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Machine Learning with Python

Machine Learning with Python

@codeprogrammer

Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.

Admin: @HusseinSheikho || @Hussein_Sheikho
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Post #3155 9.69K
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Post #3152 10.8K
✅ Screenshot-To-Code

🔨 This is a simple application that converts a screenshot into HTML/Tailwind CSS code .

✔️ The app uses GPT-4 Vision to generate code and DALL-E 3 to create similar images .

✅ The app has a React/Vite frontend and a FastAPI backend , and requires an OpenAI API key with access to the GPT-4 Vision API .

🔗 links: https://github.com/abi/screenshot-to-code

📂 Tags: #html #openai #chatgpt

http://t.me/codeprogrammer 🔒
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Post #3149 46.8K
Data Science Cheat Sheets
Quick help to make a data scientist's life easier

About Dataset
A collection of cheat sheets for various data-science related languages and topics


http://t.me/codeprogrammer 🔒

💡 #deeplearning #AI #ML #python
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Post #3142 10.9K
Building Agents: Free Course

We just released a course with > 20 videos & notebooks focused on building agents. All code is open-source and the course is free!

Context
Back in June, I gave at talk at @aiDotEngineer on building agents with LangGraph. I got ~2 hrs of questions. We took these questions along with lots of feedback we've heard from users and built a course!

Module 1: Foundations
The first module includes several notebooks & videos that focus on what is an agent explained in simple terms, how to build various types of agents (routers, ReAct, etc), how to debug them w LangGraph Studio, and how to deploy them w LangGraph Cloud.

Module 2: Memory
One of the biggest questions we've heard is how to build long-running agents, which can remember important details. We show how memory works with LangGraph, and how to use various databases (SQLite, Postgres) to serve as agent memory.

Module 3: Human-In-The-Loop
Another central question with agents is allowing humans to approve actions (tools use) or modify the agent state (add feedback). We show various human in the loop interaction patterns that are supported in LangGraph, and also show how to stream the graph state during agent execution for human review.

Module 4: Controllability
The final module focuses on various design patterns for agent control flow, including parallelization of tasks and creating multi-agent teams with their own tasks / internal memory. This builds up into a customizable multi agent system for research that pulls together themes from the entire course.

Course (links to code, all videos):
https://academy.langchain.com/courses/intro-to-langgraph

http://t.me/codeprogrammer 🔒

💡 #deeplearning #AI #ML #python
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Post #3140 8.62K
You us stars ⭐️ to support our post ❤️

Use stars ⭐️ only when you think the post deserves it, in order to draw the attention of other friends to read or view the post.
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Post #3139 10.9K
New course with @Intel! Multimodal RAG: Chat with Videos is available starting today!

Learn from @Vasudev_Lal to build an AI chat system that answers questions from video content!

Learn to:

📷 Create embeddings from videos
🧩 Build a RAG pipeline for data retrieval
💬 Use Large Vision-Language Models (LVLMs) for Q&A using both text and image inputs.

In this course, you will make API calls to access multimodal models hosted by @PredictionGuard on Intel’s cloud.

Enroll Free Here:
https://www.deeplearning.ai/short-courses/multimodal-rag-chat-with-videos/


http://t.me/codeprogrammer 🔒

💡 #deeplearning #AI #ML #python
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Post #3137 8.17K
1️⃣ Multiple Linear Regression exemplified for dummies

2️⃣ Simple Linear Regression exemplified for dummies

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