PyTorch Tutorial- How to Develop Deep Learning Models Part 1: Tensors
PyTorch Tensor Tutorial
Machine Learning Expedition
Boosting Your Predictive Power: A Practical Guide to Regression with XGBoost
In this blog post, we'll explore how XGBoost, a powerful machine learning algorithm, can be utilized for regression tasks. XGBoost stands for eXtreme Gradient Boosting and is known for its efficiency and effectiveness in predictive modeling. We'll cover the basics of regression, introduce XGBoost, and then
Foundations of Diffusion Models and How DDPM Works
Diffusion models have emerged as a groundbreaking approach in the landscape of deep generative models, offering a robust alternative to traditional methods like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). These models are based on a stochastic process that gradually transforms data into a distribution of pure noise and
Systematic Review of Prompt Engineering in Large Language Models
Prompt engineering has become a crucial strategy for enhancing the capabilities of large language models (LLMs) and vision-language models (VLMs). This method employs task-specific instructions, or prompts, to improve model performance without altering the underlying model architecture. Instead of retraining the model, prompts offer a straightforward way to adapt pre-trained
SQL for Data Scientists: Mastering the Language of Data
In the realm of data science, the ability to extract and manipulate data is as essential as understanding statistical models. SQL (Structured Query Language) is the bedrock tool for this task, enabling data scientists to communicate with databases effectively. In this blog, we'll explore the importance of SQL
Unlocking the Power of Data: Mastering Feature Engineering for Machine Learning Success
Feature engineering is a crucial step in improving the performance of XGBoost models, especially in Kaggle competitions where fine margins can determine the difference between rankings. The process involves creating new features from the existing data to better capture the underlying patterns and relationships. Here's a comprehensive approach
Markov Decision Process
Reinforcement learning (RL) is a paradigm of machine learning where an agent learns to make decisions by performing actions in an environment to achieve a goal. The concept of Markov Decision Processes (MDPs) is central to understanding how reinforcement learning works. An MDP provides a mathematical framework for modeling decision-making