Nvidia Data Science, AI, and ML Training

NVIDIA is the chip maker that became an AI superpower. With its invention of the GPU in 1999, NVIDIA sparked the growth of the PC gaming market, redefined computer graphics, ignited the era of modern AI, and is fueling industrial digitalization across markets. NVIDIA is now a full-stack computing company with data-center-scale offerings that are reshaping industry.
Fundamentals of Deep Learning
Course ID: NV-FUND-DL
Delivery: On-Site or Instructor-led Virtual

Businesses worldwide are using artificial intelligence to solve their greatest challenges. Healthcare professionals use AI to enable more accurate, faster diagnoses in patients. Retail businesses use it to offer personalized customer shopping experiences. Automakers use it to make personal vehicles, shared mobility, and delivery services safer and more efficient. Deep learning is a powerful AI approach that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, and language translation. Using deep learning, computers can learn and recognize patterns from data that are considered too complex or subtle for expert-written software.

Generative AI with Diffusion Models
Course ID: NV-GEN-AI-DM
Delivery: On-Site or Instructor-led Virtual

Thanks to improvements in computing power and scientific theory, Generative AI is more accessible than ever before.Generative AI will play a significant role across industries and will gain significant importance due to its numerous applications such as Creative Content Generation, Data Augmentation, Simulation and Planning, Anomaly Detection, Drug Discovery, and Personalized Recommendations etc. In this course we will take a deeper dive on denoising diffusion models, which are a popular choice for text-to-image pipelines, disrupting several industries.
Building Conversational AI Applications
Course ID: NV-CONV-AI-APPS
Delivery: On-Site or Instructor-led Virtual

Conversational AI is the technology that powers automated messaging and speech-enabled applications, and its applications are used in diverse industries to improve the overall customer experience and customer service efficiency. Conversational AI pipelines are complex and expensive to develop from scratch. In this course, you’ll learn how to build conversational AI services using the NVIDIA® Riva framework. With Riva, developers can create customized language-based AI services for intelligent virtual assistants, virtual customer service agents, real-time transcription, multi-user diarization, chatbots, and much more.

Model Parallelism: Building and Deploying Large Neural Networks
Course ID: NV-MP-DEPLOY-NETW
Delivery: On-Site or Instructor-led Virtual

Very large deep neural networks (DNNs), whether applied to natural language processing (e.g., GPT-3), computer vision (e.g., huge Vision Transformers), or speech AI (e.g., Wave2Vec 2) have certain properties that set them apart from their smaller counterparts. As DNNs become larger and are trained on progressively larger datasets, they can adapt to new tasks with just a handful of training examples, accelerating the route toward general artificial intelligence. Training models that contain tens to hundreds of billions of parameters on vast datasets isn’t trivial and requires a unique combination of AI, high-performance computing (HPC), and systems knowledge. The goal of this course is to demonstrate how to train the largest of neural networks and deploy them to production.
Rapid Application Development Using Large Language Models
Course ID: NV-RAD-LLM
Delivery: On-Site or Instructor-led Virtual

Recent advancements in both the techniques and accessibility of large language models (LLMs) have opened up unprecedented opportunities for businesses to streamline their operations, decrease expenses, and increase productivity at scale. Enterprises can also use LLM-powered apps to provide innovative and improved services to clients or strengthen customer relationships. For example, enterprises could provide customer support via AI virtual assistants or use sentiment analysis apps to extract valuable customer insights.

Efficient Large Language Model (LLM) Customization
Course ID: NV-ELLM
Delivery: On-Site or Instructor-led Virtual

Enterprises need to execute language-related tasks daily, such as text classification, content generation, sentiment analysis, and customer chat support. Large language models can automate these tasks, enabling enterprises to enhance operations, reduce costs, and boost productivity. In this course, you'll go beyond using out-of-the-box pretrained LLMs and learn a variety of techniques to efficiently customize pretrained LLMs for your specific use cases—without engaging in the computationally intensive and expensive process of pretraining your own model or fine-tuning a model's internal weights. Using the open-source NVIDIA NeMo™ framework, you’ll learn prompt engineering and various parameter-efficient fine-tuning methods to customize LLM behavior for your organization.
Data Parallelism: How to Train Deep Learning Models on Multiple GPUs
Course ID: NV-DP-GPU
Delivery: On-Site or Instructor-led Virtual

Modern deep learning challenges leverage increasingly larger datasets and more complex models. As a result, significant computational power is required to train models effectively and efficiently. Learning to distribute data across multiple GPUs during deep learning model training makes possible an incredible wealth of new applications utilizing deep learning. Additionally, the effective use of systems with multiple GPUs reduces training time, allowing for faster application development and much faster iteration cycles. Teams who are able to perform training using multiple GPUs will have an edge, building models trained on more data in shorter periods of time and with greater engineer productivity.

