HOT

Hands-on Generative AI Engineering with Large Language Model

Development

Enroll Now

Hands-on Generative AI Engineering with Large Language Model, available at $54.99, with 160 lectures, and has 8 subscribers.

You will learn about Understanding how to build, implement, train, and perform inference on a Large Language Model, such as Transformer (Attention Is All You Need) from scratch. Gaining knowledge of the different components, tools, and frameworks required to build an LLM-based application. Learning how to serve and deploy your LLM-based application from scratch. Engaging in hands-on technical implementations: Notebook, Python scripts, building model as as Python package, train, infer, fine-tune, deploy & more. Receiving guidance on advanced engineering topics in Generative AI with Large Language Models. This course is ideal for individuals who are Beginner Python developers and AI/ML engineers who are curious about Generative AI, Large Language Models, and building applications using the latest AI technologies. or Individuals from other backgrounds or domains who are interested in switching their careers to focus on Generative AI, particularly Large Language Models. or Non-technical individuals who want to gain not only hands-on technical experience but also a high-level overview of this fast-growing field, making it easier for them to follow along and understand the key concepts. It is particularly useful for Beginner Python developers and AI/ML engineers who are curious about Generative AI, Large Language Models, and building applications using the latest AI technologies. or Individuals from other backgrounds or domains who are interested in switching their careers to focus on Generative AI, particularly Large Language Models. or Non-technical individuals who want to gain not only hands-on technical experience but also a high-level overview of this fast-growing field, making it easier for them to follow along and understand the key concepts.

Enroll now: Hands-on Generative AI Engineering with Large Language Model

Summary

Title: Hands-on Generative AI Engineering with Large Language Model

Price: $54.99

Number of Lectures: 160

Number of Published Lectures: 160

Number of Curriculum Items: 160

Number of Published Curriculum Objects: 160

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

  • Understanding how to build, implement, train, and perform inference on a Large Language Model, such as Transformer (Attention Is All You Need) from scratch.
  • Gaining knowledge of the different components, tools, and frameworks required to build an LLM-based application.
  • Learning how to serve and deploy your LLM-based application from scratch.
  • Engaging in hands-on technical implementations: Notebook, Python scripts, building model as as Python package, train, infer, fine-tune, deploy & more.
  • Receiving guidance on advanced engineering topics in Generative AI with Large Language Models.

Who Should Attend

  • Beginner Python developers and AI/ML engineers who are curious about Generative AI, Large Language Models, and building applications using the latest AI technologies.
  • Individuals from other backgrounds or domains who are interested in switching their careers to focus on Generative AI, particularly Large Language Models.
  • Non-technical individuals who want to gain not only hands-on technical experience but also a high-level overview of this fast-growing field, making it easier for them to follow along and understand the key concepts.

Target Audiences

  • Beginner Python developers and AI/ML engineers who are curious about Generative AI, Large Language Models, and building applications using the latest AI technologies.
  • Individuals from other backgrounds or domains who are interested in switching their careers to focus on Generative AI, particularly Large Language Models.
  • Non-technical individuals who want to gain not only hands-on technical experience but also a high-level overview of this fast-growing field, making it easier for them to follow along and understand the key concepts.

Dive into the rapidly evolving world of Generative AI with our comprehensive course, designed for learners eager to build, train, and deploy Large Language Models (LLMs) from scratch.

This course equips you with a wide range of tools, frameworks, and techniques to create your GenAI applications using Large Language Models, including Python, PyTorch, LangChain, LlamaIndex, Hugging Face, FAISS, Chroma, Tavily, Streamlit, Gradio, FastAPI, Docker, and more.

This hands-on course covers essential topics such as implementing Transformers, fine-tuning models, prompt engineering, vector embeddings, vector stores, and creating cutting-edge AI applications like AI Assistants, Chatbots, Retrieval-Augmented Generation (RAG) systems, autonomous agents, and deploying your GenAI applications from scratch using REST APIs and Docker containerization.

