What is Tokenization in Artificial Intelligence, and why is it important for machines to understand human language?
Tokenization: The First Step for Machines to Understand Us Introduction Today, we talk to machines through messages, voice assistants, chatbots, and online search. But machines do not understand full sentences like humans. They understand data in small parts.So, to help computers understand language, Artificial Intelligence uses a process called Tokenization. Tokenization is the very first…
Advantage of Using Orthogonal Matrices in Solving Linear Systems
A Stable and Efficient Approach for Solving Linear Systems Introduction When solving a system of linear equations, especially large or complex systems, the choice of method can significantly influence stability, accuracy, and efficiency. One powerful approach involves using orthogonal matrices. An orthogonal matrix is a special type of square matrix whose columns and rows are…
Mastering Class-Level Attribute Patching in Unit Tests
How to Safely Override External-Dependent Class Attributes for Reliable Testing Introduction Unit testing becomes challenging when your class interacts with external resources APIs, databases, network calls, or third-party services. Often, these interactions happen through class-level attributes. Because these attributes live at the class level, they persist across all instances, making testing tricky if you want…
Harnessing Gram–Schmidt Orthogonalization for ML Optimization
Why Orthogonalizing Vectors Strengthens Model Stability, Performance, and Interpretability Introduction In machine learning, optimizing a model often requires understanding the structure and relationships of the data’s underlying vector space. When feature vectors are highly correlated or linearly dependent, many algorithms struggle: gradients misbehave, matrices become unstable, and numerical precision weakens.This is where Gram–Schmidt orthogonalization becomes…
Testing Private Methods Effectively: The Smart and Maintainable Approach
Why Directly Testing Private Methods Is Not the Best Choice and What to Do Instead Introduction In object-oriented programming, classes often contain private methods that encapsulate important business logic. These methods act as internal helpers, breaking down complex operations into smaller, manageable steps. Although they hold significant value, they are intentionally hidden from the outside…
Removing Noise with SVD in the Most Unusual Way
A Counter-Intuitive Approach to Make the Image Even “Cleaner” Introduction When you use Singular Value Decomposition (SVD) to denoise an image, the usual technique is to keep the largest singular values and drop the smaller ones because they mostly contain noise. But here, let’s explore a completely different method—one that goes in the opposite direction.…
Mocking Hard-to-Instantiate Dependencies in Testing
A Simple Strategy to Handle Complex Services in Unit Tests Introduction When a class relies on a dependency that is too complicated, too heavy, or simply impossible to create inside a normal test setup, most developers try many advanced testing techniques. But the easiest way to handle such situations is to directly run the entire…
Picking the Best Way to Run CPU Tasks in Parallel
Why Simple Multithreading Is the Most Powerful Solution Introduction When you have a heavy CPU-bound task and want to speed it up by using multiple cores, your design choices matter. In class-based architectures, the goal is usually to distribute the workload cleanly while keeping the code understandable and efficient. Many developers immediately think about multiprocessing…
Measuring Document Similarity Using Cosine Similarity
Introduction In software development, the Singleton Design Pattern ensures that a class has only one instance throughout the lifecycle of an application and provides a global point of access to it. This pattern is particularly useful for managing shared resources such as configuration settings, logging systems, or database connections. However, implementing a singleton becomes challenging…
Ensuring Thread-Safe Updates in Multithreaded Applications
How Synchronization Prevents Race Conditions and Data Corruption Introduction In multithreaded programming, several threads often operate concurrently to improve performance and resource utilization. However, when these threads attempt to modify a shared resource—like a counter, file, or data structure—simultaneously, it can lead to race conditions. A race condition occurs when the output or state of…
Measuring Document Similarity Using Cosine Similarity
Understanding Why Cosine Metric is Best for Word Frequency Vectors Introduction In the field of Natural Language Processing (NLP) and Information Retrieval, comparing documents to determine how similar they are is a common and crucial task. When documents are represented as word frequency vectors—where each element of the vector corresponds to the frequency or count…
Implementing Automatic Object Removal in Caching Systems
Leveraging Weak References for Efficient Memory Management Introduction In Python, memory management is mostly automatic, thanks to its built-in garbage collector. Developers rarely need to worry about freeing up memory manually because Python keeps track of all objects and removes the ones that are no longer needed. However, a common issue arises when objects form…
Handling Distance Similarity in High-Dimensional Data
Choosing an Appropriate Distance Metric for High-Dimensional Spaces Introduction In high-dimensional spaces, traditional distance metrics like Euclidean distance often lose their effectiveness because the distances between points tend to become almost uniform. This phenomenon is known as the “curse of dimensionality.” When distances between all points appear similar, clustering algorithms like K-Means or Hierarchical Clustering…
Choosing a Robust Distance Metric for K-Nearest Neighbors with Outliers
Why Manhattan Distance is More Reliable Than Euclidean Distance in Handling Outlier Introduction The K-Nearest Neighbors (KNN) algorithm is one of the simplest yet most powerful classification techniques in machine learning. It classifies a data point based on the majority label of its nearest neighbors. The algorithm’s effectiveness, however, heavily depends on the distance metric…
Ensuring Proper Cleanup of Objects with Circular References in Python
Effective Techniques to Prevent Memory Leaks and Maintain Efficient Memory Management Introduction In Python, memory management is mostly automatic, thanks to its built-in garbage collector. Developers rarely need to worry about freeing up memory manually because Python keeps track of all objects and removes the ones that are no longer needed. However, a common issue…
Follow My Blog
Get new content delivered directly to your inbox.
It is not the strongest of the species that survives, nor the most intelligent , but the one most responsive to change.