# Author Archive

## Weight Decay in Neural Networks

What is Weight Decay Weight decay is a regularization technique in deep learning. Weight decay works by adding a penalty term to the cost function of a neural network which has the effect of shrinking the weights

## Feature Scaling and Data Normalization for Deep Learning

Before training a neural network, there are several things we should do to prepare our data for learning. Normalizing the data by performing some kind of feature scaling is a step that can dramatically boost the performance

## An Introduction to Neural Network Loss Functions

This post introduces the most common loss functions used in deep learning. The loss function in a neural network quantifies the difference between the expected outcome and the outcome produced by the machine learning model. From the

## Understanding Basic Neural Network Layers and Architecture

This post will introduce the basic architecture of a neural network and explain how input layers, hidden layers, and output layers work. We will discuss common considerations when architecting deep neural networks, such as the number of

## Understanding Backpropagation With Gradient Descent

In this post, we develop a thorough understanding of the backpropagation algorithm and how it helps a neural network learn new information. After a conceptual overview of what backpropagation aims to achieve, we go through a brief

## How do Neural Networks Learn

In this post, we develop an understanding of how neural networks learn new information. Neural networks learn by propagating information through one or more layers of neurons. Each neuron processes information using a non-linear activation function. Outputs

## Understanding Hinge Loss and the SVM Cost Function

In this post, we develop an understanding of the hinge loss and how it is used in the cost function of support vector machines. Hinge Loss The hinge loss is a specific type of cost function that

## What is a Support Vector?

In this post, we will develop an understanding of support vectors, discuss why we need them, how to construct them, and how they fit into the optimization objective of support vector machines. A support vector machine classifies

## What is a Kernel in Machine Learning?

In this post, we are going to develop an understanding of Kernels in machine learning. We frame the problem that kernels attempt to solve, followed by a detailed explanation of how kernels work. To deepen our understanding

## Principal Components Analysis Explained for Dummies

In this post, we will have an in-depth look at principal components analysis. We start with a simple explanation to build an intuitive understanding of PCA. In the second part, we will look at a more mathematical