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Build a first LangChain application with LLMs, prompt templates, few-shot prompting, and an LLM chain in Colab.
Combine Llama3, CrewAI, and Groq to create a fast AI agent for email responses. Learn setup, tools, and implementation in this hands-on tutorial.
Discover how OmniParser enables AI agents to interpret and interact with user interfaces through advanced screen reading and LLM integration capabilities.
Explore Kimi K2.5's powerful agent swarm capabilities and see how Moonshot AI's latest model excels at complex task automation through collaborative AI agents.
Explore Mistral's new Agents API, its agentic capabilities, and how to implement it through examples, cookbook resources, and documentation for building advanced LLM agents.
Explore Gemini TTS capabilities for speech generation, including single speaker and multi-speaker podcast demos, with practical examples in Google AI Studio and Colab.
Dive into DeepSeek R1's technical architecture, benchmarks, and implementation, with hands-on demonstrations using Colab and practical insights for running distilled versions.
Learn parameter-efficient fine-tuning of decoder-style GPT models with PEFT and LoRA, including a Hugging Face code walkthrough.
Explore Google's PaLM2 AI model: improvements, specialized versions, and practical applications in the Vertex AI platform. Hands-on demonstrations of various prompting techniques and code examples included.
Explore ReACT framework implementation using LangChain, covering its principles, manual application, and integration for enhanced AI reasoning and task-solving capabilities.
Explore Phi 1.5, a small yet powerful language model. Learn about its development, capabilities, and potential applications through in-depth analysis and hands-on coding examples.
Explore the advancements in LLaMA2, including new base models, performance comparisons, and insights into its development and deployment on various platforms.
Explore PaLM-2's integration with LangChain, covering chatbots, information retrieval, and database creation. Learn to leverage these powerful tools for advanced language processing and AI applications.
Explore LangChain Expression Language's features: bindings, OpenAI functions, output parsers, retrievers, tools, and arbitrary functions for enhanced language model interactions.
Learn to build a Recursive Criticism and Improvement (RCI) chain for AI agents using LangChain Expression Language. Explore multi-chain implementation and combined chain approaches for enhanced agent performance.
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