By the end of this course, you will have the practical skills and theoretical knowledge needed to engineer and deploy your own LLM-based applications.

Let’s look at our table of contents:

Introduction to the Course

  • Course Objectives

  • Course Structure

  • Learning Paths

Part 1: Software Prerequisites for Python Projects

  • IDE

    • VS Code

    • PyCharm

  • Terminal

    • Windows: PowerShell, etc.

    • macOS: iTerm2, etc.

    • Linux: Bash, etc.

  • Python Installation

    • Python installer

    • Anaconda distribution

  • Python Environment

    • venv

    • conda

  • Python Package Installation

    • PyPI, pip

    • Anaconda, conda

  • Software Used in This Course

Part 2: Introduction to Transformers

  • Introduction to NLP Before and After the Transformer’s Arrival

  • Mastering Transformers Block by Block

  • Transformer Training Process

  • Transformer Inference Process

Part 3: Implementing Transformers from Scratch with PyTorch

  • Introduction to the Training Process Implementation

  • Implementing a Transformer as a Python Package

  • Calling the Training and Inference Processes

  • Experimenting with Notebooks

Part 4: Generative AI with the Hugging Face Ecosystem

  • Introduction to Hugging Face

  • Hugging Face Hubs

    • Models

    • Datasets

    • Spaces

  • Hugging Face Libraries

    • Transformers

    • Datasets

    • Evaluate, etc.

  • Practical Guides with Hugging Face

    • Fine-Tuning a Pre-trained Language Model with Hugging Face

    • End-to-End Fine-Tuning Example

    • Sharing Your Model

Part 5: Components to Build LLM-Based Web Applications

  • Backend Components

    • LLM Orchestration Frameworks: LangChain, LlamaIndex

    • Open-Source vs. Proprietary LLMs

    • Vector Embedding

    • Vector Database

    • Prompt Engineering

  • Frontend Components

    • Python-Based Frontend Frameworks: Streamlit, Gradio

Part 6: Building LLM-Based Web Applications

  • Task-Specific AI Assistants

    • Culinary AI Assistant

    • Marketing AI Assistant

    • Customer AI Assistant

    • SQL-Querying AI Assistant

    • Travel AI Assistant

    • Summarization AI Assistant

    • Interview AI Assistant

  • Simple AI Chatbot

  • RAG (Retrieval-Augmented Generation) Based AI Chatbot

    • Chat with PDF, DOCX, CSV, TXT, Webpage

  • Agent-Based AI Chatbot

    • AI Chatbot with Math Problems

    • AI Chatbot with Search Problems

Part 7: Serving LLM-Based Web Applications

  • Creating the Frontend and Backend as Two Separate Services

  • Communicating Between Frontend and Backend Using a REST API

  • Serving the Application with Docker

    • Install, Run, and Enable Communication Between Frontend and Backend in a Single Docker Container

  • Use Case

    • An LLM-Based Song Recommendation App

Conclusions and Next Steps

  • What We Have Learned

  • Next Steps

Thank You

Course Curriculum

Chapter 1: Introduction to the Course

Lecture 1: Welcome to the course

Lecture 2: Big Picture

Lecture 3: What We Will Learn

Lecture 4: Course Objectives

Lecture 5: Course Structure

Lecture 6: Learning Paths

Chapter 2: Software Prerequisites for Python Projects

Lecture 1: Introduction

Lecture 2: What We Will Learn

Lecture 3: Overview of Software Prerequisites for Python Projects

Lecture 4: Integrated Development Environment (IDE)

