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Modern projelerde en kritik nokta ilk temas süresidir. betchan login süreci minimum adım, maksimum hız prensibiyle tasarlanmış. Uzun ve karmaşık formlar yerine sade, hızlı ve kullanıcı odaklı bir akış sunuluyor.
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Platforma giriş yaptıktan sonra sistem sizi statik kampanyalarla değil, dinamik promosyon yapısıyla karşılıyor.
betchan casino bonus kampanyaları, kullanıcı davranışına göre optimize edilmiş teşvikler sunarak oyun süresini artırıyor.
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Platformun oyun kütüphanesi geniş ve teknik olarak güçlü. Binlerce slot, canlı masa oyunları ve optimize edilmiş grafik motoru sayesinde sistem yüksek performansta çalışıyor.
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Kaggle offers interactive courses that will help you quickly understand the key topics of DS and ML.
The format is simple: short lessons, practical tasks, and a certificate upon completion — all for free.
Inside:
• basics of Python for data analysis;
• machine learning and working with models;
• pandas, SQL, visualization;
• advanced techniques and practical cases.
Each course takes just 3–5 hours and immediately provides practical knowledge for work.
tags: #ML #DEEPLEARNING #AI
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Forwarded from Data Analytics
#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🔴
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
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Google
polars-cheat-sheet.ipynb
Run, share, and edit Python notebooks
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If you work with AI, agents, or APIs, this is the foundation that developers in top companies are currently going through.
https://anthropic.skilljar.com/claude-with-the-anthropic-api
https://anthropic.skilljar.com/introduction-to-model-context-protocol
https://anthropic.skilljar.com/claude-in-amazon-bedrock
https://anthropic.skilljar.com/claude-with-google-vertex
https://anthropic.skilljar.com/model-context-protocol-advanced-topics
https://anthropic.skilljar.com/claude-code-in-action
tags: #courses #ai
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If you want to understand AI not through "vacuum" courses, but through real open-source projects - here's a top list of repos that really lead you from the basics to practice:
1) Karpathy – Neural Networks: Zero to Hero
The most understandable introduction to neural networks and backprop "in layman's terms"
https://github.com/karpathy/nn-zero-to-hero
2) Hugging Face Transformers
The main library of modern NLP/LLM: models, tokenizers, fine-tuning
https://github.com/huggingface/transformers
3) FastAI – Fastbook
Practical DL training through projects and experiments
https://github.com/fastai/fastbook
4) Made With ML
ML as an engineering system: pipelines, production, deployment, monitoring
https://github.com/GokuMohandas/Made-With-ML
5) Machine Learning System Design (Chip Huyen)
How to build ML systems in real business: data, metrics, infrastructure
https://github.com/chiphuyen/machine-learning-systems-design
6) Awesome Generative AI Guide
A collection of materials on GenAI: from basics to practice
https://github.com/aishwaryanr/awesome-generative-ai-guide
7) Dive into Deep Learning (D2L)
One of the best books on DL + code + assignments
https://github.com/d2l-ai/d2l-en
Save it for yourself - this is a base on which you can really grow into an ML/LLM engineer.
#Python #datascience #DataAnalysis #MachineLearning #AI #DeepLearning #LLMS
https://news.1rj.ru/str/CodeProgrammer
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🌟 Generative AI Training for Beginners
A course from Microsoft with 21 lessons covering the basics of creating applications based on generative AI. Each lesson includes theory and practical examples in Python and TypeScript, allowing you to learn at a comfortable pace.
🚀 Key features:
- 21 lessons on generative #AI
- Support for Python and TypeScript
- Lessons with theory and practical tasks
- Additional resources for in-depth study
- Multilingual support
📌 GitHub: https://github.com/microsoft/generative-ai-for-beginners
#python #LLMS #generative_Ai
https://news.1rj.ru/str/CodeProgrammer
A course from Microsoft with 21 lessons covering the basics of creating applications based on generative AI. Each lesson includes theory and practical examples in Python and TypeScript, allowing you to learn at a comfortable pace.
