Google just released a free 2-hour course on full Graph engineering: 1 prompt → 100 agents → loops → graphs from 0% to 100%: 🤖⚙️

10% → 17:44 - build your first agent 🚀
30% → 39:30 - Loop engineering: iterate, check, break 🔁
60% → 1:12:38 - Graph engineering 🕸️
75% → 1:34:26 - agents that throttle themselves ⚡
100% → 1:55:05 - full graph for multi-agentic systems 🏗️

everyone builds one agent and calls it done - this is the full system where agents wire themselves into a graph.

watch the course, build the graph - then read the full architecture below ↓

More: https://telegra.ph/Graph-Engineering-build-1000-agent-loops-in-one-window-from-one-prompt-full-5-step-course-08-02

#GraphEngineering #AI #Agents #Graphs #Tech #Coding

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Attention Heatmap vs Token Pruning 🔍✂️

🔗 More: https://www.overshoot.ai/blogs/an-introduction-to-token-pruning-for-vlms

#AI #MachineLearning #TokenPruning #DeepLearning #TechNews #VLM

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🔖 5 Free Courses on AI Agents

1. https://huggingface.co/learn/agents-course — AI Agents Course 🤗

2. https://deeplearning.ai/courses/ai-agents-in-langgraph — AI Agents in LangGraph 🧠

3. https://deeplearning.ai/short-courses/multi-ai-agent-systems-with-crewai/ — Multi AI Agent Systems with CrewAI 🤖

4. https://microsoft.github.io/AI-For-Beginners/agentic-ai/ — AI Agents for Beginners 🚀

5. https://deeplearning.ai/courses/building-code-agents-with-hugging-face-smolagents — Building Code Agents with Hugging Face smolagents 💻

If you want to learn about Agentic AI, save this collection. 💾

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"Introduction to Machine Learning" is another free textbook on machine learning, approximately 600 pages long, which emphasizes a deep mathematical understanding of the subject. 📚🧮

The book begins with the mathematical foundations necessary for further study: linear algebra, mathematical analysis, probability theory, matrix analysis, and optimization methods. It then covers the main supervised learning algorithms: linear and logistic regression, the k-nearest neighbors method, decision trees, random forests, boosting, and neural networks. 🤖📈

A significant portion of the book is dedicated to probabilistic and generative models. It discusses Monte Carlo methods, graphical models, Bayesian networks, variational methods, normalizing flows, variational autoencoders (VAEs), and generative adversarial networks (GANs). 🎲🧠

The final chapters discuss clustering, principal component analysis (PCA), learning on manifolds, and theoretical estimates of a model's ability to generalize. 🔍📊

In my opinion, this is an excellent resource for those who want to gain a broad understanding of machine learning and understand the mathematics underlying the key methods, rather than treating them as "black boxes." 💡✨

https://arxiv.org/pdf/2409.02668

#MachineLearning #DeepLearning #AI #Mathematics #DataScience #NeuralNetworks

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