Llm Engineering: Master Ai & Large Language Models (Llms)

Published 9/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 5.65 GB | Duration: 7h 26m

Master Generative AI and Large Language Models (LLMs). Explore and deploy LLM applications, learn fundamental theory.

What you’ll learn
Design and develop a full solution to a given business problem by selecting, training and applying LLMs
Compare and contrast the latest techniques for improving the performance of your LLM solution, such as RAG, fine-tuning and agentic workflows
Weigh up the leading 10 frontier and 10 open-source LLMs, and be able to select the best choice for a given task
Solve problems by applying leading open-source platforms, frameworks and tools, including Hugging Face, Gradio and Weights & Biases
State the common AI paradigms, and identify the types of business problems most suitable for each
Define fundamental data science concepts around deep learning, including training vs inference, generalizing vs overfitting, and the key ideas behind the NN
Describe core concepts such as Generative AI, LLMs and the Transformer Architecture, and discuss what can be achieved with state-of-the-art performance
Explain how LLMs work in sufficient detail to be able to train and test them, apply them to new scenarios, and diagnose & fix common issues
Implement LLM solutions in Python using frontier and open-source models with both APIs and direct inference
Execute code to write documents, answer questions and generate images.

Requirements
Familiarity with Python. This course will not cover Python basics and is completed in Python.

Description
Mastering Generative AI and LLMs: An 8-Week Hands-On JourneyAccelerate your career in AI with practical, real-world projects led by industry veteran Ed Donner. Build advanced Generative AI products, experiment with over 20 groundbreaking models, and master state-of-the-art techniques like RAG, QLoRA, and Agents.What you’ll learn• Build advanced Generative AI products using cutting-edge models and frameworks.• Experiment with over 20 groundbreaking AI models, including Frontier and Open-Source models.• Develop proficiency with platforms like HuggingFace, LangChain, and Gradio.• Implement state-of-the-art techniques such as RAG (Retrieval-Augmented Generation), QLoRA fine-tuning, and Agents.• Create real-world AI applications, including:• A multi-modal customer support assistant that interacts with text, sound, and images.• An AI knowledge worker that can answer any question about a company based on its shared drive.• An AI programmer that optimizes software, achieving performance improvements of over 60,000 times.• An ecommerce application that accurately predicts prices of unseen products.• Transition from inference to training, fine-tuning both Frontier and Open-Source models.• Deploy AI products to production with polished user interfaces and advanced capabilities.• Level up your AI and LLM engineering skills to be at the forefront of the industry.About the InstructorI’m Ed Donner, an entrepreneur and leader in AI and technology with over 20 years of experience. I’ve co-founded and sold my own AI startup, started a second one, and led teams in top-tier financial institutions and startups around the world. I’m passionate about bringing others into this exciting field and helping them become experts at the forefront of the industry.Why This Course?• Hands-On Learning: The best way to learn is by doing. You’ll engage in practical exercises, building real-world AI applications that deliver stunning results.• Cutting-Edge Techniques: Stay ahead of the curve by learning the latest frameworks and techniques, including RAG, QLoRA, and Agents.• Accessible Content: Designed for learners at all levels. Step-by-step instructions, practical exercises, cheat sheets, and plenty of resources are provided.• No Advanced Math Required: The course focuses on practical application. No calculus or linear algebra is needed to master LLM engineering.Course StructureWeek 1: Foundations and First Projects• Dive into the fundamentals of Transformers.• Experiment with six leading Frontier Models.• Build your first business Gen AI product that scrapes the web, makes decisions, and creates formatted sales brochures.Week 2: Frontier APIs and Customer Service Chatbots• Explore Frontier APIs and interact with three leading models.• Develop a customer service chatbot with a sharp UI that can interact with text, images, audio, and utilize tools or agents.Week 3: Embracing Open-Source Models• Discover the world of Open-Source models using HuggingFace.• Tackle 10 common Gen AI use cases, from translation to image generation.• Build a product to generate meeting minutes and action items from recordings.Week 4: LLM Selection and Code Generation• Understand the differences between LLMs and how to select the best one for your business tasks.• Use LLMs to generate code and build a product that translates code from Python to C++, achieving performance improvements of over 60,000 times.Week 5: Retrieval-Augmented Generation (RAG)• Master RAG to improve the accuracy of your solutions.• Become proficient with vector embeddings and explore vectors in popular open-source vector datastores.• Build a full business solution similar to real products on the market today.Week 6: Transitioning to Training• Move from inference to training.• Fine-tune a Frontier model to solve a real business problem.• Build your own specialized model, marking a significant milestone in your AI journey.Week 7: Advanced Training Techniques• Dive into advanced training techniques like QLoRA fine-tuning.• Train an open-source model to outperform Frontier models for specific tasks.• Tackle challenging projects that push your skills to the next level.Week 8: Deployment and Finalization• Deploy your commercial product to production with a polished UI.• Enhance capabilities using Agents.• Deliver your first productionized, agentized, fine-tuned LLM model.• Celebrate your mastery of AI and LLM engineering, ready for a new phase in your career.