🚀 Key features:
- 21 lessons on generative #AI
- Support for Python and TypeScript
- Lessons with theory and practical tasks
- Additional resources for in-depth study
- Multilingual support
📌 GitHub: https://github.com/microsoft/generative-ai-for-beginners
#python #LLMS #generative_Ai
https://news.1rj.ru/str/CodeProgrammer
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Excellent free courses on neural networks from Nvidia— the company decided to share knowledge that usually costs 90 dollars.
Here's everything important: video processing, app development, robotics, and much more. An electronic certificate is issued upon completion of the training.
We gain useful knowledge —
https://developer.nvidia.com/join-nvidia-developer-program
https://news.1rj.ru/str/CodeProgrammer🌟
Here's everything important: video processing, app development, robotics, and much more. An electronic certificate is issued upon completion of the training.
We gain useful knowledge —
https://developer.nvidia.com/join-nvidia-developer-program
https://news.1rj.ru/str/CodeProgrammer
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⚡️ MIT has released a full course on Deep Learning - for free
MIT OpenCourseWare has published the course 6.7960 Deep Learning (Fall 2024) — one of the most relevant and practical university courses on modern deep learning.
It includes full-fledged lectures at a top-university level, available for free.
What's in the course
- Fundamentals of deep learning and architectures
- Transformers and modern models
- Generative AI
- Self-supervised learning
- Scaling laws
- Diffusion and generative models
- RL and reinforcement learning
- Practical analyses of modern approaches
The lectures are led by MIT professors and researchers working with cutting-edge technologies.
Why it's valuable
This is not a basic course for beginners.
This is material at the level of:
- ML engineers
- researchers
- developers of AI systems
The course reflects the current state of the industry and explains how people who create modern models think.
It's perfect if you:
- already know Python and the basics of ML
- want to transition to Deep Learning
- work with LLMs / AI
- want a systematic understanding instead of individual tutorials
If you want FAANG / Research-level knowledge - learn from MIT.
https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/video_galleries/lecture-videos/
https://news.1rj.ru/str/CodeProgrammer✅
MIT OpenCourseWare has published the course 6.7960 Deep Learning (Fall 2024) — one of the most relevant and practical university courses on modern deep learning.
It includes full-fledged lectures at a top-university level, available for free.
What's in the course
- Fundamentals of deep learning and architectures
- Transformers and modern models
- Generative AI
- Self-supervised learning
- Scaling laws
- Diffusion and generative models
- RL and reinforcement learning
- Practical analyses of modern approaches
The lectures are led by MIT professors and researchers working with cutting-edge technologies.
Why it's valuable
This is not a basic course for beginners.
This is material at the level of:
- ML engineers
- researchers
- developers of AI systems
The course reflects the current state of the industry and explains how people who create modern models think.
It's perfect if you:
- already know Python and the basics of ML
- want to transition to Deep Learning
- work with LLMs / AI
- want a systematic understanding instead of individual tutorials
If you want FAANG / Research-level knowledge - learn from MIT.
https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/video_galleries/lecture-videos/
https://news.1rj.ru/str/CodeProgrammer
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Pandas vs. Polars: A Complete Comparison of Syntax, Speed, and Memory
Need help choosing the right #Python dataframe library? This article compares #Pandas and #Polars to help you decide.
If you've been working with data in Python, you've almost certainly used pandas. It's been the go-to library for data manipulation for over a decade. But recently, Polars has been gaining serious traction. Polars promises to be faster, more memory-efficient, and more intuitive than pandas. But is it worth learning? And how different is it really?
In this article, we'll compare pandas and Polars side-by-side. You'll see performance benchmarks, and learn the syntax differences. By the end, you'll be able to make an informed decision for your next data project.
