1) Accelerate Seamless Data Science Workflows – Master tools to optimize and automate data science processes.

2) Building RAG Agents with LLMs – Learn to create Retrieval-Augmented Generation agents with large language models.

3) Enhance Your Language Model with RAG – Boost the capabilities of your AI models with RAG techniques.

4) Build a Brain in 10 Minutes – Dive into neural network basics and create a functioning AI brain quickly.

5) Generative AI Explained – Get an in-depth look at generative AI technology and applications.

With NVIDIA’s expertise and accessible format, these free courses are a fantastic opportunity to upskill in AI without any fees. Seize the chance to learn from the best and fuel your career growth in AI!

Here are the details of the free courses…

1) Accelerate Data Science Workflows with Zero Code Changes

In this course, you’ll learn to use RAPIDS to speed up your CPU-based data science workflows.

About this Course

Across industries, modern data science requires large amounts of data to be processed quickly and efficiently. These workloads need to be accelerated to ensure prompt results and increase overall productivity. NVIDIA RAPIDS offers a seamless experience to enable GPU-acceleration for many existing data science tasks with zero code changes.

Learning Objectives

In this course, you’ll learn to use RAPIDS to speed up your CPU-based data science workflows. By participating in this workshop, you’ll :Understand the benefits of a unified workflow across CPUs and GPUs for data science tasks. Learn how to GPU-accelerate various data processing and machine learning workflows with zero code changes. Experience the significant reduction in processing time when workflows are GPU-accelerated.

Topics Covered

In this course, you’ll learn to use RAPIDS to speed up your CPU-based data science workflows.

Course Outline

• Understand the benefits of a unified workflow across CPUs and GPUs for data science tasks. 
• Learn how to GPU-accelerate various data processing and machine learning workflows with zero code changes. 
• Experience the significant reduction in processing time when workflows are GPU-accelerated. – LEARN

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2) Building RAG Agents with LLMs

Agents powered by large language models (LLMs) have shown great retrieval capability for using tools, looking at documents, and plan their approaches. This course will show you how to deploy an agent system in practice with the flexibility to scale up your system to meet the demands of users and customers.

About this Course

This course is free for a limited time.

The evolution and adoption of large language models (LLMs) have been nothing short of revolutionary, with retrieval-based systems at the forefront of this technological leap. These models are not just tools for automation; they are partners in enhancing productivity, capable of holding informed conversations by interacting with a vast array of tools and documents. This course is designed for those eager to explore the potential of these systems, focusing on practical deployment and the efficient implementation required to manage the considerable demands of both users and deep learning models. As we delve into the intricacies of LLMs, participants will gain insights into advanced orchestration techniques that include internal reasoning, dialog management, and effective tooling strategies.

Learning Objectives

The goal of the course is to teach participants how to:

• Compose an LLM system that can interact predictably with a user by leveraging internal and external reasoning components.
• Design a dialog management and document reasoning system that maintains state and coerces information into structured formats.
• Leverage embedding models for efficient similarity queries for content retrieval and dialog guardrailing.
• Implement, modularize, and evaluate a RAG agent that can answer questions about the research papers in its dataset without any fine-tuning.

By the end of this workshop, participants will have a solid understanding of RAG agents and the tools necessary to develop their own LLM applications.

Topics Covered

The workshop includes topics such as LLM Inference Interfaces, Pipeline Design with LangChain, Gradio, and LangServe, Dialog Management with Running States, Working with Documents, Embeddings for Semantic Similarity and Guardrailing, and Vector Stores for RAG Agents. Each of these sections is designed to equip participants with the knowledge and skills necessary to develop and deploy advanced LLM systems effectively.

Course Outline

• Introduction to the workshop and setting up the environment.
• Exploration of LLM inference interfaces and microservices.
• Designing LLM pipelines using LangChain, Gradio, and LangServe.
• Managing dialog states and integrating knowledge extraction.
• Strategies for working with long-form documents.
• Utilizing embeddings for semantic similarity and guardrailing.
• Implementing vector stores for efficient document retrieval.
• Evaluation, assessment, and certification. – LEARN

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3) Augment your LLM Using Retrieval Augmented Generation

In this introductory course, we will provide a high-level overview of Retrieval Augmented Generation and how it improves Generative AI (GenAI).

About this Course

Retrieval Augmented Generation (RAG) – Introduced by Facebook AI Research in 2020, is an architecture used to optimize the output of an LLM with dynamic, domain specific data without the need of retraining the model. RAG is an end-to-end architecture that combines an information retrieval component with a response generator. In this introduction we provide a starting point using components we at NVIDIA have used internally. This workflow will jumpstart you on your LLM and RAG journey.

Learning Objectives

• Understand the basics of Retrieval Augmented Generation.
• Learn about the RAG retreival process
• Learn about NVIDIA AI Foundations and the components that constitue a RAG model.

Topics Covered

• Large Language Models (LLMs)
• Retrieval Augmented Generation (RAG)

Course Outline

• Retrieval-augmented Generation Explained, also called RAG
• The RAG ingestion and retrieval processes
• NVIDIA’s Canonical RAG model on NV AI Foundations
• Summary of what we have learned – LEARN

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4) Building A Brain in 10 Minutes

The notebook explores the biological inspiration for early neural networks.

About this Course

This notebook explores the biological and psychological inspirations to the world’s first neural networks.

Learning Objectives

The goals of this exercise include:

• Exploring how neural networks use data to learn.
• Understanding the math behind a neuron.

Topics Covered

• AI Data
• Neurons
• TensorFlow 2

Course Outline

• Data
• Building a Neuron
• Initiate Training
• Evaluating the Model – LEARN

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5) Generative AI Explained

In this no-coding course, learn Generative AI concepts and applications, as well as the challenges and opportunities in this exciting field.

About this Course

Generative AI describes technologies that are used to generate new content based on a variety of inputs. In recent time, Generative AI involves the use of neural networks to identify patterns and structures within existing data to generate new content. In this course, you will learn Generative AI concepts, applications, as well as the challenges and opportunities in this exciting field. 

Learning Objectives

Upon completion, you will have a basic understanding of Generative AI and be able to more effectively use the various tools built on this technology. 

Topics Covered

This no coding course provides an overview of Generative AI concepts and applications, as well as the challenges and opportunities in this exciting field. 

Course Outline

• Define Generative AI and explain how Generative AI works
• Describe various Generative AI applications
• Explain the challenges and opportunities in Generative AI – LEARN

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