Data Science Central

Data Science Central is a premier online hub for professionals working with big data. It covers a wide range of topics, from analytics and data integration to visualization. The platform offers a vibrant community where users can engage with each other, access informative articles, seek technical help through forums, stay updated on the latest technologies and tools, and explore job opportunities in the industry.

International, Trade/B2B

English

Online/Digital

Outlet metrics

Domain Authority

Ranking

Global

#360974

United States

#417415

Computers Electronics and Technology/Social Networks and Online Communities

#1389

Traffic sources

Monthly visitors

Articles

How to Build and Optimize High-Performance Deep Neural Networks from Scratch

2 weeks ago | datasciencecentral.com | Vincent Granville

With explainable AI, intuitive parameters easy to fine-tune, versatile, robust, fast to train, without any library other than Numpy. In short, you have full control over all components, allowing for deep customization, and much fewer parameters than in standard architectures. I explore deep neural networks (DNNs) starting from the foundations, introducing a new type of architecture, as much different from machine learning than it is from traditional AI.
How to make AI work in QA

3 weeks ago | datasciencecentral.com | Saqib Jan

AI has radically changed Quality Assurance, breaking old inefficient ways of test automation, promising huge leaps in speed and the ability to test things we otherwise couldn’t easily test before. But getting AI to be trusted by QA teams is a major challenge across the industry. A big part of the difficulty stems from the “black box” nature of AI tools where the internal processing is abstracted away.
Stay ahead of the sales curve with AI-assisted Salesforce integration

1 month ago | datasciencecentral.com | Anas Baig

Any organization with Salesforce in its SaaS sprawl must find a way to integrate it with other systems. For some, this integration could be in the form of a platform-native connector or a custom script. However, these options have limitations: platform-native connectors are restricted to one platform, while custom connectors are difficult to build and maintain. So the remaining option is to use a reliable third-party Salesforce integration tool, like Exalate.
Generative AI in software development: Does faster code come at the cost of quality?

1 month ago | datasciencecentral.com | Scott Thompson

From creating comprehensive essays to writing intriguing fiction, there’s hardly anything untouched by the impact of generative AI. The technology has caught the attention of forward-thinking engineering companies, too. Many have attempted generative AI-assisted code development and seen measurable improvements in overall efficiency. However, the potential of this disruptive technology isn’t limited to code generation.
New Book: 0 and 1 - From Elemental Math to Quantum AI

1 month ago | datasciencecentral.com | Vincent Granville

This book opens up new research areas in theoretical and computational number theory, numerical approximation, dynamical systems, quantum dynamics, and the physics of numbers.
How to Design LLMs that Don't Need Prompt Engineering

1 month ago | datasciencecentral.com | Vincent Granville

Standard LLMs rely on prompt engineering to fix problems (hallucinations, poor response, missing information) that come from issues in the backend architecture. If the backend (corpus processing) is properly built from the ground up, it is possible to offer a full, comprehensive answer to a meaningful prompt, without the need for multiple prompts, rewording your query, having to go through a chat session, or prompt engineering.
From raw data to real-time insights: Building high-speed data pipeline diagrams

1 month ago | datasciencecentral.com | Erika Balla

Data pipeline diagrams function as blueprints that transform unprocessed data into useful information. According to an IBM study, 39% of businesses anticipate increased revenue and reduced operating costs, and 44% of businesses expect quick data to help them make better decisions. These figures demonstrate the importance of fast data pipelines for businesses looking to stay ahead. Businesses that use predictive models driven by AI have seen a 20% improvement in risk management.
How generative AI is reshaping traditional QA strategies

2 months ago | datasciencecentral.com | Saqib Jan

Software development cycles accelerate constantly, pushing quality assurance teams to keep pace. However, the pressure engineering leaders face to ensure quality under the speed and complexity modern pipelines require is also immense. And simply doing more of the same old way of things isn’t enough with the advancing user demands. Interestingly, while much of the focus has been on accelerating coding or transforming creative workflows, GenAI is profoundly reshaping quality assurance.
Geometric deep learning: AI beyond text & images

2 months ago | datasciencecentral.com | Kevin Vu

While traditional deep learning techniques have excelled at handling structured data like images and text, they often struggle when faced with irregular, complex data like molecules and networks of data. Geometric Deep Learning (GDL) is a machine learning methodology suitable for solving problems with irregular and complex data.
Accounting for attention in speech-to-speech AI applications

2 months ago | datasciencecentral.com | Jelani Harper |Vincent Granville |Dan Wilson

The quantum leap forward in natural language technologies attributed to foundation models, LLMs, and modern vocal applications of AI is due in no small part to the mastery of the concept of attention. When training and deploying the aforementioned models, attention mechanisms account for several things. One of the most valuable is allowing models to look back at different parts of a conversation or text and determine how that context relates to present inputs (or lines of text).