Artificial Intelligence
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+36330 kunlar
Postlar arxiv
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📱Artificial Intelligence and Machine Learning
📱Machine Learning and AI Foundations: Clustering and Association
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🔅 Machine Learning and AI Foundations: Clustering and Association
📝 Learn how to use cluster analysis, association rules, and anomaly detection algorithms for unsupervised learning.
🌐 Author: Keith McCormick
🔰 Level: Intermediate
⏰ Duration: 3h 33m
📋 Topics: Machine Learning, Artificial Intelligence
🔗 Join Artificial Intelligence and Machine Learning for more courses
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📱Artificial Intelligence and Machine Learning
📱Choosing the Right ML Approach for Your Business Case
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🔅 Choosing the Right ML Approach for Your Business Case
📝 Learn the system components of machine learning (ML), their function in the AI ecosystem, and how to choose the best approach for your business pipeline.
🌐 Author: Lyron Andrews
🔰 Level: Intermediate
⏰ Duration: 1h 42m
📋 Topics: Machine Learning, Artificial Intelligence
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088k| 🔰 Machine Learning
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067k| 🔰 Data Analysis and Databases
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031k| 🔰 Crypto Tutorials
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🔗 Machine Learning, Simplified
Ever wondered what Machine Learning really means and how it impacts your everyday life?
ML is not just about fancy algorithms—it's about how machines learn like humans to make decisions, automate tasks, and personalize your digital experiences. 🔍✨
Here are real-world use cases you interact with daily:
🔹 Generative AI → ChatGPT, Midjourney
🔹 Speech Recognition → Siri, Alexa
🔹 Computer Vision → Face ID, Self-driving cars
🔹 RPA & Stock Trading Bots → Automating workflows & finance!
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🔅 GraphRAG Essential Training
📝 Learn how to build robust AI applications by creating knowledge graphs for retrieval-augmented generation (RAG) in Python using LangChain and Neo4j.
🌐 Author: Dr. Clair Sullivan
🔰 Level: Intermediate
⏰ Duration: 1h 39m
📋 Topics: Retrieval-Augmented Generation, Knowledge Graph Augmentation, Knowledge Graphs
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🔰 Why Python is a Must-Have Skill?
If you're diving into programming or data science, mastering Python is essential! Its versatility and simplicity make it the go-to language across industries.◆ Powerful and Versatile From web development to data analysis, Python’s broad libraries and frameworks adapt to almost any project. ◆ Data-Driven Python, combined with libraries like Pandas and NumPy, allows you to analyze and manipulate datasets efficiently. ◆ Automate the Boring Stuff Automate repetitive tasks, streamline workflows, and boost productivity with Python’s easy-to-use scripts. ◆ AI and Machine Learning With frameworks like TensorFlow and Scikit-learn, Python is at the forefront of AI, enabling you to build predictive models and explore deep learning. ◆ Readable and Beginner-Friendly Python’s simple syntax makes it easy to learn, even for beginners, without sacrificing power and functionality. ◆ Community Support Backed by a massive global community, Python is constantly evolving, with new libraries and resources available at your fingertips.
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+1
⚡️ Agentic Reward Modeling is a fresh project from THU-KEG, the goal of which is to rethink the approach to training agent systems.
This tool aims to develop reward methods where the agent does not simply follow commands, but learns to understand its actions in the context of more complex tasks and long-term goals.
Key Features:
- Instead of standard RL methods, where rewards often depend on pre-set criteria, the emphasis here is on developing more complex strategies that adapt to changing environments and goals.
- The tool helps model rewards in such a way that the agent can independently adjust its actions, learn from mistakes and, ultimately, demonstrate more “human” decision making.
- Developers can use this approach in multi-agent systems and complex tasks where dynamic assessment of the effectiveness of actions is important.
This tool is interesting not only for its theoretical potential, but also for its practical applications in the field of creating more autonomous and intelligent systems. Agentic Reward Modeling opens up new possibilities for studying agents that can learn in real time, which makes it promising for further research and integration into real applications.
▪️Paper: https://arxiv.org/abs/2502.19328
▪️Code: https://github.com/THU-KEG/Agentic-Reward-Modeling
