Rapid Application Development Using Large Language Models Training
Course Authors
Rapid Application Development Using Large Language Models Training Hands-on (Online, Onsite, and Classroom Live)
| Commitment | 1 Day, 7-8 hours a day. |
| Language | English |
| User Ratings | Average User Rating 4.8 See what learners said |
| Price | REQUEST |
| Delivery Options | Instructor-Led Onsite, Online, and Classroom Live |
COURSE OVERVIEW
In this Rapid Application Development Using Large Language Models Training course, you’ll gain a strong understanding and practical knowledge of LLM application development by exploring the open-sourced ecosystem, including pre-trained LLMs, that can help you get started quickly developing LLM-based applications.
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.
Please note that once a booking has been confirmed, it is non-refundable. This means that after you have confirmed your seat for an event, it cannot be cancelled and no refund will be issued, regardless of attendance.
WHAT'S INCLUDED?
- 1 day of Rapid Application Development Using Large Language Models Training with an expert instructor
- Rapid Application Development Using Large Language Models Electronic Course Guide
- Certificate of Completion
- 100% Satisfaction Guarantee
RESOURCES
- Rapid Application Development Using Large Language Models – https://www.wiley.com/
- Rapid Application Development Using Large Language Models – https://www.packtpub.com/
- Rapid Application Development Using Large Language Models – https://store.logicaloperations.com/
- Rapid Application Development Using Large Language Models – https://us.artechhouse.com/
- Rapid Application Development Using Large Language Models Training – https://www.amazon.com/
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- Rapid Application Development Using Large Language Models Training
ADDITIONAL INFORMATION
COURSE OBJECTIVES
Upon completion of this Rapid Application Development Using Large Language Models Training course, participants can:
- Find, pull in, and experiment with the HuggingFace model repository and the associated transformers API
- Use encoder models for tasks like semantic analysis, embedding, question-answering, and zero-shot classification
- Use decoder models to generate sequences like code, unbounded answers, and conversations
- Use state management and composition techniques to guide LLMs for safe, effective, and accurate conversation
CUSTOMIZE IT
- We can adapt this Rapid Application Development Using Large Language Models Training course to your group’s background and work requirements at little to no added cost.
- If you are familiar with some aspects of this Rapid Application Development Using Large Language Models course, we can omit or shorten their discussion.
- We can adjust the emphasis placed on the various topics or build the Rapid Application Development Using Large Language Models course around the mix of technologies of interest to you (including technologies other than those in this outline).
- If your background is nontechnical, we can exclude the more technical topics, include the topics that may be of special interest to you (e.g., as a manager or policymaker), and present the Rapid Application Development Using Large Language Models course in a manner understandable to lay audiences.
AUDIENCE/TARGET GROUP
The target audience for this Rapid Application Development Using Large Language Models Training Customization course:
- ALL
CLASS PREREQUISITES
The knowledge and skills that a learner must have before attending this Rapid Application Development Using Large Language Models Training course are:
- Introductory deep learning, with comfort with PyTorch and transfer learning preferred. Content covered by DLI’s Getting Started with Deep Learning or Fundamentals of Deep Learning courses, or similar experience is sufficient.
- Intermediate Python experience, including object-oriented programming and libraries. Content covered by Python Tutorial (w3schools.com) or similar experience is sufficient.
COURSE SYLLABUS
Introduction
- Meet the instructor.
- Create an account at courses.nvidia.com/join
From Deep Learning to Large Language Models
- Learn how large language models are structured and how to use them:
- Review deep learning- and class-based reasoning, and see how language modeling falls out of it.
- Discuss transformer architectures, interfaces, and intuitions, as well as how they scale up and alter to make state-of-the-art LLM solutions.
Specialized Encoder Models
- Learn how to look at the different task specifications:
- Explore cutting-edge HuggingFace encoder models.
- Use already-tuned models for interesting tasks such as token classification, sequence classification, range prediction, and zero-shot classification.
Encoder-Decoder Models for Seq2Seq
- Learn about forecasting LLMs for predicting unbounded sequences:
- Introduce a decoder component for autoregressive text generation.
- Discuss cross-attention for sequence-as-context formulations.
- Discuss general approaches for multi-task, zero-shot reasoning.
- Introduce multimodal formulation for sequences, and explore some examples.
Decoder Models for Text Generation
- Learn about decoder-only GPT-style models and how they can be specified and used:
- Explore when decoder-only is good and talk about issues with the formation.
- Discuss model size, special deployment techniques, and considerations.
- Pull in some large text-generation models and see how they work.
Stateful LLMs
- Learn how to elevate language models above stochastic parrots via context injection:
- Show off modern LLM composition techniques for history and state management.
- Discuss retrieval-augmented generation (RAG) for external environment access.
Assessment and Q&A
- Review key learnings.
- Take a code-based assessment to earn a certificate.
This course is part of the following Certifications:
- NVIDIA-Certified Associate: Generative AI Multimodal
- NVIDIA-Certified Associate: Generative AI LLMs
