Machine Learning with Python

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The Hundred-Page Language Models Book Read it: https://github.com/aburkov/theLMbook

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pandas for Data Science - Guide In this learning path, you’ll get started with pandas and get to know the ins and outs of how you can use it to analyze data with Python. pandas is a game-changer for #datascience and analytics, particularly if you came to #Python because you were searching for something more powerful than #Excel and #VBA. #pandas uses fast, flexible, and expressive data structures designed to make working with relational or labeled data both easy and intuitive. Read: https://realpython.com/learning-paths/pandas-data-science/ https://t.me/CodeProgrammer

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The Large Language Model Course How to become an LLM Scientist or Engineer from scratch Read FreeL

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Python, Bash and SQL Essentials for Data Engineering Specialization What you'll learn Develop #dataengineering solutions with a minimal and essential subset of the Python language and the Linux environment Design scripts to connect and query a #SQL #database using #Python Use a #scraping library in Python to read, identify and extract data from websites Enroll Free: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke https://t.me/DataScience4

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Running a Neural Network Model in OpenCV Many machine learning models have been developed, each with strengths and weaknesses. This catalog is not complete without neural network models. In OpenCV, you can use a neural network model developed using another framework. In this post, you will learn about the workflow of applying a neural network in OpenCV. Specifically, you will learn: 🏐 What OpenCV can use in its neural network model 🏐 How to prepare a neural network model for OpenCV Read: https://machinelearningmastery.com/running-a-neural-network-model-in-opencv/

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"Introduction to Applied Linear Algebra" by S. Boyd (Stanford) & L. Vandenberghe (UCLA) 📘 Freely available at: https://web.stanford.edu/~boyd/vmls/ 📽 Lecture Videos at: https://youtube.com/playlist?list=PLoROMvodv4rMz-WbFQtNUsUElIh2cPmN9

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Practical Deep Learning A free course designed for people with some coding experience, who want to learn how to apply deep learning and machine learning to practical problems. New! Enroll Free: https://course.fast.ai/

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how transformers remember facts

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MIT's "Machine Learning" lecture notes PDF: https://introml.mit.edu/_static/spring24/LectureNotes/6_390_lecture_notes_spring24.pdf

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🚀 EXCITING UPDATE ALERT 🚀 A brand-new, FREE course on LLM evaluations has just been launched in collaboration with Elvis Saravia! Dive into the world of AI model assessments and elevate your skills with cutting-edge insights. 📚 What’s Included? - ⚖️ In-depth coverage of LLM-as-a-judge metrics, LLM unit testing, monitoring, and beyond - 🤖 Hands-on experience with real-world projects, such as building a YouTube search agent - 🎉 Access to open-source models via LiteLLM Whether you're an AI enthusiast, developer, or researcher, this course is designed to empower you with practical knowledge and tools. Don’t miss out—enroll now to secure your spot! 👇 https://www.comet.com/site/llm-course/

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Some people asked me about a resource for learning about Transformers. Here's a good one I am sharing again -- it covers just about everything you need to know. brandonrohrer.com/transformers Amazing stuff. It's totally worth your weekend.

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