Read: https://www.kdnuggets.com/pandas-vs-polars-a-complete-comparison-of-syntax-speed-and-memory
https://news.1rj.ru/str/CodeProgrammer 🌺
Need help choosing the right #Python dataframe library? This article compares #Pandas and #Polars to help you decide.
If you've been working with data in Python, you've almost certainly used pandas. It's been the go-to library for data manipulation for over a decade. But recently, Polars has been gaining serious traction. Polars promises to be faster, more memory-efficient, and more intuitive than pandas. But is it worth learning? And how different is it really?
In this article, we'll compare pandas and Polars side-by-side. You'll see performance benchmarks, and learn the syntax differences. By the end, you'll be able to make an informed decision for your next data project.
Read: https://www.kdnuggets.com/pandas-vs-polars-a-complete-comparison-of-syntax-speed-and-memory
https://news.1rj.ru/str/CodeProgrammer 🌺
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Forwarded from Machine Learning
10 GitHub Repositories to Master System Design
Want to move beyond drawing boxes and arrows and actually understand how scalable systems are built? These GitHub repositories break down the concepts, patterns, and real-world trade-offs that make great system design possible.
Read: https://www.kdnuggets.com/10-github-repositories-to-master-system-design
https://news.1rj.ru/str/DataScienceM✅
Want to move beyond drawing boxes and arrows and actually understand how scalable systems are built? These GitHub repositories break down the concepts, patterns, and real-world trade-offs that make great system design possible.
Most engineers encounter system design when preparing for interviews, but in reality, it is much bigger than that. System design is about understanding how large-scale systems are built, why certain architectural decisions are made, and how trade-offs shape everything from performance to reliability. Behind every app you use daily, from messaging platforms to streaming services, there are careful decisions about databases, caching, load balancing, fault tolerance, and consistency models.
What makes system design challenging is that there is rarely a single correct answer. You are constantly balancing cost, scalability, latency, complexity, and future growth. Should you shard the database now or later? Do you prioritize strong consistency or eventual consistency? Do you optimize for reads or writes? These are the kinds of questions that separate surface-level knowledge from real architectural thinking.
The good news is that many experienced engineers have documented these patterns, breakdowns, and interview strategies openly on GitHub. Instead of learning only through trial and error, you can study real case studies, curated resources, structured interview frameworks, and production-grade design principles from the community.
In this article, we review 10 GitHub repositories that cover fundamentals, interview preparation, distributed systems concepts, machine learning system design, agent-based architectures, and real-world scalability case studies. Together, they provide a practical roadmap for developing the structured thinking required to design reliable systems at scale.
Read: https://www.kdnuggets.com/10-github-repositories-to-master-system-design
https://news.1rj.ru/str/DataScienceM
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Pandas-Cheat-Sheet.pdf
2.7 MB
This cheat sheet—part of our Complete Guide to #NumPy, #pandas, and #DataVisualization—offers a handy reference for essential pandas commands, focused on efficient #datamanipulation and analysis. Using examples from the Fortune 500 Companies #Dataset, it covers key pandas operations such as reading and writing data, selecting and filtering DataFrame values, and performing common transformations.
You'll find easy-to-follow examples for grouping, sorting, and aggregating data, as well as calculating statistics like mean, correlation, and summary statistics. Whether you're cleaning datasets, analyzing trends, or visualizing data, this cheat sheet provides concise instructions to help you navigate pandas’ powerful functionality.
Designed to be practical and actionable, this guide ensures you can quickly apply pandas’ versatile data manipulation tools in your workflow.
https://news.1rj.ru/str/CodeProgrammer
You'll find easy-to-follow examples for grouping, sorting, and aggregating data, as well as calculating statistics like mean, correlation, and summary statistics. Whether you're cleaning datasets, analyzing trends, or visualizing data, this cheat sheet provides concise instructions to help you navigate pandas’ powerful functionality.
Designed to be practical and actionable, this guide ensures you can quickly apply pandas’ versatile data manipulation tools in your workflow.
https://news.1rj.ru/str/CodeProgrammer
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