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This well-structured GitHub repository is a goldmine for beginners who want to learn PyTorch with hands-on examples and clear explanations
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Best Data Science Archive Notes
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๐๐จ๐ ๐ข๐ฌ๐ญ๐ข๐ ๐๐๐ ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐ง ๐๐ฑ๐ฉ๐ฅ๐๐ข๐ง๐๐ ๐ฌ๐ข๐ฆ๐ฉ๐ฅ๐ฒ
If youโve just started learning Machine Learning, ๐๐จ๐ ๐ข๐ฌ๐ญ๐ข๐ ๐๐๐ ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐ง is one of the most important and misunderstood algorithms.
Hereโs everything you need to know ๐
๐ โจ ๐๐ก๐๐ญ ๐ข๐ฌ ๐๐จ๐ ๐ข๐ฌ๐ญ๐ข๐ ๐๐๐ ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐ง?
Itโs a supervised ML algorithm used to predict probabilities and classify data into binary outcomes (like 0 or 1, Yes or No, Spam or Not Spam).
๐ โจ ๐๐จ๐ฐ ๐ข๐ญ ๐ฐ๐จ๐ซ๐ค๐ฌ?
It starts like Linear Regression, but instead of outputting continuous values, it passes the result through a ๐ฌ๐ข๐ ๐ฆ๐จ๐ข๐ ๐๐ฎ๐ง๐๐ญ๐ข๐จ๐ง to map the result between 0 and 1.
๐๐ณ๐ฐ๐ฃ๐ข๐ฃ๐ช๐ญ๐ช๐ต๐บ = ๐ / (๐ + ๐โป(๐ฐ๐ฑ + ๐))
Here,
๐ฐ = weights
๐ฑ = inputs
๐ = bias
๐ = Eulerโs number (approx. 2.718)
๐ โจ ๐๐ก๐ฒ ๐ง๐จ๐ญ ๐๐ข๐ง๐๐๐ซ ๐๐๐ ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐ง?
Because Linear Regression predicts any number from -โ to +โ, which doesnโt make sense for probability.
We need outputs between 0 and 1 and thatโs where the sigmoid function helps.
๐ โจ ๐๐จ๐ฌ๐ฌ ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง ๐ฎ๐ฌ๐๐?
๐๐ข๐ง๐๐ซ๐ฒ ๐๐ซ๐จ๐ฌ๐ฌ-๐๐ง๐ญ๐ซ๐จ๐ฉ๐ฒ
โ = โ(y log(p) + (1 โ y) log(1 โ p))
Where y is the actual value (0 or 1), and p is the predicted probability
๐ โจ ๐๐ฉ๐ฉ๐ฅ๐ข๐๐๐ญ๐ข๐จ๐ง๐ฌ ๐ข๐ง ๐ซ๐๐๐ฅ ๐ฅ๐ข๐๐:
๐๐ฆ๐๐ข๐ฅ ๐๐ฉ๐๐ฆ ๐๐๐ญ๐๐๐ญ๐ข๐จ๐ง
๐๐ข๐ฌ๐๐๐ฌ๐ ๐๐ซ๐๐๐ข๐๐ญ๐ข๐จ๐ง
๐๐ฎ๐ฌ๐ญ๐จ๐ฆ๐๐ซ ๐๐ก๐ฎ๐ซ๐ง ๐๐ซ๐๐๐ข๐๐ญ๐ข๐จ๐ง
๐๐ฅ๐ข๐๐ค-๐๐ก๐ซ๐จ๐ฎ๐ ๐ก ๐๐๐ญ๐ ๐๐ซ๐๐๐ข๐๐ญ๐ข๐จ๐ง
๐๐ข๐ง๐๐ซ๐ฒ ๐ฌ๐๐ง๐ญ๐ข๐ฆ๐๐ง๐ญ ๐๐ฅ๐๐ฌ๐ฌ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง
๐ โจ ๐๐ฌ. ๐๐ญ๐ก๐๐ซ ๐๐ฅ๐๐ฌ๐ฌ๐ข๐๐ข๐๐ซ๐ฌ
Itโs fast, interpretable, and easy to implement, but it struggles with non-linearly separable data unlike Decision Trees or SVMs.
๐ โจ ๐๐๐ง ๐ข๐ญ ๐ก๐๐ง๐๐ฅ๐ ๐ฆ๐ฎ๐ฅ๐ญ๐ข๐ฉ๐ฅ๐ ๐๐ฅ๐๐ฌ๐ฌ๐๐ฌ?
Yes, using One-vs-Rest (OvR) or Softmax in Multinomial Logistic Regression.
๐ โจ ๐๐ฑ๐๐ฆ๐ฉ๐ฅ๐ ๐ข๐ง ๐๐ฒ๐ญ๐ก๐จ๐ง
If youโve just started learning Machine Learning, ๐๐จ๐ ๐ข๐ฌ๐ญ๐ข๐ ๐๐๐ ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐ง is one of the most important and misunderstood algorithms.
