Santhosh Sthanikam
Santhosh Sthanikam
  • Видео 10
  • Просмотров 465
Chat termination
In this video, we dive into an essential feature of the Autogen framework
Chat Termination. Managing when and how AI agents end their conversations is crucial for optimizing workflows, preventing unnecessary interactions, and improving efficiency.
🔹 What You'll Learn:
✅ Why chat termination is important in agentic AI
✅ How to implement chat termination using Autogen
✅ Practical examples and use cases
This video builds on the previous episode, where we introduced **Agentic AI, the Agentic AI framework, and Autogen**. If you haven’t watched that yet, be sure to check it out!
💡 Stay tuned for more in-depth tutorials on leveraging Autogen for AI automation.
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Просмотров: 7

Видео

Getting Started with Agentic AI: Autogen Framework Explained with Examples10:45Getting Started with Agentic AI: Autogen Framework Explained with Examples
Getting Started with Agentic AI: Autogen Framework Explained with Examples
Просмотров 153День назад
In this video, I introduce Agentic AI and the concept of Agentic Frameworks, along with an in-depth explanation of the Autogen Framework. You’ll learn: What Agentic AI is and why it’s important. The role of Agentic Frameworks in automating tasks with AI. A hands-on demonstration of the Autogen Framework with sample code. Whether you're a beginner or an advanced AI enthusiast, this video will gi...
Logistic regression using Python and Sklearn6:55Logistic regression using Python and Sklearn
Logistic regression using Python and Sklearn
Просмотров 393 года назад
This video talks about what is mean by logistic regression and how we can implement logistic regression using Python and Sklearn
Cross validation using Python and Sklearn10:04Cross validation using Python and Sklearn
Cross validation using Python and Sklearn
Просмотров 303 года назад
This video talks about how we can implement Single fold cross-validation, KFold cross-validation, Shuffled KFold cross-validation, Stratified KFold cross-validation, and cross-validation without KFold using Python Sklearn library.
Logistic regression using Python and Sklearn9:01Logistic regression using Python and Sklearn
Logistic regression using Python and Sklearn
Просмотров 103 года назад
This video talks about how we can do EDA, Feature engineering, and Feature selection using RFE for logistic regression, and also this video discusses how we can implement logistic regression with the selected features. The reference data set is the titanic dataset.
Probability density function (PDF) Vs Probability mass function (PMF)5:57Probability density function (PDF) Vs Probability mass function (PMF)
Probability density function (PDF) Vs Probability mass function (PMF)
Просмотров 243 года назад
This video talks about What is mean by Probability density function and Probability mass function, and how we can implement this thing using Python
Variance and Standard deviation using Python5:29Variance and Standard deviation using Python
Variance and Standard deviation using Python
Просмотров 793 года назад
This video talks about what is mean by Mean, Variance, and Standard deviation and how can implement these things using Python
Mean Mode Median using Python2:05Mean Mode Median using Python
Mean Mode Median using Python
Просмотров 243 года назад
In this video, we discussed Mean, Mode and Median, and how we can implement this using Python.
Reinforcement learning 1: Introduction to Reinforcement learning5:19Reinforcement learning 1: Introduction to Reinforcement learning
Reinforcement learning 1: Introduction to Reinforcement learning
Просмотров 203 года назад
This video talks about What is mean by Reinforcement learning. What is mean by Supervised learning and unsupervised learning, and what it means by agent, action, reward, state/observation.
Context aware cosine similarity1:04Context aware cosine similarity
Context aware cosine similarity
Просмотров 863 года назад
This video talks about how to implement context-aware cosine similarity implementation using a Universal sentence encoder model in less than 10 lines of code. This code is implemented using scikit learn(sklearn) and TensorFlow hub libraries in python.

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