Lecture 5: Terminal

Lecture 6: Python Installation

Lecture 7: Python Environment

Lecture 8: Python Package Installation

Lecture 9: What I Used in This Course

Chapter 3: Introduction to Transformer

Lecture 1: Introduction

Lecture 2: What We Will Learn

Lecture 3: NLP Before Transformer's Arrival – Pros & Cons of RNNs

Lecture 4: The Arrival of Transformer

Lecture 5: RNNs vs Transformers

Lecture 6: NLP After Transformer's Arrival

Lecture 7: Objective: Mastering Transformer's Block-by-Block

Lecture 8: Inputs / Outputs

Lecture 9: Tokenizer

Lecture 10: Preparing Inputs for Encoder Part

Lecture 11: Preparing Inputs for Decoder Part

Lecture 12: Preparing Target for Loss Calculation

Lecture 13: Introduction to Encoder / Decoder Inputs

Lecture 14: Input Embedding

Lecture 15: Positional Encoding

Lecture 16: Encoder / Decoder Inputs – Put It All Together

Lecture 17: Introduction to Encoder

Lecture 18: Multi-Head Attention | Self-Attention Mechanism

Lecture 19: Layer Normalization

Lecture 20: Feed Forward

Lecture 21: Residual Connection

Lecture 22: Encoder – Put It All Together

Lecture 23: Introduction to Decoder

Lecture 24: Masked Multi-Head Attention

Lecture 25: Multi-Head Attention for Decoder

Lecture 26: Decoder – Put It All Together

Lecture 27: Prediction Output

Lecture 28: Transformer Building Blocks – Congratulations!

Lecture 29: Transformer's Training Process

Lecture 30: Transformer's Inference Process

Lecture 31: What We Have Learned

Chapter 4: Implementing Transformer from Scratch with PyTorch

Lecture 1: Introduction

Lecture 2: What We Will Learn

Lecture 3: Implementation's Formula

Lecture 4: Implementation Plan

Lecture 5: Training Process Implementation

Lecture 6: Source Code Structure

Lecture 7: Load Config

Lecture 8: Get Dataset

Lecture 9: Get Tokenizer

Lecture 10: Introduction to Mask Functions

Lecture 11: Creating Encoder Mask

Lecture 12: Creating Padding Mask

Lecture 13: Creating Causal Mask

Lecture 14: Creating Decoder Mask

Lecture 15: Data Preprocessor

Lecture 16: Preprocessing Data

Lecture 17: Introduction to Transformer's Building Layers

Lecture 18: Input Embedding

Lecture 19: Positional Encoding

Lecture 20: Multi-Head Attention

Lecture 21: Feed Forward

Lecture 22: Layer Normalization

Lecture 23: Residual Connection

Lecture 24: Projection

Lecture 25: Introduction to Encoder & Decoder Implementation

Lecture 26: Encoder Layer

Lecture 27: Encoder

Lecture 28: Decoder Layer

Lecture 29: Decoder

Lecture 30: Transformer

Lecture 31: Creating Transformer Model

Lecture 32: Softmax

Lecture 33: Cross Entropy Loss

Lecture 34: Introduction to Training Functions

Lecture 35: Train Engine

Lecture 36: Evaluation During Training

Lecture 37: Inference During Training

Lecture 38: Training in Action

Lecture 39: Introduction to Inference

Lecture 40: Inference

Lecture 41: What We Have Learned

Chapter 5: Generative AI with the Hugging Face Ecosystem

Lecture 1: Introduction

Lecture 2: What We Will Learn

Lecture 3: Introduction to Hugging Face

Lecture 4: Hugging Face Ecosystem

Lecture 5: Hugging Face Hubs

Lecture 6: Hugging Face Libraries

Lecture 7: Transformers – HuggingFace's Python Package

Lecture 8: Datasets – HuggingFace's Python Package

Instructors

Rating Distribution

  • 1 stars: 0 votes
  • 2 stars: 0 votes
  • 3 stars: 0 votes
  • 4 stars: 0 votes
  • 5 stars: 0 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

You may also like

Email Marketing. Increase Sales With Email Marketing!

TikTok Marketing Domination : Mastering Marketing Strategies

Video Email Marketing Made Easy