Artificial intelligence (AI) has reshaped multiple industries, including finance, where it has automated tasks and enhanced accuracy and efficiency. Yet, a gap still exists between the finance sector and AI community due to proprietary financial data and the specialized knowledge required to analyze it. Therefore, more advanced AI tools are required to democratize the use…
Artificial Intelligence (AI) and complex neural networks are growing rapidly, necessitating efficient hardware to handle power and resource constraints. One potential solution is In-memory computing (IMC) which focuses on developing efficient devices and architectures that can optimize algorithms, circuits, and devices. The explosion of data from the Internet of Things (IoT) has propelled this need…
Local feature image matching techniques often fall short when tested on out-of-domain data, leading to diminished model performance. Given the high costs associated with collecting extensive data sets from every image domain, researchers are focusing on improving model architecture to enhance generalization capabilities. Historically, local feature models like SIFT, SURF, and ORB were used in…
The rise of Artificial Intelligence (AI) is being tapped into by various enterprises, thanks to the innovation it presents. AWS (Amazon Web Services) offers substantial AI solutions and services, and carries a series of courses to enhance an individual's aptitude in such technologies. This report uncovers the leading AI courses by AWS that instill learners…
Google Cloud AI researchers have unveiled a novel pre-training framework called LANISTR, designed to effectively and efficiently manage both structured and unstructured data. LANISTR, which stands for Language, Image, and Structured Data Transformer, addresses a key issue in machine learning; the handling of multimodal data, such as language, images, and structured data, specifically when certain…
Data structures and algorithms are integral tools in creating effective, efficient, and reliable software. By studying them, programmers can enhance their coding abilities and gear up for technical interviews and complex real-world tasks. The following list details the best tutorials on data structures and algorithms to help you thrive in software development and interviews.
1. "Foundations…
Anomaly detection in time series data, which is pivotal for practical applications like monitoring industrial systems and detecting fraudulent activities, has been facing challenges in terms of its metrics. Existing measures such as Precision and Recall, designed for independent and identically distributed (iid) data, fail to entirely capture anomalies, potentially leading to flawed evaluations in…
Anthropic AI's Claude family of models signifies a massive milestone in anomaly detection AI technology. The release of the Claude 3 series has seen a significant expansion in the models' abilities and performance, making them suitable for a broad spectrum of applications that span from text generation to high-level vision processing. This article aims to…
Large language models (LLMs) are renowned for their ability to perform specific tasks due to the principle of fine-tuning their parameters. Full Fine-Tuning (FFT) involves updating all parameters, while Parameter-Efficient Fine-Tuning (PEFT) techniques such as Low-Rank Adaptation (LoRA) update only a small subset, thus reducing memory requirements. LoRA operates by utilizing low-rank matrices, enhancing performance…
The field of Natural Language Processing (NLP) has seen a significant advancement thanks to Large Language Models (LLMs) that are capable of understanding and generating human-like text. This technological progression has revolutionized applications such as machine translation and complex reasoning tasks, and sparked new research and development opportunities.
However, a notable challenge has been the…
Language models are integral to the study of natural language processing (NLP), a field that aims to generate and understand human language. Applications such as machine translation, text summarization, and conversational agents rely heavily on these models. However, effectively assessing these approaches remains a challenge in the NLP community due to their sensitivity to differing…