Скачать или смотреть Understanding AI and LLMs (Part 2): Exploring Models

Ready to dive deeper into how AI models actually work? This introduction launches Part 2 of our AI mastery series, covering three key concepts: where your data goes when you use AI, what model parameters mean for performance, and how reasoning models "think" before responding.

What you'll explore in the next three videos:

Open-Source vs Closed AI (Privacy & Data Protection)
Learn the critical differences between open-source models running on local servers versus commercial APIs, data retention policies, and how to choose models based on data sensitivity.

Model Size & Parameters (Capabilities & Edge AI)
Understand how parameters determine AI capabilities, why bigger models recognize more patterns, and how compression techniques are bringing AI to smartphones and edge devices.

Reasoning Models (Chain-of-Thought & Complex Problem Solving)
Discover the new generation of AI that thinks before responding, using chain-of-thought prompting and self-reflection to solve logic puzzles and multi-step problems.

Where this fits:
After mastering fundamentals in Keys 1-5 (tokens, context windows, temperature, prompting, and agents), these next three keys reveal how model architecture and deployment affect privacy, performance, and reasoning capabilities.

Who this is for: Anyone moving beyond basic AI usage to understand model selection, privacy implications, and advanced capabilities for professional or research applications.

#AIModels #MachineLearning #LLMExplained #AIEducation #DeepLearning