More and more applications are using computer vision to detect and recognize objects. These applications usually execute on large computers, but developers can save money and power by running them on single-board computers (SBCs). The Raspberry Pi 4 is one of the most popular SBCs available. It's also the first computer in the Raspberry Pi family powerful enough to execute computer vision applications. Also, the software needed to build these applications can be downloaded freely from the Internet. In this course, instructor Matt Scarpino shows programmers how to write and execute computer vision applications on the Raspberry Pi 4. Matt introduces you to using the Thonny IDE, the OpenCV library, and NumPy array operations. He steps through object detection and neural networks, then explores convolutional neural networks (CNNs), including the Keras package and the TensorFlow package. Matt also walks you through what you can do with a Raspberry Pi HQ camera.
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To truly master these foundational algorithms
It's crucial to dive deeper into their real-world applications and understand how AI is shaping the future.
That's where "The Most Effective Guide to Master AI" comes in! This comprehensive guide covers everything you need to know:
- Real-world AI applications
- Computer Vision
- Generative Models
- Essential AI tools
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Want to become an Agent AI Expert in 2025?
🤩AI isn’t just evolving—it’s transforming industries. And agentic AI is leading the charge!
Here’s your 6-step guide to mastering it:
1️⃣ Master AI Fundamentals – Python, TensorFlow & PyTorch 📊
2️⃣ Understand Agentic Systems – Learn reinforcement learning 🧠
3️⃣ Get Hands-On with Projects – OpenAI Gym & Rasa 🔍
4️⃣ Learn Prompt Engineering – Tools like ChatGPT & LangChain ⚙️
5️⃣ Stay Updated – Follow Arxiv, GitHub & AI newsletters 📰
6️⃣ Join AI Communities – Engage in forums like Reddit & Discord 🌐
🤩AI isn’t just evolving—it’s transforming industries. And agentic AI is leading the charge!
Here’s your 6-step guide to mastering it:
1️⃣ Master AI Fundamentals – Python, TensorFlow & PyTorch 📊
2️⃣ Understand Agentic Systems – Learn reinforcement learning 🧠
3️⃣ Get Hands-On with Projects – OpenAI Gym & Rasa 🔍
4️⃣ Learn Prompt Engineering – Tools like ChatGPT & LangChain ⚙️
5️⃣ Stay Updated – Follow Arxiv, GitHub & AI newsletters 📰
6️⃣ Join AI Communities – Engage in forums like Reddit & Discord 🌐
🎯 AI Agent is all about creating intelligent systems that can make decisions autonomously—perfect for businesses aiming to scale with minimal human intervention.
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📖 AutoGen, ChatGPT API, Streamlit, Google Cloud, build and deploy LLM AI Agents based apps (locally or at scale)
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01. Intro to LLM Agents - Part 01.zip
380.2 MB
01. Intro to LLM Agents - Part 01
01. Intro to LLM Agents - Part 02.zip
137.7 MB
01. Intro to LLM Agents - Part 02
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02_LLM_Agents_Implementation_with_OpenAI's_ChatGPT_Part_01.zip
321.7 MB
02. LLM Agents Implementation (with OpenAI's ChatGPT) - Part 01
02_LLM_Agents_Implementation_with_OpenAI's_ChatGPT_Part_02.zip
171 MB
02. LLM Agents Implementation (with OpenAI's ChatGPT) - Part 02
02_LLM_Agents_Implementation_with_OpenAI's_ChatGPT_Part_03.zip
380.1 MB
02. LLM Agents Implementation (with OpenAI's ChatGPT) - Part 03
02_LLM_Agents_Implementation_with_OpenAI's_ChatGPT_Part_04.zip
195 MB
02. LLM Agents Implementation (with OpenAI's ChatGPT) - Part 04
02_LLM_Agents_Implementation_with_OpenAI's_ChatGPT_Part_05.zip
229.5 MB
02. LLM Agents Implementation (with OpenAI's ChatGPT) - Part 05
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03_Optional_Running_Open_Source_LLMs_Locally_Free_instead_of_OpenAI's.zip
245.2 MB
03. [Optional] Running Open Source LLMs Locally (Free) instead of OpenAI's ChatGPT - Part 01
03_Optional_Running_Open_Source_LLMs_Locally_Free_instead_of_OpenAI's.zip
330.7 MB
03. [Optional] Running Open Source LLMs Locally (Free) instead of OpenAI's ChatGPT - Part 02
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