How to Evaluate Deep Learning Models: Key Metrics Explained
Learn to evaluate deep learning models using the confusion matrix, accuracy, precision, and recall. Covers binary, multi-class, and object detection with Scikit-learn.
Regression vs Transformers: Key Differences and When to Use Each Model
Explore the differences between regression and transformer models in machine learning. Understand how each works and when to use them.
Dimension Reduction – IsoMap
Through a series of posts, learn how to implement dimension reduction algorithms using IsoMap.
Stable Diffusion Made Easy: Get Started on GPU cloud servers
Discover how to easily set up and run Stable Diffusion on GPU cloud servers. This tutorial guides you through the step-by-step process to get your AI-powered image generation up and running smoothly, even if you’re a beginner.
Dropout Regularization in Deep Learning
Discover how dropout layers prevent overfitting by randomly deactivating neurons during training. Learn dropout ratios for better model generalization.
Vision Transformers (ViTs): Computer Vision with Transformer Models
Discover how Vision Transformers (ViTs) are transforming computer vision by using transformer architecture for tasks like image classification and object detection. Learn what are ViTs, inductive bias, and the working of ViTs.
A Guide to Distilled Stable Diffusion: Implemented with Gradio
In this tutorial, we show how to take advantage of the first distilled stable diffusion model, and show how to run it using Jupyter Notebook and a convenient Gradio demo.
Boost Your Models with AdaBoost Explained
AdaBoost is a popular machine learning technique that improves model accuracy by combining several weak learners into a strong one. In this guide, we’ll explain how AdaBoost works, explore its pros and cons, and show you how to implement it in Python using scikit-learn.