Overview
Section 1: Week 1 – Build Your First LLM Product: Exploring Frontier Models & Transformers

Lecture 1 Day 1 – Mastering LLM Engineering: From Basics to Outperforming GPT-4 in 8 Weeks

Lecture 2 Day 1 – Getting Started with Generative AI: First Steps in LLM Project Setup

Lecture 3 Day 1 – Building a Web Page Summarizer with OpenAI GPT-4: Instant Gratification

Lecture 4 Day 1 – Mastering OpenAI API: Write Code for Frontier Models in Generative AI

Lecture 5 Day 2 – Generative AI Course Structure: 8 Weeks to LLM Mastery

Lecture 6 Day 2 – Exploring Frontier LLMs: ChatGPT, Claude, Gemini and more

Lecture 7 Day 3 – Frontier LLMs: Exploring Strengths and Weaknesses of Top Gen AI Models

Lecture 8 Day 3 – ChatGPT vs Other LLMs: Strengths, Weaknesses, and Complementary Models

Lecture 9 Day 3 – Claude AI: Exploring Capabilities and Limitations of the Frontier Model

Lecture 10 Day 3 – Comparing Gemini AI to Other Frontier Models: Strengths and Limitations

Lecture 11 Day 3 – Comparing Frontier LLMs: Command-R Plus, Meta AI, & Perplexity AI Models

Lecture 12 Day 3 – Comparing Top AI Models: GPT-4, Claude, and Gemini in Leadership Battle

Lecture 13 Day 4 – AI Leadership Battle: Analyzing GPT-4, Claude-3, and Gemini-1.5 Pitches

Lecture 14 Day 4 – Gen AI Breakthroughs: Transformer Models & Emergent Intelligence

Lecture 15 Day 4 – Tokenization in LLMs: How GPT Processes Text for Natural Language Tasks

Lecture 16 Day 4 – Understanding Context Windows: Maximizing LLM Performance and Memory

Lecture 17 Day 5 – Implementing One-Shot Prompting with OpenAI for Business Applications

Lecture 18 Day 5 – How to Use GPT-4 for JSON Generation in Python: AI-Powered Web Scraping

Lecture 19 Day 5 – Building a Full Business Solution with Generative AI and OpenAI’s API

Lecture 20 Day 5 – Extending Gen AI: Multi-Shot Prompting & Translation Techniques

Section 2: Week 2 – Build a Multi-Modal Chatbot: LLMs, Gradio UI, and Agents in Action

Lecture 21 Day 1 – Mastering Multiple AI APIs: OpenAI, Claude, and Gemini for LLM Engineers

Lecture 22 Day 1 – Streaming AI Responses: Implementing Real-Time LLM Output in Python

Lecture 23 Day 1 – How to Create Adversarial AI Conversations Using OpenAI and Claude APIs

Lecture 24 Day 1 – AI Tools: Exploring Transformers & Frontier LLMs for Developers

Lecture 25 Day 2 – Building AI UIs with Gradio: Quick Prototyping for LLM Engineers

Lecture 26 Day 2 – Gradio Tutorial: Create Interactive AI Interfaces for OpenAI GPT Models

Lecture 27 Day 2 – Implementing Streaming Responses with GPT and Claude in Gradio UI

