LLM Mastery: ChatGPT, Gemini, Claude, Llama3, OpenAI & APIs

LLM Mastery: ChatGPT, Gemini, Claude, Llama3, OpenAI & APIs, available at $54.99, has an average rating of 4.78, with 158 lectures, based on 214 reviews, and has 1816 subscribers.

You will learn about Functionality of LLMs: Parameters, Weights, Inference, and Neural Networks Understanding Neural Networks Operation of Neural Networks with Tokens in LLMs Transformer Architecture and Mixture of Experts Fine-Tuning and the Creation of the Assistant Model Reinforcement Learning (RLHF) in LLMs LLM Scaling Laws: GPU & Data for Improvements Capabilities and Future Developments of LLMs Use of Tools by LLMs: Calculator, Python Libraries, and More Multimodality and Visual Processing with LLMs Multimodality in Language as in the Movie 'Her' Systems Thinking and Future Prospects for LLMs Self-Improvement after AlphaGo (Self-Improvement) Improvement Possibilities: Prompts, RAG, and Customization Prompt Engineering: Effective Use of LLMs with Chain of Thought and Tree of Thoughts Prompting & More Adaptation of LLMs through System Prompts and Personalization with ChatGPT Memory Long-Term Memory with RAG and GPTs The GPT Store: Everything You Need to Know Using GPTs for Data Analysis, PDFs, or Tetris Programming Embeddings and Vector Databases for RAG Integrating Zapier Actions in GPTs Open-Source vs. Closed-Source LLMs API Basics Usage of the Google Gemini API and Claude API Microsoft Copilot and Its Use in Microsoft 365 GitHub Copilot: The Solution for Programmers The OpenAI API: Features, Pricing Models, and Everything You Need to Know About the OpenAI API Including App Creation Introduction to Google Colab for API Calls to OpenAI Creation of AI Apps and Chatbots with Langchain, Flowise, Vectorshift, LangGraph, CrewAI, Autogen, Langflow & more Creation of AI Agents for Various Tasks like Social Media Contetn with Agency Swarm and Langchain Agents Security in LLMs: Jailbreaks and Prompt Injections & more Comparison of the Best LLMs Google Gemini in Standard Interface and Google Labs with NotebookLM Claude by Anthropic: Overview Everything About Perplexity and POE OpenAI Playground: Features, Billing Account & Temperature of LLMs Google Gemini API: Video Analysis and More Open-Source LLMs: Models and Use of Llama 3, Mixtral, Command R+, and Many More HuggingChat: Interface for Open-Source LLMs Running Local LLMs with Ollama and Building Local Rag Chatbots Groq: Fastest Interface with LPU Installation of LM Studio for Using Local Open-Source like Llama3 LLMs for Maximum Security Using Open-Source Models in LM Studio and Censored vs. Uncensored LLMs Fine-Tuning an Open-Source Model with Huggingface Creating Your Own Apps via APIs in Google Colab with Dall-E, Whisper, GPT-4o, Vision, and More Microsoft Autogen for AI Agents CrewAI for AI Agents Flowise with LangChain Function Calling OpenAI Assistant API with function Calling for AI-Agents in different Frameworks Flowise with Open-Source LLM as ChatBot Security in LLMs and Methods to Hack LLMs Future of LLMs as Operating Systems in Robots and PCs This course is ideal for individuals who are To everyone who wants to learn something new and gain deep insights into LLMs or To entrepreneurs who want to become more efficient and save money or To individuals who are interested in AI and want to build their own models It is particularly useful for To everyone who wants to learn something new and gain deep insights into LLMs or To entrepreneurs who want to become more efficient and save money or To individuals who are interested in AI and want to build their own models.

