Python Status: Pending Migration

📖 Large Language Models (LLMs)¶

  • 🧠 What are LLMs?
    • 🔍 Definition and Intuition
    • 🏗️ LLM vs. Standard Transformer
    • 📐 Typical Sizes (Parameters, Layers, Data)
  • 🏋️ Pretraining LLMs
    • 📖 Causal Language Modeling (CLM)
    • 🧩 Masked Language Modeling (MLM)
    • 🔁 Next Sentence Prediction and Others
    • 🧠 Self-supervised Learning Explained
  • 🔁 Fine-tuning Strategies
    • 🎯 Task-specific Fine-tuning
    • 🧪 Instruction Tuning
    • 💬 RLHF (Reinforcement Learning with Human Feedback)
    • 🧰 Parameter-Efficient Tuning (LoRA, Adapters)
  • 📊 Evaluation of LLMs
    • ✅ Standard Benchmarks (GLUE, HELM, MMLU)
    • 🧠 Measuring Hallucination, Bias, Toxicity
    • ⚖️ Tradeoffs: Size vs. Accuracy vs. Inference Cost
  • 🧰 LLM Capabilities and Limitations
    • 🧠 Emergent Behaviors at Scale
    • 🛠️ In-Context Learning
    • 🧨 Hallucinations and Failures
    • 📉 Prompt Sensitivity
  • 🚀 Use Cases and Applications
    • 💬 Chatbots and Assistants
    • 📄 Text Summarization
    • 🔎 Search + RAG (Retrieval-Augmented Generation)
    • 🧠 Reasoning, Coding, Math, and Beyond
  • 🔐 Risks and Ethical Concerns
    • 🔁 Model Misuse and Jailbreaks
    • 🔬 Biases and Stereotyping
    • 🔒 Privacy, Copyright, and Data Leakage
    • ⚖️ Responsible Deployment
  • 🔧 Building with LLMs Today
    • 🤖 Open vs. Closed Models
    • 🧰 APIs (OpenAI, Anthropic, Cohere)
    • ⚙️ Open-source (LLaMA, Mistral, Falcon)
    • 🧠 Model Selection Tips
  • 🔚 Closing Notes
    • 🧭 Summary and Key Takeaways
    • 🚀 Next Up: Prompt Engineering (05)
    • 🧠 What to Practice

🧠 What are LLMs?¶

🔍 Definition and Intuition¶

🏗️ LLM vs. Standard Transformer¶

📐 Typical Sizes (Parameters, Layers, Data)¶

Back to the top

🏋️ Pretraining LLMs¶

📖 Causal Language Modeling (CLM)¶

🧩 Masked Language Modeling (MLM)¶

🔁 Next Sentence Prediction and Others¶

🧠 Self-supervised Learning Explained¶

Back to the top

🔁 Fine-tuning Strategies¶

🎯 Task-specific Fine-tuning¶

🧪 Instruction Tuning¶

💬 RLHF (Reinforcement Learning with Human Feedback)¶

🧰 Parameter-Efficient Tuning (LoRA, Adapters)¶

Back to the top

📊 Evaluation of LLMs¶

✅ Standard Benchmarks (GLUE, HELM, MMLU)¶

🧠 Measuring Hallucination, Bias, Toxicity¶

⚖️ Tradeoffs: Size vs. Accuracy vs. Inference Cost¶

Back to the top

🧰 LLM Capabilities and Limitations¶

🧠 Emergent Behaviors at Scale¶

🛠️ In-Context Learning¶

🧨 Hallucinations and Failures¶

📉 Prompt Sensitivity¶

Back to the top

🚀 Use Cases and Applications¶

💬 Chatbots and Assistants¶

📄 Text Summarization¶

🔎 Search + RAG (Retrieval-Augmented Generation)¶

🧠 Reasoning, Coding, Math, and Beyond¶

Back to the top

🔐 Risks and Ethical Concerns¶

🔁 Model Misuse and Jailbreaks¶

🔬 Biases and Stereotyping¶

🔒 Privacy, Copyright, and Data Leakage¶

⚖️ Responsible Deployment¶

Back to the top

🔧 Building with LLMs Today¶

🤖 Open vs. Closed Models¶

🧰 APIs (OpenAI, Anthropic, Cohere)¶

⚙️ Open-source (LLaMA, Mistral, Falcon)¶

🧠 Model Selection Tips¶

Back to the top

🔚 Closing Notes¶

🧭 Summary and Key Takeaways¶

🚀 Next Up: Prompt Engineering¶

🧠 What to Practice¶

Back to the top