Hereโs everything you need to know ๐
๐ โจ ๐๐ก๐๐ญ ๐ข๐ฌ ๐๐จ๐ ๐ข๐ฌ๐ญ๐ข๐ ๐๐๐ ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐ง?
Itโs a supervised ML algorithm used to predict probabilities and classify data into binary outcomes (like 0 or 1, Yes or No, Spam or Not Spam).
๐ โจ ๐๐จ๐ฐ ๐ข๐ญ ๐ฐ๐จ๐ซ๐ค๐ฌ?
It starts like Linear Regression, but instead of outputting continuous values, it passes the result through a ๐ฌ๐ข๐ ๐ฆ๐จ๐ข๐ ๐๐ฎ๐ง๐๐ญ๐ข๐จ๐ง to map the result between 0 and 1.
๐๐ณ๐ฐ๐ฃ๐ข๐ฃ๐ช๐ญ๐ช๐ต๐บ = ๐ / (๐ + ๐โป(๐ฐ๐ฑ + ๐))
Here,
๐ฐ = weights
๐ฑ = inputs
๐ = bias
๐ = Eulerโs number (approx. 2.718)
๐ โจ ๐๐ก๐ฒ ๐ง๐จ๐ญ ๐๐ข๐ง๐๐๐ซ ๐๐๐ ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐ง?
Because Linear Regression predicts any number from -โ to +โ, which doesnโt make sense for probability.
We need outputs between 0 and 1 and thatโs where the sigmoid function helps.
๐ โจ ๐๐จ๐ฌ๐ฌ ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง ๐ฎ๐ฌ๐๐?
๐๐ข๐ง๐๐ซ๐ฒ ๐๐ซ๐จ๐ฌ๐ฌ-๐๐ง๐ญ๐ซ๐จ๐ฉ๐ฒ
โ = โ(y log(p) + (1 โ y) log(1 โ p))
Where y is the actual value (0 or 1), and p is the predicted probability
๐ โจ ๐๐ฉ๐ฉ๐ฅ๐ข๐๐๐ญ๐ข๐จ๐ง๐ฌ ๐ข๐ง ๐ซ๐๐๐ฅ ๐ฅ๐ข๐๐:
๐๐ฆ๐๐ข๐ฅ ๐๐ฉ๐๐ฆ ๐๐๐ญ๐๐๐ญ๐ข๐จ๐ง
๐๐ข๐ฌ๐๐๐ฌ๐ ๐๐ซ๐๐๐ข๐๐ญ๐ข๐จ๐ง
๐๐ฎ๐ฌ๐ญ๐จ๐ฆ๐๐ซ ๐๐ก๐ฎ๐ซ๐ง ๐๐ซ๐๐๐ข๐๐ญ๐ข๐จ๐ง
๐๐ฅ๐ข๐๐ค-๐๐ก๐ซ๐จ๐ฎ๐ ๐ก ๐๐๐ญ๐ ๐๐ซ๐๐๐ข๐๐ญ๐ข๐จ๐ง
๐๐ข๐ง๐๐ซ๐ฒ ๐ฌ๐๐ง๐ญ๐ข๐ฆ๐๐ง๐ญ ๐๐ฅ๐๐ฌ๐ฌ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง
๐ โจ ๐๐ฌ. ๐๐ญ๐ก๐๐ซ ๐๐ฅ๐๐ฌ๐ฌ๐ข๐๐ข๐๐ซ๐ฌ
Itโs fast, interpretable, and easy to implement, but it struggles with non-linearly separable data unlike Decision Trees or SVMs.
๐ โจ ๐๐๐ง ๐ข๐ญ ๐ก๐๐ง๐๐ฅ๐ ๐ฆ๐ฎ๐ฅ๐ญ๐ข๐ฉ๐ฅ๐ ๐๐ฅ๐๐ฌ๐ฌ๐๐ฌ?
Yes, using One-vs-Rest (OvR) or Softmax in Multinomial Logistic Regression.
๐ โจ ๐๐ฑ๐๐ฆ๐ฉ๐ฅ๐ ๐ข๐ง ๐๐ฒ๐ญ๐ก๐จ๐ง
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)
pred = model.predict(X_test)
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Best Data Science Archive Notes
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๐๐ฐ Linear Algebra in Python: Matrix Inverses and Least Squares โ https://realpython.com/python-linear-algebra/
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This channels is for Programmers, Coders, Software Engineers.
0๏ธโฃ Python
1๏ธโฃ Data Science
2๏ธโฃ Machine Learning
3๏ธโฃ Data Visualization
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Converting Pandas DataFrames to PyTorch DataLoaders for Custom Deep Learning Model Training
Link: https://machinelearningmastery.com/converting-pandas-dataframes-to-pytorch-dataloaders-for-custom-deep-learning-model-training/
Link: https://machinelearningmastery.com/converting-pandas-dataframes-to-pytorch-dataloaders-for-custom-deep-learning-model-training/
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Top 50 LLM Interview Questions!