Enroll now: LLM Mastery: ChatGPT, Gemini, Claude, Llama3, OpenAI & APIs

Summary

Title: LLM Mastery: ChatGPT, Gemini, Claude, Llama3, OpenAI & APIs

Price: $54.99

Average Rating: 4.78

Number of Lectures: 158

Number of Published Lectures: 158

Number of Curriculum Items: 158

Number of Published Curriculum Objects: 158

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Functionality of LLMs: Parameters, Weights, Inference, and Neural Networks
Understanding Neural Networks
Operation of Neural Networks with Tokens in LLMs
Transformer Architecture and Mixture of Experts
Fine-Tuning and the Creation of the Assistant Model
Reinforcement Learning (RLHF) in LLMs
LLM Scaling Laws: GPU & Data for Improvements
Capabilities and Future Developments of LLMs
Use of Tools by LLMs: Calculator, Python Libraries, and More
Multimodality and Visual Processing with LLMs
Multimodality in Language as in the Movie 'Her'
Systems Thinking and Future Prospects for LLMs
Self-Improvement after AlphaGo (Self-Improvement)
Improvement Possibilities: Prompts, RAG, and Customization
Prompt Engineering: Effective Use of LLMs with Chain of Thought and Tree of Thoughts Prompting & More
Adaptation of LLMs through System Prompts and Personalization with ChatGPT Memory
Long-Term Memory with RAG and GPTs
The GPT Store: Everything You Need to Know
Using GPTs for Data Analysis, PDFs, or Tetris Programming
Embeddings and Vector Databases for RAG
Integrating Zapier Actions in GPTs
Open-Source vs. Closed-Source LLMs
API Basics
Usage of the Google Gemini API and Claude API
Microsoft Copilot and Its Use in Microsoft 365
GitHub Copilot: The Solution for Programmers
The OpenAI API: Features, Pricing Models, and Everything You Need to Know About the OpenAI API Including App Creation
Introduction to Google Colab for API Calls to OpenAI
Creation of AI Apps and Chatbots with Langchain, Flowise, Vectorshift, LangGraph, CrewAI, Autogen, Langflow & more
Creation of AI Agents for Various Tasks like Social Media Contetn with Agency Swarm and Langchain Agents
Security in LLMs: Jailbreaks and Prompt Injections & more
Comparison of the Best LLMs
Google Gemini in Standard Interface and Google Labs with NotebookLM
Claude by Anthropic: Overview
Everything About Perplexity and POE
OpenAI Playground: Features, Billing Account & Temperature of LLMs
Google Gemini API: Video Analysis and More
Open-Source LLMs: Models and Use of Llama 3, Mixtral, Command R+, and Many More
HuggingChat: Interface for Open-Source LLMs
Running Local LLMs with Ollama and Building Local Rag Chatbots
Groq: Fastest Interface with LPU
Installation of LM Studio for Using Local Open-Source like Llama3 LLMs for Maximum Security
Using Open-Source Models in LM Studio and Censored vs. Uncensored LLMs
Fine-Tuning an Open-Source Model with Huggingface
Creating Your Own Apps via APIs in Google Colab with Dall-E, Whisper, GPT-4o, Vision, and More
Microsoft Autogen for AI Agents
CrewAI for AI Agents
Flowise with LangChain Function Calling
OpenAI Assistant API with function Calling for AI-Agents in different Frameworks
Flowise with Open-Source LLM as ChatBot
Security in LLMs and Methods to Hack LLMs
Future of LLMs as Operating Systems in Robots and PCs

Who Should Attend

To everyone who wants to learn something new and gain deep insights into LLMs
To entrepreneurs who want to become more efficient and save money
To individuals who are interested in AI and want to build their own models

Target Audiences

To everyone who wants to learn something new and gain deep insights into LLMs
To entrepreneurs who want to become more efficient and save money
To individuals who are interested in AI and want to build their own models

Have you ever thought about how Large Language Models (LLMs) are transforming the world and creating unprecedented opportunities?

“AI won’t take your job, but someone who knows how to use AI might,” says Richard Baldwin.

Are you ready to master the intricacies of LLMs and leverage their full potential for various applications, from data analysis to the creation of chatbots and AI agents?

