Master AI, Large Language Models & Agents Training

• Cold Open Jumping Right into LLM Engineering

• Setting Up Ollama for Local LLM Deployment on Windows and Mac

• Unleashing the Power of Local LLMs Build Spanish Tutor Using Ollama

• LLM Engineering Roadmap From Beginner to Master in 8 Weeks

• Building LLM Applications Chatbots, RAG, and Agentic AI Projects

• From Wall Street to AI Ed Donner's Path to Becoming an LLM Engineer

• Setting Up Your LLM Development Environment Tools and Best Practices

• Mac Setup Guide Jupyter Lab and Conda for LLM Projects

• Setting Up Anaconda for LLM Engineering Windows Installation Guide

• Alternative Python Setup for LLM Projects Virtualenv vs. Anaconda Guide

• Setting Up OpenAI API for LLM Development Keys, Pricing & Best Practices

• Creating a. env File for Storing API Keys Safely

• nstant Gratification Project Creating an AI-Powered Web Page Summarizer

• Implementing Text Summarization Using OpenAI's GPT-4 and Beautiful Soup

• Wrapping Up Key Takeaways and Next Steps in LLM Engineering

• Mastering LLM Engineering Key Skills and Tools for AI Development

• Understanding Frontier Models GPT, Claude, and Open Source LLMs

• How to Use Ollama for Local LLM Inference Python Tutorial with Jupyter

• Hands-On LLM Task Comparing OpenAI and Ollama for Text Summarization

• Frontier AI Models Comparing GPT-4, Claude, Gemini, and LLAMA

• Comparing Leading LLMs Strengths and Business Applications

• Exploring GPT-4o vs O1 Preview Key Differences in Performance

• Creativity and Coding Leveraging GPT-4o’s Canvas Feature

• Claude 3.5’s Alignment and Artifact Creation A Deep Dive

• AI Model Comparison Gemini vs Cohere for Whimsical and Analytical Tasks

• Evaluating Meta AI and Perplexity Nuances of Model Outputs

• LLM Leadership Challenge Evaluating AI Models Through Creative Prompts

• Revealing the Leadership Winner A Fun LLM Challenge

• Exploring the Journey of AI From Early Models to Transformers

• Understanding LLM Parameters From GPT-1 to Trillion-Weight Models

• GPT Tokenization Explained How Large Language Models Process Text Input

• How Context Windows Impact AI Language Models Token Limits Explained

• Navigating AI Model Costs API Pricing vs. Chat Interface Subscriptions

• Comparing LLM Context Windows GPT-4 vs Claude vs Gemini 1.5 Flash

• Wrapping Up Key Takeaways and Practical Insights

• Building AI-Powered Marketing Brochures with OpenAI API and Python

• JupyterLab Tutorial Web Scraping for AI-Powered Company Brochures

• Structured Outputs in LLMs Optimizing JSON Responses for AI Projects

• Creating and Formatting Responses for Brochure Content

• Final Adjustments Optimizing Markdown and Streaming in JupyterLab

• Mastering Multi-Shot Prompting Enhancing LLM Reliability in AI Projects

• Mastering Multiple AI APStreaming AI Responses Implementing Real-Time LLM Output in Python

