Before we can process language-based data using Artificial Neural Networks (ANNs), we need to convert this data into some kind of a numerical representation. Embedding models are designed for this purpose. They transform language data into dense high-dimensional vectors that preserve the semantic associations between words. These vectors capture the essence of language data in a way that computers can understand and process. This note explores the most popular embedding model architectures, looks into how these models are trained, and discusses their critical role in Natural Language Processing (NLP).

If you are using Anaconda, the popular python distribution for data science, you know that it comes with its own package and environment manager called conda.

There are many tutorials out there, that tell you to update your conda environments using conda update --all. Luckily, you know that this is a terrible idea!

Decentralized clinical trials (DCTs) represent a transformative approach in the field of clinical research. By leveraging technology and innovative methodologies, DCTs offer a way to conduct studies that are less reliant on traditional, centralized site-based models. This shift is not just a logistical change; it’s a strategic one that can significantly impact drug development, patient accessibility, and the overall speed and efficiency of bringing new therapies to market.

Clinical trials are crucial in medical research, helping to determine the safety and efficacy of new treatments. The designs of these trials vary based on their objectives and the phase of the trial. In this note, we’ll explore some of the most popular and impactful clinical trial designs, from the classic randomized controlled trials to innovative adaptive designs.

Anyone involved in clinical trials would be familiar with the term “endpoint”, but it is surprising how many clinical professionals cannot clearly define what the term means or what differentiates an endpoint from an objective. This note is intended to bring some clarity on the matter.

Not all Artificial Neural Networks (ANN) are made the same, each is designed to tackle a specific type of problem. In this note, we’ll explore the most prominent architectures in detail, and learn how they are used in different applications.

Artificial Neural Networks (ANNs) exist in a fascinating intersection of neuroscience and machine learning, giving rise to powerful systems that can emulate intelligent behaviors. This note covers the essential glossary and theory behind this powerful technology.

Matrix factorization (or decomposition) is a fundamental concept in linear algebra that has widespread applications in various fields, including machine learning, data mining, and signal processing. At its core, matrix factorization involves decomposing a matrix into a product of two or more matrices, revealing the underlying structure of the data represented by the original matrix. This technique is especially powerful in uncovering latent features and reducing dimensionality, making it a cornerstone in many modern algorithms and systems.

In the realm of machine learning algorithms, XGBoost stands out as a powerful tool for predictive modeling. Its efficiency, flexibility, and accuracy have made it a favorite among data scientists and machine learning practitioners. This note delves into what XGBoost is, how it works, and demonstrates its implementation using Python with illustrative examples.

When you step into the labyrinth of clinical research, you find yourself surrounded by hedge walls made of acronyms and abbreviations. The clinical world is laden with shorthand that can seem like a secret language to the uninitiated. Yet, behind these cryptic codes lie vital information crucial for patient care. This living note is my personal attempt to keep the alphabet soup of healthcare terminology under control and to unlock the secrets hidden within.