Then this course is for you!

Dive into ‘LLM Mastery: ChatGPT, Gemini, Claude, Llama3, OpenAI & APIs‘—where you will explore the fundamental and advanced concepts of LLMs, their architectures, and practical applications. Transform your understanding and skills to lead in the AI revolution.

This course is perfect for developers, data scientists, AI enthusiasts, and anyone who wants to be at the forefront of LLM technology. Whether you want to understand neural networks, fine-tune AI models, or develop AI-driven applications, this course offers everything you need.

What to expect in this course:

Comprehensive Knowledge of LLMs:

Understanding LLMs: Learn about parameters, weights, inference, and neural networks.
Neural Networks: Understand how neural networks function with tokens in LLMs.
Transformer Architecture: Explore the Transformer architecture and Mixture of Experts.
Fine-Tuning: Understand the fine-tuning process and the development of the Assistant model.
Reinforcement Learning (RLHF): Dive into reinforcement learning with human feedback.

Advanced Techniques and Future Trends:

Scaling Laws: Learn about the scaling laws of LLMs, including GPU and data improvements.
Future of LLMs: Discover the capabilities and future developments in LLM technology.
Multimodal Processing: Understand multimodality and visual processing with LLMs, inspired by movies like “Her.”

Practical Skills and Applications:

Tool Utilization: Use tools with LLMs like calculators and Python libraries.
Systems Thinking: Dive into systems thinking and future perspectives for LLMs.
Self-Improvement: Learn self-improvement methods inspired by AlphaGo.
Optimization Techniques: Enhance LLM performance with prompts, RAG, function calling, and customization.

Prompt Engineering:

Advanced Prompts: Master techniques like Chain of Thought and Tree of Thoughts prompting.
Customization: Customize LLMs with system prompts and personalize with ChatGPT memory.
Long-Term Memory: Implement RAG and GPTs for long-term memory capabilities.

API and Integration Skills:

API Basics: Understand the basics of API usage, including OpenAI API, Google Gemini, and Claude APIs.
Microsoft and GitHub Copilot: Utilize Microsoft Copilot in 365 and GitHub Copilot for programming.
OpenAI API Mastery: Explore functionalities, pricing models, and app creation with the OpenAI API.

AI App Development:

Google Colab: Learn API calls to OpenAI with Google Colab.
AI Agents: Create AI agents for various tasks in LangChain frameworks like Langgraph, Langflow, Vectorshift, Autogen, CrewAI, Flowise, and more.
Security: Ensure security with methods to prevent jailbreaks and prompt injections.

Comparative Insights:

Comparing Top LLMs: Compare the best LLMs, including Google Gemini, Claude, and more.
Open-Source Models: Explore and utilize open-source models like Llama 3, Mixtral, and Command R+ with the possibility of running everything locally on your PC for maximum security.

Practical Applications:

Embedding and Vector Databases: Implement embeddings for RAG.
Zapier Integration: Integrate Zapier actions into GPTs.
Open-Source LLMs: Install and use LM Studio for local open-source LLMs for maximum security.
Model Fine-Tuning: Fine-tune open-source models with Huggingface.
API-Based App Development: Create apps with DALL-E, Whisper, GPT-4o, Vision, and more in Google Colab.

Innovative Tools and Agents:

Microsoft Autogen: Use Microsoft Autogen for developing AI agents.
CrewAI: Develop AI agents with CrewAI.
LangChain: Understand the framework with divisions like LangGraph, LangFlow, and more.
Flowise: Implement Flowise with function calls and open-source LLM as a chatbot.

Ethical and Security Considerations:

LLM Security: Understand and apply security measures to prevent hacking.
Future of LLMs: Explore the potential of LLMs as operating systems in robots and PCs.

This course is ideal for anyone looking to delve deeper into the world of LLMs—from developers and creatives to entrepreneurs and AI enthusiasts.

Harness the transformative power of LLM technology to develop innovative solutions and expand your understanding of their diverse applications.

