The Murdaugh verdict has been a subject of interest in recent times, with a South Carolina jury finding Alex Murdaugh guilty on two counts of first-degree murder and two counts of weapons possession during a violent crime. This article explores the case from a Bayesian perspective, providing insights into how Bayesian reasoning can be applied …
Time series forecasting has long been regarded as a critical component in myriad fields, ranging from finance and economics to environmental science and engineering. Accurate predictions of future observations enable businesses, researchers, and policymakers to make informed decisions, optimize resource allocation, and mitigate potential risks. However, real-world time series data often exhibit complex patterns and …
Understanding Multivariate Probabilistic Time Series Forecasting with Informer Read More »
In today’s fast-paced financial world, businesses require accurate and timely financial forecasting to make informed decisions. Multivariate time series forecasting has become a popular technique in finance to predict future values of multiple variables simultaneously. It is a statistical method that models the behavior of several interdependent variables over time, considering the historical data of …
Using Multivariate Time Series Forecasting with Transformers in Finance Read More »
In today’s digital world, every tech giant is exploring ways to enhance their AI capabilities. The latest entrant in this race is Llama, Facebook’s new AI-powered tool that aims to outdo OpenAI’s ChatGPT in the field of natural language processing (NLP). Llama, developed by Facebook’s AI team, is an AI-powered tool that aims to understand …
Introduction Cancer is one of the leading causes of death globally, with millions of people diagnosed each year. Despite significant progress in cancer research, there is still a long way to go in terms of improving diagnosis, treatment, and outcomes. However, with the emergence of artificial intelligence (AI), the future of oncology looks promising, with …
The Future of Oncology: Exploring the Impact of AI on Cancer Research Read More »
Visual anomaly detection is the process of identifying unusual or abnormal events or patterns within visual data. It is a critical technology that enables various industries to detect, diagnose, and solve problems that may go unnoticed otherwise. The ability to detect visual anomalies can provide significant benefits across multiple sectors, including manufacturing, healthcare, finance, and …
The Future of Visual Anomaly Detection: Emerging Trends and Technologies Read More »
Introduction Bayesian inference is a powerful tool that allows us to make predictions based on uncertain data. It has wide-ranging applications, from natural language processing to computer vision, and is particularly useful for problems that involve high-dimensional probability distributions. However, traditional methods for performing Bayesian inference can be computationally expensive, especially when the number of …
Stein Variational Gradient Descent: A Game Changer for Bayesian Inference Read More »
In the world of machine learning, there is a constant push to build more complex and accurate models. However, this often requires large amounts of data and computing power, which can be a significant barrier for many organizations. Transfer learning is a technique that has emerged as a solution to this problem, allowing organizations to …
The Rise of Transfer Learning in Machine Learning Read More »
Electronic medical records (EMRs) are digital versions of the traditional paper-based medical records that healthcare providers have used for decades. EMRs provide a number of benefits over paper records, including the ability to access patient information quickly and easily, improved data security, and the ability to easily share patient information with other healthcare providers. However, …
Zero-shot learning is a type of machine learning approach that allows a model to classify items into categories that it has never seen before, based on the similarities and relationships between categories that it has learned during training. This is in contrast to traditional machine learning approaches, which require the model to be trained on …
