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 »
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.
How Has Artificial Intelligence Evolved From Symbolic AI To Deep Learning? Read More »