• How to Create Adversarial AI Conversations Using OpenAI and Claude APIs

• AI Tools Exploring Transformers & Frontier LLMs for Developers

• Building AI UIs with Gradio Quick Prototyping for LLM Engineers

• Gradio Tutorial Create Interactive AI Interfaces for OpenAI GPT Models

• Implementing Streaming Responses with GPT and Claude in Gradio UI

• Building a Multi-Model AI Chat Interface with Gradio GPT vs Claude

• Building Advanced AI UIs From OpenAI API to Chat Interfaces with Gradio

• Building AI Chatbots Mastering Gradio for Customer Support Assistants

• Build a Conversational AI Chatbot with OpenAI & Gradio Step-by-Step

• Enhancing Chatbots with Multi-Shot Prompting and Context Enrichment

• Mastering AI Tools Empowering LLMs to Run Code on Your Machine

• Using AI Tools with LLMs Enhancing Large Language Model Capabilities

• Building an AI Airline Assistant Implementing Tools with OpenAI GPT-4

• How to Equip LLMs with Custom Tools OpenAI Function Calling Tutorial

• Mastering AI Tools Building Advanced LLM-Powered Assistants with APIs

• Multimodal AI Assistants Integrating Image and Sound Generation

• Multimodal AI Integrating DALL-E 3 Image Generation in JupyterLab

• Build a Multimodal AI Agent Integrating Audio & Image Tools

• How to Build a Multimodal AI Assistant Integrating Tools and Agents

• Hugging Face Tutorial: Exploring Open-Source AI Models and Datasets

• Exploring HuggingFace Hub Models, Datasets & Spaces for AI Developers

• Intro to Google Colab Cloud Jupyter Notebooks for Machine Learning

• Hugging Face Integration with Google Colab Secrets and API Keys Setup

• Mastering Google Colab Run Open-Source AI Models with Hugging Face

• Hugging Face Transformers Using Pipelines for AI Tasks in Python

• Hugging Face Pipelines Simplifying AI Tasks with Transformers Library

• Mastering HuggingFace Pipelines Efficient AI Inference for ML Tasks

• Exploring Tokenizers in Open-Source AI Llama, Phi-2, Qwen, & Starcoder

• Tokenization Techniques in AI Using AutoTokenizer with LLAMA 3.1 Model

• Comparing Tokenizers Llama, PHI-3, and QWEN2 for Open-Source AI Models

• Hugging Face Tokenizers Preparing for Advanced AI Text Generation

• Hugging Face Model Class Running Inference on Open-Source AI Models

• Hugging Face Transformers Loading & Quantizing LLMs with Bits & Bytes

• Hugging Face Transformers Generating Jokes with Open-Source AI Models

• Mastering Hugging Face Transformers Models, Pipelines, and Tokenizers

• Combining Frontier & Open-Source Models for Audio-to-Text Summarization

• Using Hugging Face & OpenAI for AI-Powered Meeting Minutes Generation

• Build a Synthetic Test Data Generator Open-Source AI Model for Business

• How to Choose the Right LLM: Comparing Open and Closed Source Models

• Chinchilla Scaling Law: Optimizing LLM Parameters and Training Data Size

• Limitations of LLM Benchmarks: Overfitting and Training Data Leakage

• Evaluating Large Language Models: 6 Next-Level Benchmarks Unveiled

• HuggingFace OpenLLM Leaderboard: Comparing Open-Source Language Models

• Master LLM Leaderboards: Comparing Open Source and Closed Source Models

• Comparing LLMs: Top 6 Leaderboards for Evaluating Language Models

• Specialized LLM Leaderboards: Finding the Best Model for Your Use Case

• LLAMA vs GPT-4: Benchmarking Large Language Models for Code Generation

• Human-Rated Language Models: Understanding the LM Sys Chatbot Arena

• Commercial Applications of Large Language Models: From Law to Education

• Comparing Frontier and Open-Source LLMs for Code Conversion Projects

• Leveraging Frontier Models for High-Performance Code Generation in C++

• Comparing Top LLMs for Code Generation: GPT-4 vs Claude 3.5 Sonnet

• Optimizing Python Code with Large Language Models: GPT-4 vs Claude 3.5

• Code Generation Pitfalls: When Large Language Models Produce Errors