Building Transformer-Based Natural Language Processing Applications
Course ID: NV-BTBN-LPA
Delivery: On-Site or Instructor-led Virtual

Applications for natural language processing (NLP) and generative AI have exploded in the past decade. With the proliferation of applications like chatbots and intelligent virtual assistants, organizations are infusing their businesses with more interactive human-machine experiences. Understanding how transformer-based large language models (LLMs) can be used to manipulate, analyze, and generate text-based data is essential. Modern pretrained LLMs can encapsulate the nuance, context, and sophistication of language, just as humans do. When fine-tuned and deployed correctly, developers can use these LLMs to build powerful NLP applications that provide natural and seamless human-computer interactions within chatbots, AI voice agents, and more. Transformer-based LLMs, such as Bidirectional Encoder Representations from Transformers (BERT), have revolutionized NLP by offering accuracy comparable to human baselines on benchmarks like SQuAD for question answering, entity recognition, intent recognition, sentiment analysis, and more.
Deep Learning Models on Multiple GPUs
Course ID: NV-DLMM-GPU
Delivery: On-Site or Instructor-led Virtual

Modern deep learning challenges leverage increasingly larger datasets and more complex models. As a result, significant computational power is required to train models effectively and efficiently. Learning to distribute data across multiple GPUs during deep learning model training makes possible an incredible wealth of new applications utilizing deep learning. Additionally, the effective use of systems with multiple GPUs reduces training time, allowing for faster application development and much faster iteration cycles. Teams who are able to perform training using multiple GPUs will have an edge, building models trained on more data in shorter periods of time and with greater engineer productivity.

Fundamentals of Accelerated Data Science
Course ID: NV-ACC-DS
Delivery: On-Site or Instructor-led Virtual

Whether you work at a software company that needs to improve customer retention, a financial services company that needs to mitigate risk, or a retail company interested in predicting customer purchasing behavior, your organization is tasked with preparing, managing, and gleaning insights from large volumes of data without wasting critical resources. Traditional CPU-driven data science workflows can be cumbersome, but with the power of GPUs, your teams can make sense of data quickly to drive business decisions.

Fundamentals of Accelerated Computing with CUDA Python
Course ID: NV-ACC-CUDA-PYTH
Delivery: On-Site or Instructor-led Virtual

This workshop teaches you the fundamental tools and techniques for running GPU-accelerated Python applications using CUDA® GPUs and the Numba compiler. You’ll work though dozens of hands-on coding exercises and, at the end of the training, implement a new workflow to accelerate a fully functional linear algebra program originally designed for CPUs, observing impressive performance gains. After the workshop ends, you’ll have additional resources to help you create new GPU-accelerated applications on your own.
Fundamentals of Accelerated Computing with CUDA C/C++
Course ID: NV-ACC-CUDA-C
Delivery: On-Site or Instructor-led Virtual

This workshop teaches the fundamental tools and techniques for accelerating C/C++ applications to run on massively parallel GPUs with CUDA®. You’ll learn how to write code, configure code parallelization with CUDA, optimize memory migration between the CPU and GPU accelerator, and implement the workflow that you’ve learned on a new task—accelerating a fully functional, but CPU-only, particle simulator for observable massive performance gains. At the end of the workshop, you’ll have access to additional resources to create new GPU-accelerated applications on your own.
Accelerating Data Engineering Pipelines
Course ID: NV-ACC-DATA-ENG
Delivery: On-Site or Instructor-led Virtual

Data engineering is the foundation of data science and lays the groundwork for analysis and modeling. In order for organizations to extract knowledge and insights from structured and unstructured data, fast access to accurate and complete datasets is critical. Working with massive amounts of data from disparate sources requires complex infrastructure and expertise. Minor inefficiencies can result in major costs, both in terms of time and money, when scaled across millions to trillions of data points.

Applications of AI for Anomaly Detection
Course ID: NV-APP-AI-AD
Delivery: On-Site or Instructor-led Virtual

Whether your organization needs to monitor cybersecurity threats, fraudulent financial transactions, product defects, or equipment health, artificial intelligence can help catch data abnormalities before they impact your business. AI models can be trained and deployed to automatically analyze datasets, define “normal behavior,” and identify breaches in patterns quickly and effectively. These models can then be used to predict future anomalies. With massive amounts of data available across industries and subtle distinctions between normal and abnormal patterns, it’s critical that organizations use AI to quickly detect anomalies that pose a threat.

Applications of AI for Predictive Maintenance
Course ID: NV-APP-AI-PM
Delivery: On-Site or Instructor-led Virtual

According to the International Society of Automation, $647 billion is lost globally each year due to downtime from machine failure. Organizations across manufacturing, aerospace, energy, and other industrial sectors are overhauling maintenance processes to minimize costs and improve efficiency. With artificial intelligence and machine learning, organizations can apply predictive maintenance to their operation, processing huge amounts of sensor data to detect equipment failure before it happens. Compared to routine-based or time-based preventative maintenance, predictive maintenance gets ahead of the problem and can save a business from costly downtime.