By the end of ‘LLM Mastery: ChatGPT, Gemini, Claude, Llama3, OpenAI & APIs‘ you will have a comprehensive understanding of LLMs, their applications, and the skills to harness their power for various purposes. If you are ready to embark on a transformative journey into AI and position yourself at the forefront of this technological revolution, this course is for you.

Enroll today and start your journey to becoming an expert in the world of Large Language Models!

Course Curriculum

Chapter 1: Introduction and Overview

Lecture 1: Welcome

Lecture 2: Course Overview

Lecture 3: My Goal and Some Tips

Lecture 4: Explanation of Links and Downloads

Lecture 5: Important Links

Chapter 2: How LLMs Work: Parameters, Weights, Inference, Neural Networks & More

Lecture 1: What This Section Is About?

Lecture 2: An LLM Consists of Only Two Files Parameter File and a Few Lines of Code

Lecture 3: How Are the Parameters Created Pretraining (Initial Training of the LLM)

Lecture 4: What Is a Neural Network and how it works?

Lecture 5: How a Neural Network Works in an LLM with Tokens

Lecture 6: The Transformer Architecture Is Not Fully Understood (Yet?)

Lecture 7: Other Possibilities of the Transformer Architecture: Mixture of Experts Explaied

Lecture 8: After Pretraining Comes Finetuning: The Assistant Model Is Created

Lecture 9: The Final Step: Reinforcement Learning (RLHF)

Lecture 10: LLM Scaling Laws: To Improve LLM, We Only Need Two Things, GPU & Data

Lecture 11: Review: What Have You Learned So Far

Chapter 3: Additional Capabilities of LLMs & Future Developments

Lecture 1: What This Section Is About

Lecture 2: LLMs Can Use Various Tools, Like Calculators, Python Libraries, etc.

Lecture 3: Multimodality, Visual Processing (Vision), and Image Recognition

Lecture 4: Multimodality with Language Like in the Movie "Her"

Lecture 5: What Could Happen in the Future? Systems Thinking! [Thinking Fast and Slow]

Lecture 6: Self-Improvement Inspired by AlphaGo

Lecture 7: Further Ways to Improve LLMs: Prompts, RAG, Customization/System Prompts

Lecture 8: LLMs as the New Operating System: What the Future Could Look Like

Lecture 9: Review: What Have You Learned in This Section

Chapter 4: Prompt Engineering: Effective Use of LLMs in the Standard Interface

Lecture 1: What This Section Is About and the Interface of LLMs

Lecture 2: What is the Token Limit and why is it important

Lecture 3: Why Is Prompt Engineering Important? An Example!

Lecture 4: Prompt Engineering Basics: Semantic Association

Lecture 5: Prompt Engineering for LLMs: The Simplest Strategies (Structured Prompts)

Lecture 6: 3 Important "hacks" for Prompt Engineering and the Instruction Pormpting

Lecture 7: Role Prompting in ChatGPT and other LLMs

Lecture 8: Shot Prompting: Zero-Shot, One-Shot und Few-Shot

Lecture 9: Reverse Prompt Engineering and the "OK" Trick

Lecture 10: Chain of Thought Prompting: Step by Step to the Goal

Lecture 11: Tree of Thoughts (ToT) Prompting

Lecture 12: The Combination of Prompting Concepts

Lecture 13: Real-World Use Cases for Large Language Models

Lecture 14: Review and a bit of Homework

Chapter 5: LLM Customization: System Prompts, Memory, RAG & Creating Expert Models or GPTs

Lecture 1: What This Section Is About

Lecture 2: The Simplest Form of Personalization: ChatGPT Memory

Lecture 3: Customization Through System Prompts and Custom Instructions

Lecture 4: In-Context Learning: Short-Term Memory as Simple as Possible

Lecture 5: In-Context Learning: "The Short-Term Memory" but Efficient with SPR

Lecture 6: Embeddings and Vector Databases for RAG: A Detailed Explanation

Lecture 7: Long-Term Memory with RAG: As Simple as Possible with GPTs & RAG

Lecture 8: The GPT Store: Everything You Need to Know & Testing of GPTs for Code, PDFs & YT

Lecture 9: Three ways to make Money with GPTs

Lecture 10: First: You need a Builder Profile to generate Leads from GPTs

Lecture 11: Create a GPT with Knowledge that can generate Leads and makes Upsells

Lecture 12: What is a API?

