Course Detail

Generative AI Fundamentals

Overview
Course Details
Course Fee

Course Overview

Learn how large language models process inputs, including tokenisation and prompting techniques. Understand risks like cognitive challenges, security vulnerabilities, and privacy regulations. Explore AI use cases and gain insights into leading vendors and benchmarks.

Mode of Delivery

Self-paced course (Online Learning)

Course Description

This course is designed to help learners understand fundamental concepts within the field of Artificial Intelligence. Through the lessons, you will discover key terminologies and concepts, including machine learning, deep learning, natural language processing, generative AI, language models, transformers, and others.

You will gain insights into the mechanics of tokenisation and the sophisticated architecture of transformers. You will learn how large language models (LLMs) process the inputs and the role of prompts in generating outputs. Furthermore, the course will address the critical aspects of employing LLMs, including cognitive constraints, security risks, and privacy and regulatory concerns, equipping you with strategies to mitigate these challenges.

You will also become familiar with the major industry players, from leading AI vendors to the roles of cloud service providers (CPS), enhancing your understanding of the operational landscape of generative AI technologies. The course will allow you to compare different models using both technical and behavioral parameters. It also will introduce you to enhancing LLM capabilities with plugins and the structure of retrieval-augmented generation (RAG) systems. Finally, you will explore multiple case studies showcasing the successful application of LLMs across various industries.

Learning Outcomes

The primary goal of this course is to equip learners with a fundamental understanding of core Generative AI concepts and terms.

Upon completion of the course, learners will be able to:

Identify and explain key terms associated with AI, including machine learning, deep learning, natural language processing, generative AI, and language models.
Explain the tokenisation process.
Describe the transformers' architecture.
Explain the prompt processing based on the transformers' architecture.
Classify the limitations of LLM usage and give examples for each limitation type.
Identify the reasons behind these limitations.
Recognise security limitations.
List mitigation strategies for cognitive and security limitations.
Mitigate privacy and legal (regulatory) limitations.
Recognise and describe major industry players, including leading vendors and cloud service providers.
List and explain technical and behavioral parameters used to characterize language models (LLMs and SLMs).
List the most common LLM benchmarks.
List the main approaches for expanding LLM capabilities.
Classify the LLM plugins and give examples for each plugin type.
Describe the structure and workflow of an RAG system.
Recall cases in different industries where AI was successfully implemented.

Who Should Attend

Individuals with an interest in understanding key concepts and applications of Generative AI and LLMs.

Professionals seeking to explore AI’s limitations and challenges related to cognitive interactions, privacy, and security.

Duration

(1 Hrs)

Registration Fee

A Registration Fee of $10.90 (Incl. of 9% GST) will be collected upon course registration, and It is non-refundable.
* In the event of any class cancellation by Eduquest, the Registration fee will be refunded

Miscellaneous Fees (Incl. of 9% GST)

Admin Fee for Absent and Reschedule: $32.70

Admin Fee for Make-up Session for Partial Absent, Appeal & Re-assessment: $54.50

Miscellaneous Fees refer to any non-compulsory fees which the trainees pay only when applicable. Such fees are normally collected by Eduquest when the need arises.

Fees indicated are per pax and not per proforma invoice or invoice.

Schedule

More Schedule

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This Course Includes:

Duration: (1 Hrs)

Language: English

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