecture 3 continues our discussion of linear classifiers. We introduce the idea of a loss function to quantify our unhappiness with a model's predictions, and ...

**Gradient descent, how neural networks learn | Chapter 2, deep learning**

Subscribe for more (part 3 will be on backpropagation): Thanks to everybody supporting on Patreon

**Convolutional Neural Networks - Ep. 8 (Deep Learning SIMPLIFIED)**

Out of all the current Deep Learning applications, machine vision remains one of the most popular. Since Convolutional Neural Nets (CNN) are one of the best ...

**Cross Entropy**

This video is part of the Udacity course "Deep Learning". Watch the full course at

**Linear regression (2): Gradient descent**

Gradient and stochastic gradient descent; gradient computation for MSE.

**Lecture 6 | Training Neural Networks I**

In Lecture 6 we discuss many practical issues for training modern neural networks. We discuss different activation functions, the importance of data ...

**ML50. Neural Networks Learning - Cost function**