Lecture 13: Zapier Actions in GPTs: Automate Gmail, Google Docs, & more with the Zapir API

Lecture 14: How to Integrate Every API in your GPT

Lecture 15: Summary: What You Have Learned in This Section

Chapter 6: Closed-Source LLMs: An Overview of Available Models and how to use them

Lecture 1: Open-Source vs. Closed-Source LLMs

Lecture 2: What is the difference: Parameters, Architecture, Pretraining size & more

Lecture 3: Google Gemini in the Standard Interface: Everything you need to know

Lecture 4: Google Labs with NotebookLM: The Best Method to Learn Books

Lecture 5: Claude by Anthropic: An Overview

Lecture 6: The Leading Companies Are OpenAI, Google & Anthropic: Many Are Building on Them

Lecture 7: Perplexity: Advantages and Disadvantages, and Applications

Lecture 8: Poe, The Versatile All-in-One Platform

Lecture 9: What is the Microsoft Copilot: How it works and is my Data Save?

Lecture 10: Using Microsoft Copilot in the Web Interface

Lecture 11: Microsoft Copilot PCs

Lecture 12: Microsoft 365: Differences Between Free and Paid Subscription

Lecture 13: The Right Copilot Subscription and a Free Alternative.

Lecture 14: Copilot in Microsoft Word: Write Faster Than Ever

Lecture 15: Copilot in Microsoft PowerPoint: The Quick Presentation

Lecture 16: Copilot in Microsoft Outlook: Write and Reply to Your Emails Faster

Lecture 17: Copilot in Microsoft Excel: Big Possibilities but Still a Bit Early

Lecture 18: Microsoft Copilot GPT: Create your own personalized ChatBots

Lecture 19: GitHub Copilot: The AI Solution for Programmers

Lecture 20: Conclusion on Microsoft Copilot

Lecture 21: Review of the Closed-Source LLMs

Chapter 7: APIs of Closed-Source LLMs

Lecture 1: What Is This About? APIs of Closed-Source LLMs

Lecture 2: Overview of the OpenAI API

Lecture 3: Pricing Models of the OpenAI API

Lecture 4: Important: OpenAI Playground overview and Billing Account

Lecture 5: The OpenAI Playgroundin action

Lecture 6: The Google Gemini API: Video Analysis and Other Features

Lecture 7: The Anthropic API for the Claude Models

Lecture 8: Summary of the Closed-Source APIs

Chapter 8: Open-Source LLMs: Available Models and Their Use in the Claude & Locally

Lecture 1: What Are Open-Source LLMs and Which Ones Are Available

Lecture 2: Huggingface: An Introduction

Lecture 3: HuggingChat: An Interface for Using Open-Source LLMs with Function Calling

Lecture 4: Groq: The Fastest Interface with an LPU Instead of a GPU

Lecture 5: Installation of LM Studio for Opensource LLMs: You need GPU, CPU, Cuda, Ram

Lecture 6: Using Open-Source Models in LM Studio: Llama3, Mistral; Phi-3 & more

Lecture 7: Censored vs. Uncensored LLMs (Llama3 Dolphin)

Lecture 8: Setting Up Your Own Local Server with LM Studio

Lecture 9: Finetuning an Open-Source Model with Huggingface or Google Colab

Instructors

Arnold Oberleiter
Dein Dozent

Rating Distribution

1 stars: 2 votes
2 stars: 2 votes
3 stars: 7 votes
4 stars: 30 votes
5 stars: 173 votes

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