AI Skills

🔅 AI Sentiment Analysis with PyTorch and Hugging Face Transformers 📝 Build and deploy a sentiment analysis model using Hugging Face Transformers and PyTorch. 🌐 Author: Zhongyu Pan 🔰 Level: Beginner ⏰ Duration: 32m 📋 Topics: PyTorch, Sentiment Analysis 🔗 Join Machine Learning for more courses

🚀 8 Types of AI Agents You Should Know

AI agents are evolving beyond just text generation. Different architectures are being designed to specialize in reasoning, perception, action, and abstraction. Here’s a quick breakdown:

1️⃣ GPTs – general-purpose text generators, great for fluency and versatility. 2️⃣ MoE (Mixture of Experts) – route tasks to specialized subnetworks for efficiency. 3️⃣ Large Reasoning Models – optimized for multi-step logical reasoning. 4️⃣ Vision-Language Models – bridge perception and language for multimodal tasks. 5️⃣ Small Language Models – lightweight, cost-efficient agents for edge deployment. 6️⃣ Large Action Models – built to execute code, call APIs, and perform tasks autonomously. 7️⃣ Hierarchical Language Models – break problems into sub-tasks, enabling long-horizon planning. 8️⃣ Large Concept Models – capture abstract, high-level knowledge for generalization. 🔍 What this really shows is that “AI agents” are no longer a monolithic idea. They’re evolving into a system of complementary architectures—each optimized for a different layer of intelligence.

📢 Hugging Face is now integrated with Kaggle Notebooks Starting today, Kaggle users can directly use any Hugging Face models in their notebooks — without manual downloads, token setup, or additional libraries. 🤝 Hugging Face and Kaggle platforms announced a partnership that will allow competition participants and researchers to work with the latest SOTA models literally "out of the box." 🔥 This is just the first step: teams are already working on further integration to make working with HF models even more convenient within the Kaggle ecosystem. 🔗 You can try it right now — support is already included in the Kaggle Notebooks environment. https://huggingface.co/blog/kaggle-integration

🔅 Agentic AI and Autonomous Development

📝 Discover how agentic AI supports autonomous development, including its practical implications for future workflows.

📕 Agentic Coding Mastery AI that writes, debugs, and ships code.

Daily updates on Claude, Codex, Gemini, plus Cursor, Windsurf, Devin, GitHub Copilot, Replit Agent, OpenCode and Many more.

• 🧠 Prompt engineering • 🛠 Tool-calling & MCP • ⚡️ Autopilot dev loops • 🧩 Multi-agent swarms • 🔌 IDE integrations • 📦 Code generation & refactoring • 🐛 Automated debugging & PR reviews For builders who ship faster with AI—without losing control. 👉 Learn, experiment, and break things (responsibly). 🔗 AI Coding

🔎 Using TensorFlow Object Detection API with OpenVINO™ 🛠 TensorFlow, or TF for short, is an open-source framework for machine learning. 🔰 The TensorFlow Object Detection API is an open-source computer vision framework built on top of TensorFlow. 🔰It is used for building object detection and instance segmentation models that can localize multiple objects in the same image. 🔰TensorFlow Object Detection API supports various architectures and models, which can be found and downloaded from the TensorFlow Hub. 🌐 Links: Github