Features and labels are crucial in machine learning. To ensure algorithms can process data, categories must be converted into numerical formats using techniques like one-hot encoding and ordinal encoding. This post provides an overview of both methods, explaining their workings and applications
Two fundamental building blocks of machine learning are features (input) and labels (output). This article explains what features and labels are, their different types, and how they are applied in various machine learning models.
In this post, we'll explore the fundamental concepts of dataset splitting in machine learning. We'll cover the definitions of train, validation, and test sets, the importance of splitting the dataset, different partitioning strategies, and tips for ensuring proper dataset splitting. Join us as we unravel the keys to effective model development and evaluation.
In this post, we'll explore how to prevent overfitting in your machine learning models using simple regularization techniques. Dive into controlling model complexity and improving generalization for better performance in real-world situations.
Overfitting, underfitting, and a model's capacity are critical concepts in deep learning, particularly in the context of training neural networks. In this post, we'll learn how a model's capacity leads to overfitting and underfitting of the data and its effect on the performance of a neural network. Let's begin! Overview OverviewModel's CapacityGeneralization GapHow Model's Capacity Affects the Way A Model Fits the Same Set of DataHow to Know If The Model Would Work Well on Unseen Data?SummaryFurther Reading In this post, you will learn: What is the model's capacity? How model's capacity affect the way the model fits the same set of data? Concept of Overfitting, underfitting, and finding just the right fit How to know if the model would work well on unseen data? Model's Capacity A model's capacity refers to its ability to capture and represent complex patterns in the data. It reflects the flexibility and complexity of the model architecture. Let's understand this with the help of an example: We can train a model using historical data and make predictions about the lottery based on that trained model as shown in the figure below: Figure 1: ML model to predict lottery The problem is that the model being able to fit on seen data, doesn't mean that it will perform well on unseen data. This means that the model with high capacity (has a large number of parameters and is trained long enough) can memorize training samples. In the lottery case, the input can be the date of the lottery and the output can be the lucky number of that day's lottery. If we train a…
Loss Functions - Training a neural network is an optimization problem. The goal is to find parameters that minimize this loss function and increase the model's performance as a consequence. So, training a neural network means finding the weights that minimize our loss function. This means that we need to know what loss functions are to make sure to use the right one based on the neural network we are training to solve a particular problem. We will learn what loss functions are, what type of loss functions to use for a given problem, and how they impact the output of the neural network. Let's begin. OverviewLoss FunctionsWhat is a Loss Function?How Do Loss Functions Work?Which Loss Functions To Use for Regression and ClassificationLoss Functions for RegressionLoss Functions for Classification SummaryFurther ReadingRelated ArticlesRelated Videos Loss Functions What is a Loss Function? Loss functions, also known as error functions , indicate how well the model is performing on the training data, allowing for the updating of weights towards reducing the loss, thereby enhancing the neural network's performance. In other words, the loss function acts as a guide for the learning process within a machine learning algorithm or a neural network. It quantifies how well the model's predictions match the actual target values during training. Here are some terminology that you should be familiar with regarding calculating this. Loss Function: Applied to a single training example and measures the discrepancy between the predicted output and the true target. Cost Function: Refers to the aggregate (sum) of loss function over the entire dataset, including any regularization terms. Objective Function: This term is…
The exploding gradients occur in a situation opposite to the vanishing gradient problem. Instead of gradients becoming vanishingly small, they become extremely large during training. This makes your model unstable and unable to learn from your training data. In this post, we will understand the problem of exploding gradients in deep artificial neural networks. Let's begin Overview In this post, we will cover: What exploding gradient is and its causes. How do we know if the model has an exploding gradient problem? How to fix the exploding gradient problem? 1 - What are Exploding Gradients? The exploding gradient problem happens when the gradients in a neural network become so large that it messes up the training process. During backpropagation, the gradient of the loss function w.r.t. network's parameters (such as weights and biases) becomes extremely large. When the gradient becomes too large, it can lead to numerical instability and difficulties in training the neural network effectively. Essentially, the updates to the parameter become so large that they cause the network's parameter to "explode" meaning they grow uncontrollably. This can result in unpredictable behavior during training, making it difficult for the network to converge to a solution and hindering its ability to learn meaningful patterns in the data. 2 - Understanding Exploding Gradients Through Example Let's take the same example that we looked at for vanishing gradient problem, and see what exploding gradients would look like: Figure 1: Neural Network to predict if the person will take insurance or not For example, if we try to calculate the gradient of loss w.r.t. weight , where d1 = and d2 is…
In our daily lives, we effortlessly recognize faces and understand voices, tasks that seem almost second nature to us. But explaining how we do these things to machines is not easy. So, how do we make machines think? Can we teach them using examples? Think of it like this: just as we fuel our brains with energy, do we need to feed machine learning algorithms to make them learn? Machine learning models are made up of mathematical structures that allow them to map input to output. Imagine, you want to teach a machine to recognize faces in photos. You'd give it tons of pictures with faces labeled 'face' and pictures without labeled 'face'. The machine learns by looking at these examples, figuring out patterns, and then making its guesses whether a new picture has a face or not. Now, let's dive deeper and understand what an artificial neural network is, drawing inspiration from the intricate workings of biological neurons to construct models that simulate learning processes.