Python

Suppose you want to output a person’s name and profession. You could write a simple program as follows. # Define variables name = ‘Mark’ profession = ‘Astronaut’ age = 7 # Output info output_string = (‘My name is ‘ + name + ‘, I am ‘ + str(age) + ‘ years old ‘ + ‘and my profession is ‘ + profession + ‘.’) print(output_string) My name is Mark, I am 7 years old and my profession is Astronaut. That approach…

Over the past few years, tranformers have transformed the NLP domain in machine learning. Models like GPT and BERT have set new benchmarks in understanding and generating human language. Now the same principle is been applied to computer vision domain. A recent development in the field of computer vision are vision transformers or ViTs. As detailed in the paper “An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale”, ViTs and transformer-based models are designed to replace convolutional…

Introduction YOLOv8, developed by Ultralytics in 2023, has emerged as one of the unique object detection algorithms in the YOLO series and comes with significant architectural and performance enhancements over its predecessors, like YOLOv5. These improvements include a CSPNet backbone for better feature extraction, an FPN+PAN neck for improved multi-scale object detection, and a shift to an anchor-free approach. These changes significantly improve the model’s accuracy, efficiency, and usability for real-time object detection. Using a GPU with YOLOv8 can significantly…

Introduction Multiple Linear Regression is a fundamental statistical technique used to model the relationship between one dependent variable and multiple independent variables. In Python, tools like scikit-learn and statsmodels provide robust implementations for regression analysis. This tutorial will walk you through implementing, interpreting, and evaluating multiple linear regression models using Python. Prerequisites Before diving into the implementation, ensure you have the following: Basic understanding of Python. You can refer to Python Tutorial for Beginners. Familiarity with scikit-learn for machine learning…

The hill climbing algorithm is one of the earliest and simplest optimization algorithms in artificial intelligence and computer science. It belongs to a category called local search algorithms, which find solutions by making incremental improvements. The algorithm’s name comes from a helpful analogy: imagine a blindfolded hiker trying to reach the top of a hill. Since they can’t see the entire landscape, they can only feel the ground immediately around them. At each step, they move in whatever direction leads…

In my first coding-based statistics course in college, my teacher proposed a question: how can we model the Brownian motion of a single pollen particle in a dish of water? After several misguided attempts, my classmates and I eventually stumbled on the correct answer: a random walk. I later learned that this simple model is used to model all sorts of things, from animal movements to stock price fluctuations. In this article, we’ll explore the mathematical foundations of random walks,…

Data visualization transforms complex information into clear, actionable insights. Seaborn barplots excel at presenting categorical data through elegant, statistical graphics. The library combines the flexibility of Matplotlib with the power of pandas, making it ideal for both quick analyses and publication-ready visualizations. Seaborn barplots offer essential features for data analysis – from basic comparisons to advanced statistical representations. They handle common tasks like comparing sales metrics across regions, analyzing survey responses, and visualizing experiment results.  The library’s intuitive syntax and…

Feature extraction in machine learning transforms raw data into a set of meaningful characteristics, capturing essential information while reducing redundancy. It can involve dimensionality reduction techniques and methods that create new features from existing data. Imagine you’re trying to identify fruits in a market. While you could consider countless attributes (weight, color, texture, shape, smell, etc.), you might realize that just a few key features like color and size are enough to distinguish between apples and oranges. This is exactly…

Policy gradients in reinforcement learning (RL) are a class of algorithms that directly optimize the agent’s policy by estimating the gradient of the expected reward with respect to the policy parameters.  In this tutorial, we explain the policy gradient theorem and its derivation and show how to implement the policy gradient algorithm using PyTorch.  What is the Policy Gradient Theorem? In reinforcement learning, the agent’s policy refers to the algorithm it uses to decide its action based on its observations…