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Large Language Models Bootcamp

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. 5 DAYS . 40 HOURS . IN-PERSON / ONLINE

Comprehensive, hands-on curriculum to give you a headstart in building Large Language Model Applications.
LLM Bootcamp | Data Science Dojo
$4999

10% OFF

Ratings | LMM Bootcamp | Data Science Dojo 4.95 · 640+ reviews

$4499

Includes:

  • Software subscriptions and cloud services credit up to USD $500.
  • Breakfast, lunch and beverages daily.
  • Course material and access to coding labs, Jupyter notebooks, and hundreds of learning resources for 1 year.

INSTRUCTORS AND GUEST SPEAKERS

Learn From Industry Leaders

COURSE OVERVIEW

Learn to Build and Deploy Custom LLM Applications

Pre-trained Large Language Models like ChatGPT offer impressive capabilities but they cannot be used in scenarios where the underlying data is proprietary and requires industry-specific knowledge. Businesses are rushing to build custom LLM applications that offer enhanced performance, control, customization and most importantly, competitive advantage. This bootcamp offers a comprehensive introduction to get started with building a ChatGPT on your own data. By the end of the bootcamp, you will be capable of building LLM-powered applications on any dataset of your choice.

In collaboration with

Who Is This Course For

Anyone

Anyone interested in getting a headstart by getting a hands-on experience with building LLM applications.

Data professionals

Data professionals who want to supercharge their data skills using cutting-edge generative AI tools and techniques.

Product leaders

Product leaders at enterprises or startups seeking to leverage LLMs to enhance their products, processes and services.

Curriculum Highlights

Generative AI and LLM Fundamentals

A comprehensive introduction to the fundamentals of generative AI, foundation models and Large language models

Canonical Architectures of LLM Applications

An in-depth understanding of various LLM-powered application architectures and their relative tradeoffs

Embeddings and Vector Databases

Hands-on experience with vector databases and vector embeddings

Prompt Engineering

Practical experience with writing effective prompts for your LLM applications

Orchestration Frameworks: LangChain and Llama Index

Practical experience with orchestration frameworks like LangChain and Llama Index

Deployment of LLM Applications

Learn how to deploy your LLM applications using Azure and Hugging Face cloud

Customizing Large Language Models

Practical experience with fine-tuning, parameter efficient tuning and retrieval parameter-efficient + retrieval-augmented approaches

Building An End-to-End Custom LLM Application

A custom LLM application created on selected datasets

$4999

10% OFF

Ratings | LMM Bootcamp | Data Science Dojo 4.95 · 640+ reviews

$4499

Includes:

  • Software subscriptions and cloud services credit up to USD $500.
  • Breakfast, lunch and beverages daily.
  • Course material and access to coding labs, Jupyter notebooks, and hundreds of learning resources for 1 year.

Technologies and Tools

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Earn a Verified Certificate of Completion

In association with
UNM's continuing education | Data Science Dojo

Earn a Large Language Models certificate in association with the University of New Mexico Continuing Education, verifying your skills. Step into the market with a proven and trusted skillset.

Course Syllabus

DAY 1 - LLM Fundamentals

Understanding the LLM Ecosystem

In this module we will understand the common use cases of large language models and fundamental building blocks of such applications. Learners will be introduced to the following topics at a very high level without going into the technical details:

  • Large language models and foundation models
  • Prompts and prompt engineering
  • Context window and token limits
  • Embeddings and vector databases
  • Build custom LLM applications by:
    • Training a new model from scratch
    • Fine-tuning foundation LLMs
    • In-context learning
  • Canonical architecture for an end-to-end LLM application

Adoption Challenges and Risks

In this module, we will explore the primary challenges and risks associated with adopting generative AI technologies. Learners will be introduced to the following topics at a very high level without going into the technical details:

  • Misaligned behavior of AI systems 
  • Handling complex datasets 
  • Limitations due to context length 
  • Managing cost and latency 
  • Addressing prompt brittleness 
  • Ensuring security in AI applications 
  • Achieving reproducibility
  • Evaluating AI performance and outcomes

Evolution of Embedding

In this module, we will be reviewing how embeddings have evolved from the simplest one-hot encoding approach to more recent semantic embeddings approaches. The module will go over the following topics:

  • Review of classical techniques
    • Review of binary/one-hot, bag-of-words (BoW) and TF-IDF techniques for vectorization
    • Capturing local context with n-grams and challenges
    • Semantic Encoding Techniques
      • Overview of Word2Vec and dense word embeddings
      • Application of Word2Vec in text analytics and NLP tasks
    • Text Embeddings
      • Word and sentence embeddings
    • Text similarity measures
      • Dot product, Cosine similarity, Inner product
  • Hands-on Exercise
    • Creating TF-IDF embeddings on a document corpus
    • Calculating similarity between sentences using cosine similarity and dot product

DAY 2 - Vector Databases and Prompt Engineering

Attention Mechanism and Transformers

Dive into the world of large language models, discovering the potent mix of text embeddings, attention mechanisms, and the game-changing transformer model architecture.

  • Attention mechanism and transformer models
    • Encoder decoder
    • Transformer networks: tokenization, embedding, positional encoding and transformers block
    • Attention mechanism
    • Self-Attention
    • Multi-head Attention
    • Transformer models
  • Hands-on Exercise
    • Understanding attention mechanisms: Self-attention for contextual word analysis

Vector Databases

Learn about efficient vector storage and retrieval with vector database, indexing techniques, retrieval methods, and hands-on exercises.

  • Overview
    • Rationale for vector databases
    • Importance of vector databases in LLMs
    • Popular vector databases
  • Different types of search
    • Vector search, text search, hybrid search
  • Indexing techniques
    • Product Quantization (PQ), Locality Sensitive Hashing (LSH) and Hierarchical Navigable Small World (HNSW)
  • Retrieval techniques
    • Cosine Similarity, Nearest Neighbor Search
  • Advanced Retrieval Augmented Generation techniques
    • Limitations of embeddings and similarity in semantic search
    • Query transformation for better retrieval
    • Relevance scoring in hybrid search using Reciprocal Rank Fusion (RRF)
    • Using auto-cut feature to remove irrelevant results dynamically
    • Improving search relevance by using language understanding to re-rank search results
  • Challenges using vector databases in production
    • Scaling optimization
    • Reliability optimization
    • Cost optimization
  • Hands-on Exercise
    • Learn how to perform similarity searches with vectors as input.
    • Learn how to perform queries using vector similarity searches with embedding models and vectors.
    • Learn how to combine the results of a vector search and a keyword (BM25F) search using hybrid search approach.
    • Learn how to use multi-tenancy features for the efficient and secure management of data across multiple users or tenants.
    • Learn how to compress vectors using product quantization to reduce memory footprint.

Semantic Search

Understand how semantic search overcomes the fundamental limitation in lexical search i.e. lack of semantics . Learn how to use embeddings and similarity in order to build a semantic search model.

  • Understanding and Implementing Semantic Search
    • Introduction and importance of semantic search
    • Distinguishing semantic search from lexical search
    • Semantic search using text embedding