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What is a Convolution: Introducing the Convolution Operation Step by Step

In this post, we build an intuitive step-by-step understanding of the convolution operation and develop the mathematical definition as we go. A convolution describes a mathematical operation that blends one function with another function known as a kernel to produce an output that is often more interpretable. For example, the convolution operation in a

What is Batch Normalization And How Does it Work?

Batch normalization is a technique for standardizing the inputs to layers in a neural network. Batch normalization was designed to address the problem of internal covariate shift, which arises as a consequence of updating multiple-layer inputs simultaneously in deep neural networks. What is Internal Covariate Shift? When training a neural network, it will speed

Deep Learning Optimization Techniques for Gradient Descent Convergence

In this post, we will introduce momentum, Nesterov momentum, AdaGrad, RMSProp, and Adam, the most common techniques that help gradient descent converge faster. Understanding Exponentially Weighted Moving Averages A core mechanism behind many of the following algorithms is called an exponentially weighted moving average. As the name implies, you calculate an average of several

Stochastic Gradient Descent versus Mini Batch Gradient Descent versus Batch Gradient Descent

In this post, we will discuss the three main variants of gradient descent and their differences. We look at the advantages and disadvantages of each variant and how they are used in practice. Batch gradient descent uses the whole dataset, known as the batch, to compute the gradient. Utilizing the whole dataset returns a

Understanding The Exploding and Vanishing Gradients Problem

In this post, we develop an understanding of why gradients can vanish or explode when training deep neural networks. Furthermore, we look at some strategies for avoiding exploding and vanishing gradients. The vanishing gradient problem describes a situation encountered in the training of neural networks where the gradients used to update the weights shrink

Dropout Regularization in Neural Networks: How it Works and When to Use It

In this post, we will introduce dropout regularization for neural networks. We first look at the background and motivation for introducing dropout, followed by an explanation of how dropout works conceptually and how to implement it in TensorFlow. Lastly, we briefly discuss when dropout is appropriate. Dropout regularization is a technique to prevent neural

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 during backpropagation. This helps prevent the network from overfitting the training data as well as the exploding

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 of your neural network. In this post, we look at the most common methods for normalizing data

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 loss function, we can derive the gradients which are used to update the weights. The average over

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 hidden layers, the number of units in a layer, and which activation functions to use. In our