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…
Selecting the best activation function is critical for effective neural network design. For hidden layers, ReLU is commonly used in CNNs and MLPs, while sigmoid and tanh suit RNNs. Output layer activation depends on the task: linear for regression, sigmoid for binary classification, softmax for multi-class, and sigmoid for multi-label classification.
In the rapidly evolving landscape of Artificial Intelligence (AI), the journey from symbolic AI to the emergence of Deep Learning has been marked by significant milestones. This exploration delves into the historical context, the challenges encountered in the early days of AI, and the transformative breakthroughs that paved the way for the prominence of Deep Learning.