March 8, Global AILeadersNvidia（NVIDIA) launched the Generative AI (AIGC) professional certification on its official website. Passing the exam will earn industry-recognized certification.authorityCertificate.
At the same time, NVIDIA has also launched corresponding training courses, including generative AI explanation, introduction to deep learning/basic knowledge, Transformer-based natural language processing, customized application development using large language models, deployment, customization, and fine-tuning of large language models, etc., to help students pass the exam smoothly.
This certification supports remote registration and examination, suitable for software engineers, data engineers, cloud solution architects, AI DevOps engineers, etc., which can increase employment competitiveness and become experts in the field of large models.
Certification details: https://www.nvidia.com/en-us/learn/certification/generative-ai-llm-associate/
The certification will begin at NVIDIA’s “2024GTC” artificial intelligence conference on March 18, Pacific Time. This is also NVIDIA’s first 5-yearfirstThere will be more than 900 sessions, more than 20 of which are related to generative AI. NVIDIA will showcase its innovative technologies andup to dateResearch results.
Nvidia founder and CEO Jensen Huang said that generative AI has become the focus of technological innovation as industries and government agencies around the world are actively seeking its transformative capabilities.
Introduction to Generative AI Certification and Exam
The certification is issued by NVIDIA. The exam topics include generative AI and large language models. The exam lasts for 1 hour and includes 50 questions. The exam fee is US$135 (approximately RMB 971) and the exam is conducted online remotely.
Exam details include:
Machine Learning and Neural Network Basics:Explore the basic concepts and algorithms of machine learning and the basic structure of neural networks, including feedforward neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).
Tips for Engineering:In interaction with large language models, input prompts are designed and optimized to improve the relevance and accuracy of model outputs.
Alignment:Ensuring that large models behave in a way that is consistent with human expectations and ethical standards, including addressing issues such as model bias and decision explainability.
Data Analysis and Visualization:Analyze data sets through statistical methods and visualization tools to identify data trends and patterns and support data-driven decision-making.
experiment:Design and execute experiments to test hypotheses, evaluate model performance, and explore peculiarities of data.
Data preprocessing and feature engineering:Prepare data by cleaning, transforming, and selecting important data features to improve the performance and accuracy of machine learning models.
Experimental Design:Plan and build experimental frameworks to systematically test and evaluate different algorithms, models, and parameter configurations.
Python library for large language models:Introduce Python libraries specifically for developing and training large language models, such as Transformers, TensorFlow, PyTorch, etc.
LLM Integration and Deployment:How to integrate large language models into applications and services, including API integration, performance optimization, and model deployment.
Validity period:Certification is valid for two years from the date of issuance. Recertification can be obtained by retaking the exam.
Exam registration address: https://www.nvidia.com/en-us/learn/organizations/contact-us/
Generative AI Course Introduction
In order to help students pass the exam smoothly, NVIDIA has launched a systematic generative AI course, but some of them are charged and not cheap. Of course, if you are a technical expert, it is no problem to take the exam directly without taking the course.
The main training courses are as follows:
Generative AI Explanation: Free, 2 hours;
Introduction to Deep Learning: 8 hours, $90;
Basics of Deep Learning: Professional teacher guidance, 8 hours, $500;
Accelerated Data Science Fundamentals: Instructor-led, 8 hours, $500;
Introduction to Natural Language Processing with Transformers: 6 hours, $30;
Building Transformer-based Applications: Instructor-led, 8 hours, $500;
Application Development Using Large Language Models: Professional Teacher Guidance, 8 hours, $500;
Quick project using LLaMA-2: 3 hours, $30;
Enhance large language models using retrieval-augmented generation: 1 hour, free;
Developing RAG agents using large language models: 6 hours, $30;
Developing RAG Agents Using Large Language Models: Professional Instructor Guidance, 8 hours, $500;
Course details: https://www.nvidia.com/en-us/training/online/