Machine Learning with Python

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📂 Tags: #transformers #python #nlp http://t.me/codeprogrammer 🔒 The Transformer's decoder clearly explained 👇

📂 Tags: #transformers #python #nlp http://t.me/codeprogrammer 🔒 The Transformer's encoder clearly explained 👇

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✅ 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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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

Andrew Ng just released two new AI Python courses for beginners! The course teaches how to write code using AI. If you're thinking about learning to code, now is the perfect time to do so. https://deeplearning.ai/short-courses/ai-python-for-beginners/ http://t.me/codeprogrammer 🔒 💡 #deeplearning #AI #ML #python

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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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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