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Generative AI: Use cases, applications, solutions and implementation

Generative AI demonstrates versatile applications across diverse industries, leveraging its capacity to create novel content, simulate human behavior, and generate innovative outputs based on learned patterns.

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Knowledge graphs in machine learning: Significance, applications and development

Knowledge graphs in ML enable effective data governance by organizing, connecting data, providing context, and fostering intelligent insights for decision-making.

Supervised machine learning

Supervised machine learning: Types, use cases, applications, operational mechanics, techniques and implementation

Supervised learning is a machine learning approach where a model is trained using labeled data to make predictions or classify new, unlabeled data.

Natural Language Processing

Natural Language Processing: A comprehensive overview

Natural language processing is a branch of AI that enables computers to understand and interpret text and spoken words, similar to how humans do.

Transactional chatbot

How to train a transactional chatbot using reinforcement learning?

A transactional chatbot, also known as a task-oriented or goal-oriented chatbot, is a specialized form of artificial intelligence software designed with a clear purpose – to help users achieve a specific goal or complete a specific task.

Generative AI in healthcare: Function-level applications for healthcare operations

This article maps the healthcare operating model, detailing functions, processes, and sub-processes where generative and agentic AI can deliver workflow-specific value.

AI model

How to choose the right AI model for your application?

The sea of AI models available can be overwhelming, but understanding these models and choosing the right one is key to harnessing the full potential of AI for your specific application.

domain specific LLMs

How to train an open-source foundation model into a domain-specific LLM?

A domain-specific language model constitutes a specialized subset of large language models (LLMs), dedicated to producing highly accurate results within a particular domain.

Transfer learning

Accelerating AI model training with transfer learning

Transfer learning is a machine learning approach that involves utilizing knowledge acquired from one task to improve performance on a different but related task.

GenAI in Banking

Generative AI use cases in banking: Enhancing workflows and operational efficiency

Banking is well-suited to generative AI because it operates at the intersection of data, documents, regulations, customer interactions, risk management, and operations.

diffusion models

Demystifying diffusion Models: A comprehensive guide to key concepts and applications

Unlike GANs, diffusion models require only a single model for training and image generation, making them less complex and more efficient for image generation applications.

Foundation models

A comprehensive guide on foundation models

A foundation model is a deep learning algorithm that undergoes pre-training on a massive and diverse dataset, such as images or text.

Security in AI development

Prioritizing security in AI development: Training, building, and deploying models in a secure environment

Companies achieve maximum AI model security by integrating robust security protocols, following best practices, and ensuring adherence to regulations.

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