• Blazing Fast Code Generation: How Claude Outperforms Python by 13,000x

• Building a Gradio UI for Code Generation with Large Language Models

• Optimizing C++ Code Generation: Comparing GPT and Claude Performance

• Comparing GPT-4 and Claude for Code Generation: Performance Benchmarks

• Open Source LLMs for Code Generation: Hugging Face Endpoints Explored

• How to Use HuggingFace Inference Endpoints for Code Generation Models

• Integrating Open-Source Models with Frontier LLMs for Code Generation

• Comparing Code Generation: GPT-4, Claude, and CodeQuen LLMs

• Mastering Code Generation with LLMs: Techniques and Model Selection

• Evaluating LLM Performance: Model-Centric vs Business-Centric Metrics

• Mastering LLM Code Generation: Advanced Challenges for Python Developers

• RAG Fundamentals: Leveraging External Data to Improve LLM Responses

• Building a DIY RAG System: Implementing Retrieval-Augmented Generation

• Understanding Vector Embeddings: The Key to RAG and LLM Retrieval

• Unveiling LangChain: Simplify RAG Implementation for LLM Applications

• LangChain Text Splitter Tutorial: Optimizing Chunks for RAG Systems

• Preparing for Vector Databases: OpenAI Embeddings and Chroma in RAG

• Mastering Vector Embeddings: OpenAI and Chroma for LLM Engineering

• Visualizing Embeddings: Exploring Multi-Dimensional Space with t-SNE

• Building RAG Pipelines: From Vectors to Embeddings with LangChain

• Implementing RAG Pipeline: LLM, Retriever, and Memory in LangChain

• Mastering Retrieval-Augmented Generation: Hands-On LLM Integration

• Master RAG Pipeline: Building Efficient RAG Systems

• Optimizing RAG Systems: Troubleshooting and Fixing Common Problems

• Switching Vector Stores: FAISS vs Chroma in LangChain RAG Pipelines

• Demystifying LangChain: Behind-the-Scenes of RAG Pipeline Construction

• Debugging RAG: Optimizing Context Retrieval in LangChain

• Build Your Personal AI Knowledge Worker: RAG for Productivity Boost

• Fine-Tuning Large Language Models: From Inference to Training

• Finding and Crafting Datasets for LLM Fine-Tuning: Sources & Techniques

• Data Curation Techniques for Fine-Tuning LLMs on Product Descriptions

• Optimizing Training Data: Scrubbing Techniques for LLM Fine-Tuning

• Evaluating LLM Performance: Model-Centric vs Business-Centric Metrics

• LLM Deployment Pipeline: From Business Problem to Production Solution

• Prompting, RAG, and Fine-Tuning: When to Use Each Approach

• Productionizing LLMs: Best Practices for Deploying AI Models at Scale

• Optimizing Large Datasets for Model Training: Data Curation Strategies

• How to Create a Balanced Dataset for LLM Training: Curation Techniques

• Finalizing Dataset Curation: Analyzing Price-Description Correlations

• How to Create and Upload a High-Quality Dataset on HuggingFace

• Feature Engineering and Bag of Words: Building ML Baselines for NLP

• Baseline Models in ML: Implementing Simple Prediction Functions

• Feature Engineering Techniques for Amazon Product Price Prediction Models

• Optimizing LLM Performance: Advanced Feature Engineering Strategies

• Linear Regression for LLM Fine-Tuning: Baseline Model Comparison

• Bag of Words NLP: Implementing Count Vectorizer for Text Analysis in ML

• Support Vector Regression vs Random Forest: Machine Learning Face-Off

• Comparing Traditional ML Models: From Random to Random Forest

• Evaluating Frontier Models: Comparing Performance to Baseline Frameworks

• Human vs AI: Evaluating Price Prediction Performance in Frontier Models

• GPT-4o Mini: Frontier AI Model Evaluation for Price Estimation Tasks

• Comparing GPT-4 and Claude: Model Performance in Price Prediction Tasks

• Frontier AI Capabilities: LLMs Outperforming Traditional ML Models

• Fine-Tuning LLMs with OpenAI: Preparing Data, Training, and Evaluation

• How to Prepare JSONL Files for Fine-Tuning Large Language Models (LLMs)