Lecture 28 Day 2 – Building a Multi-Model AI Chat Interface with Gradio: GPT vs Claude

Lecture 29 Day 2 – Building Advanced AI UIs: From OpenAI API to Chat Interfaces with Gradio

Lecture 30 Day 3 – Building AI Chatbots: Mastering Gradio for Customer Support Assistants

Lecture 31 Day 3 – Build a Conversational AI Chatbot with OpenAI & Gradio: Step-by-Step

Lecture 32 Day 3 – Enhancing Chatbots with Multi-Shot Prompting and Context Enrichment

Lecture 33 Day 3 – Mastering AI Tools: Empowering LLMs to Run Code on Your Machine

Lecture 34 Day 4 – Using AI Tools with LLMs: Enhancing Large Language Model Capabilities

Lecture 35 Day 4 – Building an AI Airline Assistant: Implementing Tools with OpenAI GPT-4

Lecture 36 Day 4 – How to Equip LLMs with Custom Tools: OpenAI Function Calling Tutorial

Lecture 37 Day 4 – Mastering AI Tools: Building Advanced LLM-Powered Assistants with APIs

Lecture 38 Day 5 – Multimodal AI Assistants: Integrating Image and Sound Generation

Lecture 39 Day 5 – Multimodal AI: Integrating DALL-E 3 Image Generation in JupyterLab

Lecture 40 Day 5 – Build a Multimodal AI Agent: Integrating Audio & Image Tools

Lecture 41 Day 5 – How to Build a Multimodal AI Assistant: Integrating Tools and Agents

Section 3: Week 3 – Open-Source Gen AI: Building Automated Solutions with HuggingFace

Lecture 42 Day 1 – Hugging Face Tutorial: Exploring Open-Source AI Models and Datasets

Lecture 43 Day 1 – Exploring HuggingFace Hub: Models, Datasets & Spaces for AI Developers

Lecture 44 Day 1 – Intro to Google Colab: Cloud Jupyter Notebooks for Machine Learning

Lecture 45 Day 1 – Hugging Face Integration with Google Colab: Secrets and API Keys Setup

Lecture 46 Day 1 – Mastering Google Colab: Run Open-Source AI Models with Hugging Face

Lecture 47 Day 2 – Hugging Face Transformers: Using Pipelines for AI Tasks in Python

Lecture 48 Day 2 – Hugging Face Pipelines: Simplifying AI Tasks with Transformers Library

Lecture 49 Day 2 – Mastering HuggingFace Pipelines: Efficient AI Inference for ML Tasks

Lecture 50 Day 3 – Exploring Tokenizers in Open-Source AI: Llama, Phi-2, Qwen, & Starcoder

Lecture 51 Day 3 – Tokenization Techniques in AI: Using AutoTokenizer with LLAMA 3.1 Model

Lecture 52 Day 3 – Comparing Tokenizers: Llama, PHI-3, and QWEN2 for Open-Source AI Models

Lecture 53 Day 3 – Hugging Face Tokenizers: Preparing for Advanced AI Text Generation

Lecture 54 Day 4 – Hugging Face Model Class: Running Inference on Open-Source AI Models

Lecture 55 Day 4 – Hugging Face Transformers: Loading & Quantizing LLMs with Bits & Bytes

Lecture 56 Day 4 – Hugging Face Transformers: Generating Jokes with Open-Source AI Models

Lecture 57 Day 4 – Mastering Hugging Face Transformers: Models, Pipelines, and Tokenizers

Lecture 58 Day 5 – Combining Frontier & Open-Source Models for Audio-to-Text Summarization

Lecture 59 Day 5 – Using Hugging Face & OpenAI for AI-Powered Meeting Minutes Generation

Lecture 60 Day 5 – Build a Synthetic Test Data Generator: Open-Source AI Model for Business

Aspiring AI engineers and data scientists eager to break into the field of Generative AI and LLMs.,Professionals looking to upskill and stay competitive in the rapidly evolving AI landscape.,Developers interested in building advanced AI applications with practical, hands-on experience.

Password/解压密码www.tbtos.com

转载请注明：0daytown » Llm Engineering: Master Ai & Large Language Models (Llms)