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How to Calculate Jaccard Similarity in Python
Understanding the similarity between two objects is a universal problem. In machine learning, you can use similarity measures for various issues. These include object detection, classification and segmentation tasks in computer vision and similarity between text...
Top 12 Python Libraries for Data Science and Machine Learning
Machine learning is the science of programming a computer to learn from different data and perform inference. Yesteryear, machine learning tasks involved manual coding all of the algorithms and mathematical and statistical formulae. Nowadays, we have fantastic...
The History of Reinforcement Learning
Reinforcement learning (RL) is an exciting and rapidly developing area of machine learning that significantly impacts the future of technology and our everyday lives. RL is a field separate from supervised and unsupervised learning focusing on solving problems through...
Introduction to Pandas: A Complete Tutorial for Beginners
Pandas is an open-source library providing high-performance, easy-to-use data structures, and data analysis tools for Python. It is one of the fundamental tools for data scientists and can be thought of like Python's Excel. With Pandas, you can work with many...
The Best Books For Machine Learning for Both Beginners and Experts
Machine learning (ML) is an exciting and rapidly expanding domain in Computer Science. ML is a field of study devoted to the automated improvement of computer algorithms through exposure to data. The knowledge base underneath ML consists of a broad range in topics in...
The History of Machine Learning
Machine learning is an exciting and rapidly developing field of study centered around the automated improvement (learning) of computer algorithms through experience with data. Through persistent innovation and research, the capabilities of machine learning are now in...
5 Significant Benefits of Online Learning for Data Science
The internet has made access to information very easy and affordable. Technology has been completely integrated into how we learn and how we work. It can all be supplemented or provided by online education from primary school to degree level. Learning within a...
7 Best Tips to Help Get a Data Scientist Job From Scratch
You have developed a passion and now you want to embark on a new career, but you are unsure where to start to enter the space of data science. This post will provide you with clear, practical steps to get on the road to a rewarding and stimulating career path. The...
Paper Reading #2: XLNet Explained
One of the most celebrated, recent advancements in language understanding is the XLNet model from Carnegie Mellon University and Google. It takes the "best-of-both-worlds" approach by combining auto-encoding and autoregressive language modeling to achieve...
9 Best Tips For Early Career Research Focused Data Scientists
Embarking on a career in data science is an exciting challenge that requires a lot of initiative and a desire to learn and apply knowledge quickly. For research scientists, there is an emphasis on experimentation and scientific discovery. The methods and objectives...
Type Aliases in C++: A Guide to typedef and using
The typedef keyword in C++ is a powerful feature that allows you to create aliases for existing data types. Whether you're working with complex data structures, function pointers, or simply want to make your code more readable, understanding typedef is essential for...
How to Convert Numbers to Strings in C++
Converting numbers to strings is a common requirement in C++ programming, whether you're formatting output, processing data, or preparing values for display. In this guide, we'll explore several methods to convert numbers to strings in C++, each with its own...
How to Use HashMap in C++
C++'s unordered_map (commonly known as HashMap in other languages) is a powerful container that stores key-value pairs using hash tables. It provides average constant-time complexity for insertions, deletions, and lookups, making it an essential tool for efficient...
How to Calculate Cohen’s Kappa in R
Inter-rater reliability is crucial in research involving multiple raters or judges. Cohen's Kappa stands out as a robust statistic that accounts for chance agreement, making it particularly valuable in fields like psychology, medicine, and education. This...
Counting Model Parameters in PyTorch
In deep learning, parameters are the backbone of every neural network. Whether you're building a simple classifier or a complex deep learning model, understanding how to manage parameters effectively in PyTorch is crucial for success. This comprehensive guide will...
Solve “Function Was Not Declared in this Scope” Error in C++
Introduction The "function was not declared in this scope" error is one of the most common compilation errors encountered by C++ developers. This error occurs when you try to use a function that hasn't been properly declared or made visible to the compiler at the...
Simpson’s Diversity Index: Calculating Species Dominance and Evenness
Underwater view of a coral in the Great Barrier Reef off the coast of Queensland near Cairns, Australia. Image credit: Alexandre.ROSA / Shutterstock Simpson's Diversity Index is a fundamental tool in ecological research that measures both species richness and evenness...
Shannon Diversity Index and Equitability: Understanding Biodiversity Metrics
Bottom-up view of a mangrove forest canopy, showcasing nature's approach to carbon capture. Image credit: Fahroni / Shutterstock In the realm of ecological research, understanding and quantifying biodiversity is crucial for conservation efforts, ecosystem management,...
R vs. R-Squared: Understanding the Key Differences
In statistical analysis, R (correlation coefficient) and R² (coefficient of determination) are two related but distinct measures that help us understand relationships between variables. While they're mathematically connected, they serve different purposes and provide...
Understanding Y-hat: Predicted Values in Regression Analysis
In regression analysis, ŷ (pronounced "y-hat") represents the predicted or fitted value of the dependent variable. It's a fundamental concept that helps us understand how well our regression model performs and make predictions for new data points. Contents What is ŷ...
Suf is a senior advisor in data science with deep expertise in Natural Language Processing, Complex Networks, and Anomaly Detection. Formerly a postdoctoral research fellow, he applied advanced physics techniques to tackle real-world, data-heavy industry challenges. Before that, he was a particle physicist at the ATLAS Experiment of the Large Hadron Collider. Now, he’s focused on bringing more fun and curiosity to the world of science and research online.