• Step-by-Step Guide: Launching GPT Fine-Tuning Jobs with OpenAI API

• Fine-Tuning LLMs: Track Training Loss & Progress with Weights & Biases

• Evaluating Fine-Tuned LLMs Metrics: Analyzing Training & Validation Loss

• LLM Fine-Tuning Challenges: When Model Performance Doesn't Improve

• Fine-Tuning Frontier LLMs: Challenges & Best Practices for Optimization

• Mastering Parameter-Efficient Fine-Tuning: LoRa, QLoRA & Hyperparameters

• Introduction to LoRA Adaptors: Low-Rank Adaptation Explained

• QLoRA: Quantization for Efficient Fine-Tuning of Large Language Models

• Optimizing LLMs: R, Alpha, and Target Modules in QLoRA Fine-Tuning

• Parameter-Efficient Fine-Tuning: PEFT for LLMs with Hugging Face

• How to Quantize LLMs: Reducing Model Size with 8-bit Precision

• Double Quantization & NF4: Advanced Techniques for 4-Bit LLM Optimization

• Exploring PEFT Models: The Role of LoRA Adapters in LLM Fine-Tuning

• Model Size Summary: Comparing Quantized and Fine-Tuned Models

• How to Choose the Best Base Model for Fine-Tuning Large Language Models

• Selecting the Best Base Model: Analyzing HuggingFace's LLM Leaderboard

• Exploring Tokenizers: Comparing LLAMA, QWEN, and Other LLM Models

• Optimizing LLM Performance: Loading and Tokenizing Llama 3.1 Base Model

• Quantization Impact on LLMs: Analyzing Performance Metrics and Errors

• Comparing LLMs: GPT-4 vs LLAMA 3.1 in Parameter-Efficient Tuning

• QLoRA Hyperparameters: Mastering Fine-Tuning for Large Language Models

• Understanding Epochs and Batch Sizes in Model Training

• Learning Rate, Gradient Accumulation, and Optimizers Explained

• Setting Up the Training Process for Fine-Tuning

• Configuring SFTTrainer for 4-Bit Quantized LoRA Fine-Tuning of LLMs

• Fine-Tuning LLMs: Launching the Training Process with QLoRA

• Monitoring and Managing Training with Weights & Biases

• Keeping Training Costs Low: Efficient Fine-Tuning Strategies

• Efficient Fine-Tuning: Using Smaller Datasets for QLoRA Training

• Visualizing LLM Fine-Tuning Progress with Weights and Biases Charts

• Advanced Weights & Biases Tools and Model Saving on Hugging Face

• End-to-End LLM Fine-Tuning: From Problem Definition to Trained Model

• The Four Steps in LLM Training: From Forward Pass to Optimization

• QLoRA Training Process: Forward Pass, Backward Pass and Loss Calculation

• Understanding Softmax and Cross-Entropy Loss in Model Training

• Monitoring Fine-Tuning: Weights & Biases for LLM Training Analysis

• Revisiting the Podium: Comparing Model Performance Metrics

• Evaluation of our Proprietary, Fine-Tuned LLM against Business Metrics

• Visualization of Results: Did We Beat GPT-4?

• Hyperparameter Tuning for LLMs: Improving Model Accuracy with PEFT

• From Fine-Tuning to Multi-Agent Systems: Next-Level LLM Engineering

• Building a Multi-Agent AI Architecture for Automated Deal Finding Systems

• Unveiling Modal: Deploying Serverless Models to the Cloud

• LLAMA on the Cloud: Running Large Models Efficiently

• Building a Serverless AI Pricing API: Step-by-Step Guide with Modal

• Multiple Production Models Ahead: Preparing for Advanced RAG Solutions

• Implementing Agentic Workflows: Frontier Models and Vector Stores in RAG

• Building a Massive Chroma Vector Datastore for Advanced RAG Pipelines

• Visualizing Vector Spaces: Advanced RAG Techniques for Data Exploration

• 3D Visualization Techniques for RAG: Exploring Vector Embeddings

• Finding Similar Products: Building a RAG Pipeline without LangChain

• RAG Pipeline Implementation: Enhancing LLMs with Retrieval Techniques

• Random Forest Regression: Using Transformers & ML for Price Prediction

• Building an Ensemble Model: Combining LLM, RAG, and Random Forest

• Wrap-Up: Finalizing Multi-Agent Systems and RAG Integration

• Enhancing AI Agents with Structured Outputs: Pydantic & BaseModel Guide

• Scraping RSS Feeds: Building an AI-Powered Deal Selection System

• Structured Outputs in AI: Implementing GPT-4 for Detailed Deal Selection

• Optimizing AI Workflows: Refining Prompts for Accurate Price Recognition

• Mastering Autonomous Agents: Designing Multi-Agent AI Workflows

• The 5 Hallmarks of Agentic AI: Autonomy, Planning, and Memory

• Building an Agentic AI System: Integrating Pushover for Notifications

• Implementing Agentic AI: Creating a Planning Agent for Automated Workflows

• Building an Agent Framework: Connecting LLMs and Python Code

• Completing Agentic Workflows: Scaling for Business Applications

• Autonomous AI Agents: Building Intelligent Systems Without Human Input

• AI Agents with Gradio: Advanced UI Techniques for Autonomous Systems

• Finalizing the Gradio UI for Our Agentic AI Solution

• Enhancing AI Agent UI: Gradio Integration for Real-Time Log Visualization

• Analyzing Results: Monitoring Agent Framework Performance

• AI Project Retrospective: 8-Week Journey to Becoming an LLM Engineer