Multilingual Tokenization Efficiency in Large Language Models: A Study on Indian Languages
Authors: Mohamed Azharudeen M, Balaji Dhamodharan
ADaSci's Certified Generative AI Engineer program is an upskilling-linked certification initiative designed to recognize talent in generative AI and large language models. Participants engage in a structured learning track covering industry-relevant modules, culminating in an exam for certification by ADaSci. The program aims to elevate skills and provide recognition to professionals in this rapidly evolving field.
A person who is looking for recognition in the Generative AI field can appear and take this certification. This is a certification program offered by the premier global body of AI and data science professionals and is best suitable for the aspirants who want to start their careers in the data science field.
There are no fixed eligibility criteria for taking this certification. Anyone who is a Generative AI aspirant can take this progam. There is no limit on age, educational qualification, or working experience.
Yes. This is a course-linked certification program. You can undergo a Generative AI learning track included in this program and take the exam in the end for getting the certification
Yes, you can refer to all possible learning resources of your type for a widened knowledge of the field. However, the learning modules added to this program are sufficient to crack the exam.
You can register for this program online at any time by visiting the ADaSci website. Only you need to create your account, pay the program fees, complete the learning modules and take the exam for getting the certification.
We advise to take the exam within 1 year of registering. As the learning modules and resources may get time to time based on the development in the field.
Authors: Mohamed Azharudeen M, Balaji Dhamodharan
Authors: Sriram Gudimella, Rohit Zajaria, Jagmeet Sarna
Authors: Shubhradeep Nandi, Kalpita Roy
Authors: Suvojit Hore, Gayathri Nadella, Sanmathi Vaman Parvatikar
Authors: Varun Aggarwal, Charchit Bahl, Rahav Manoharan, Pushkar Raj
Author: Srinivas Babu Ratnam
This article details the key factors influencing RAG pipeline cost, covering implementation, operation, and data expenses.
HybridRAG integrates Knowledge Graphs and Vector Retrieval to enhance accuracy and speed in complex data extraction tasks.
Adversarial prompts exploit LLM vulnerabilities, causing harmful outputs. This article covers their types, impacts, and defenses.
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