🐱 5 of the Best GitHub Repos
🔃 for Data Scientists

👨🏻‍💻 When I was just starting out and trying to get into the "data" field, I had no one to guide me, nor did I know what exactly I should study. To be honest, I was confused for months and felt lost.

▶️ But doing projects was like water on fire and helped me a lot to build my skills.

〰 Repo Awesome Data Analysis

🏷 A complete treasure trove of everything you need to start: SQL, Python, AI, data analysis, and more... In short, if you want to start from zero and strengthen your foundation, start here first.

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〰 Repo Data Scientist Handbook

🏷 A concise handbook that tells you what you need to learn and what you can ignore for now.

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〰 Repo Cookiecutter Data Science

🏷 A standard project template used by professionals. With this template, you can structure your data analysis and AI projects like a pro.

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〰 Repo Data Science Cookie Cutter

🏷 This is also a very clean project template that teaches you how to build a data project that won’t fall apart tomorrow and can be easily updated. Meaning your projects will be useful in the real world from the start.

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〰 Repo ML From Scratch

🏷 Here, the main AI algorithms are implemented from scratch in simple language. It’s great for understanding how models really work and for explaining them well in your interviews.

🌐 #Data_Science #DataScience

These 9 lectures from Stanford are a pure goldmine for anyone wanting to learn and understand LLMs in depth

Lecture 1 - Transformer: https://lnkd.in/dGnQW39t

Lecture 2 - Transformer-Based Models & Tricks: https://lnkd.in/dT_VEpVH

Lecture 3 - Tranformers & Large Language Models: https://lnkd.in/dwjjpjaP

Lecture 4 - LLM Training: https://lnkd.in/dSi_xCEN

Lecture 5 - LLM tuning: https://lnkd.in/dUK5djpB

Lecture 6 - LLM Reasoning: https://lnkd.in/dAGQTNAM

Lecture 7 - Agentic LLMs: https://lnkd.in/dWD4j7vm

Lecture 8 - LLM Evaluation: https://lnkd.in/ddxE5zvb

Lecture 9 - Recap & Current Trends: https://lnkd.in/dGsTd8jN

Start understanding #LLMs in depth from the experts. Go through each step-by-step video.

https://news.1rj.ru/str/DataAnalyticsX 🔗

Hands-On Large Language Models

Inside:

Chapter 1: Introduction to Language Models
Chapter 2: Tokens and Embeddings
Chapter 3: Understanding the Transformer LLM from Inside
Chapter 4: Text Classification
Chapter 5: Text Clustering and Topic Modeling
Chapter 6: Prompt Engineering
Chapter 7: Advanced Techniques and Tools for Text Generation
Chapter 8: Semantic Search and Retrieval-Augmented Generation (RAG)
Chapter 9: Multimodal Large Language Models
Chapter 10: Creating Text Embedding Models
Chapter 11: Fine-Tuning Representation Models for Classification
Chapter 12: Fine-Tuning Generation Models

GitHub: http://github.com/HandsOnLLM/Hands-On-Large-Language-Models

👉 https://news.1rj.ru/str/DataAnalyticsX

👍 A fresh deep learning course from MIT is now available publicly

A full-fledged educational course has been published on the university's website: 24 lectures, practical tasks, homework assignments, and a collection of materials for self-study.

The program includes modern neural network architectures, generative models, transformers, inference, and other key topics.

A great opportunity to study deep learning based on the structure of a top university, free of charge and without simplifications — let's learn here.
https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/resources/lecture-videos/

tags: #python #deeplearning

➡ @codeprogrammer

This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

✅ https://news.1rj.ru/str/addlist/8_rRW2scgfRhOTc0

✅ https://news.1rj.ru/str/Codeprogrammer

#Polars Cheat Sheet with example

Learn and Run online: https://colab.research.google.com/github/FranzDiebold/polars-cheat-sheet/blob/main/polars-cheat-sheet.ipynb

https://news.1rj.ru/str/DataAnalyticsX 🔴