Master the fundamentals of AI, from basics to autonomous agents
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Turn your data into decisions
Training models on labeled data to make predictions. The model learns from examples where the correct answer is provided.
Learning through trial and error, where an agent learns to make decisions by receiving rewards or penalties.
Finding patterns in unlabeled data without predefined categories or outcomes.
AI systems that can analyze information, draw conclusions, and solve complex problems.
Enabling computers to understand, interpret, and generate human language.
Complex neural networks for advanced tasks
Massive neural networks trained on vast amounts of text to understand and generate human-like text.
Architecture using attention mechanisms to process sequential data efficiently, foundation of modern LLMs.
Specialized for processing grid-like data such as images, using convolutional layers to detect features.
Networks designed for sequential data, with LSTMs handling long-term dependencies.
Probabilistic generative models composed of multiple layers of stochastic latent variables.
AI models that can create new code or images based on learned patterns.
Tools and platforms (TensorFlow, PyTorch) for building and deploying deep learning models.
Generate content and code at scale
Crafting effective inputs to get desired outputs from AI models.
Enhancing LLM outputs by retrieving relevant information from external knowledge bases.
Maintaining conversation context and historical information across interactions.
Techniques to reduce AI generating false or nonsensical information.
Text-to-Speech and Automatic Speech Recognition for voice interactions.
Storing and retrieving information across different time scales.
Creating content across multiple formats simultaneously.
AI's ability to evaluate its own outputs and correct mistakes.
Execute complex tasks autonomously
Agents accessing external tools and APIs to perform actions beyond text generation.
Breaking down complex objectives into smaller, manageable sub-tasks.
Coordinating multiple tools and services to accomplish tasks.
Incorporating human judgment and approval at critical decision points.
Transferring tasks between agents or to humans when appropriate.
Setting boundaries and safety measures for agent behavior.
Platforms for discovering, sharing, and deploying AI agents.
Agents operating independently without constant human supervision.
Adapting and selecting tools based on task requirements.
Automate entire processes with agents
Standardized communication methods between multiple agents.
Multiple AI agents working together to solve complex problems.
Managing interactions and information sharing between agents.
Maintaining agent state across sessions and interactions.
Reasoning and Acting, Chain of Thought, Tree of Thoughts planning strategies.
Organizing and ordering tasks based on importance and dependencies.
Continuous assessment and improvement of agent performance.
Ability to undo actions and return to previous states when errors occur.
Agents that learn and improve from their experiences over time.
Maintaining original goals and objectives throughout task execution.
Operating independently over extended periods with minimal intervention.
Optimizing computational resources and operational costs.
Policies and mechanisms ensuring safe and ethical agent behavior.
Managing what information is stored, for how long, and how it's used.
Monitoring and understanding agent actions and decision-making processes.
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