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
One-Hot and Ordinal Encoding for Features and Labels Read More »
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.
Key Building Blocks of Machine Learning – Features and Labels Read More »
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.
How To Choose Train Validation and Test Sets For Your Model? Read More »
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.
Regularization Techniques to Prevent Model Overfitting Read More »
Overfitting, underfitting, and a model’s capacity are critical concepts in deep learning, particularly in the context of training neural networks.
Overfitting, Underfitting and Model’s Capacity in Deep Learning Read More »
Loss Functions – Training a neural network is an optimization problem. The goal is to find parameters that minimize this
Loss Functions for Regression and Classification in Deep Learning Read More »
The exploding gradients occur in a situation opposite to the vanishing gradient problem. Instead of gradients becoming vanishingly small, they
How to Detect Exploding Gradients in Neural Networks Read More »
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.
How to Understand and Implement Neural Networks: A Step-by-Step Guide Read More »
