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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...
Understanding Adjacency Matrices in C++: A Beginner’s Guide
In this guide, we'll explore how to implement an adjacency matrix in C++, a fundamental data structure for representing graphs. We'll cover both the theoretical aspects and practical implementation, including common operations and optimizations. Table of Contents...
Demystifying Max Heap in C++: Implementation and Best Practices
Max heaps are essential data structures in computer science, particularly valuable for priority-based operations where the highest value has precedence. In this guide, we'll explore various implementations of max heaps in C++, from using the Standard Template Library...
Demystifying Min Heap in C++: Implementation and Best Practices
Min heaps are fundamental data structures in computer science, particularly useful for priority-based operations and efficient sorting. In this guide, we'll explore various ways to implement min heaps in C++, from using the Standard Template Library (STL) to creating...
Cholesky Decomposition in C++: A Comprehensive Guide
In this guide, we'll explore Cholesky Decomposition in C++, a powerful matrix factorization technique specifically designed for symmetric positive-definite matrices. We'll cover both a custom implementation and how to use the Eigen library for optimal performance....
Singular Value Decomposition (SVD) in C++: A Comprehensive Guide
In this guide, we'll explore Singular Value Decomposition (SVD) in C++, a powerful matrix factorization technique widely used in data science, computer vision, and scientific computing. We'll cover both a custom implementation and how to use the Eigen library for...
LU Decomposition in C++: A Comprehensive Guide
In this guide, we'll explore how to implement LU Decomposition in C++, a fundamental technique in numerical linear algebra. We'll cover both a manual implementation and how to use the Eigen library for optimal performance. Table of Contents Introduction Mathematical...
Understanding Dot vs Arrow Operators in C++
In C++, the dot (.) and arrow (->) operators are fundamental tools for accessing object members. While they serve similar purposes, understanding their differences and proper usage is crucial for writing correct and maintainable code. In this guide, we'll explore both...
Comprehensive Guide to Looping in C++
C++ offers several powerful looping constructs that allow you to execute code repeatedly. In this guide, we'll explore the different types of loops available in C++, their use cases, and best practices for writing efficient loop code. Table of Contents For Loops While...
Comprehensive Guide to Looping in Python
Loops are fundamental building blocks in Python programming, allowing us to automate repetitive tasks and process data efficiently. In this guide, we'll explore different types of loops, their use cases, and best practices to write clean and efficient code. Table of...
How To Find Maximum/Minimum Values in a Matrix Using C++
In this guide, we'll explore efficient methods to find maximum and minimum values in matrices using C++. We'll start with basic implementations and progressively optimize them using SIMD instructions and parallel processing. Table of Contents Introduction Mathematical...
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.