A comprehensive resource that covers traditional ML basics, model architectures, real-world case studies, and theoretical foundations.
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A comprehensive resource that covers traditional ML basics, model architectures, real-world case studies, and theoretical foundations.
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Top 50 LLM Interview Questions!
#LLM #AIInterviews #MachineLearning #DeepLearning #NLP #LLMInterviewPrep #ModelArchitectures #AITheory #TechInterviews #MLBasics #InterviewQuestions #LargeLanguageModels
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๐ Design Dropbox
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๐ Design URL Shortener
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๐ Design Amazon Prime Video
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๐ Design Twitter
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๐ Design Uber
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๐ Design TikTok
๐ https://bit.ly/3UUlKxP
๐ Design Facebook Newsfeed
๐ https://bit.ly/3RldaW7
๐ Design Web Crawler
๐ https://bit.ly/3DPZTBB
๐ Design API Rate Limiter
๐ https://bit.ly/3BIVuh7
โ ๐๐ถ๐ป๐ฎ๐น ๐ฆ๐๐๐๐ฒ๐บ ๐๐ฒ๐๐ถ๐ด๐ป ๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ๐
๐ All Solved Case Studies
๐ https://bit.ly/3dCG1rc
๐ Design Terms & Terminology
๐ https://bit.ly/3Om9d3H
๐ Complete Basics Series
๐https://bit.ly/3rG1cfr
If you're targeting top product companies or leveling up your backend/system design skills, this is for you.
System Design is no longer optional in tech interviews. Itโs a must-have.
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๐ System Design Basics
๐ https://bit.ly/3SuUR0Y)
๐ Horizontal & Vertical Scaling
๐ https://bit.ly/3slq5xh)
๐ Load Balancing & Message Queues
๐ https://bit.ly/3sp0FP4)
๐ HLD vs LLD, Hashing, Monolith vs Microservices
๐ https://bit.ly/3DnEfEm)
๐ Caching, Indexing, Proxies
๐ https://bit.ly/3SvyVDc)
๐ Networking, CDN, How Browsers Work
๐ https://bit.ly/3TOHQRb
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๐ https://bit.ly/3sqQrhj
๐ Estimation, Performance Optimization
๐ https://bit.ly/3z9dSPN
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๐ https://bit.ly/3zcsfmv
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๐ https://bit.ly/3z8Aa49)
โ ๐ ๐ผ๐๐ ๐๐๐ธ๐ฒ๐ฑ ๐ฆ๐๐๐๐ฒ๐บ ๐๐ฒ๐๐ถ๐ด๐ป ๐ค๐๐ฒ๐๐๐ถ๐ผ๐ป๐
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โ ๐๐ฎ๐๐ฒ ๐ฆ๐๐๐ฑ๐ ๐๐ฒ๐ฒ๐ฝ ๐๐ถ๐๐ฒ๐ (๐ฃ๐ฟ๐ฎ๐ฐ๐๐ถ๐ฐ๐ฒ ๐ง๐ต๐ฒ๐๐ฒ!)
๐ Design Netflix
๐ https://bit.ly/3GrAUG1
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๐ https://bit.ly/3OgGJrL
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๐ Design Tinder
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๐ Design Yelp
๐ https://bit.ly/3E7IgO5
๐ Design WhatsApp
๐ https://bit.ly/3M2GOhP
๐ Design URL Shortener
๐ https://bit.ly/3xP078x
๐ Design Amazon Prime Video
๐https://bit.ly/3hVpWP4
๐ Design Twitter
๐ https://bit.ly/3qIG9Ih
๐ Design Uber
๐ https://bit.ly/3fyvnlT
๐ Design TikTok
๐ https://bit.ly/3UUlKxP
๐ Design Facebook Newsfeed
๐ https://bit.ly/3RldaW7
๐ Design Web Crawler
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๐ Design API Rate Limiter
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๐ All Solved Case Studies
๐ https://bit.ly/3dCG1rc
๐ Design Terms & Terminology
๐ https://bit.ly/3Om9d3H
๐ Complete Basics Series
๐https://bit.ly/3rG1cfr
#SystemDesign #TechInterviews #MAANGPrep #BackendEngineering #ScalableSystems #HLD #LLD #SoftwareArchitecture #DesignCaseStudies #CloudArchitecture #DataEngineering #DesignPatterns #LoadBalancing #Microservices #DistributedSystems
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mcp guide.pdf.pdf
16.7 MB
A comprehensive PDF has been compiled that includes all MCP-related posts shared over the past six months.
(75 pages, 10+ projects & visual explainers)
Over the last half year, content has been published about the Modular Computation Protocol (MCP), which has gained significant interest and engagement from the AI community. In response to this enthusiasm, all tutorials have been gathered in one place, featuring:
* The fundamentals of MCP
* Explanations with visuals and code
* 11 hands-on projects for AI engineers
Projects included:
1. Build a 100% local MCP Client
2. MCP-powered Agentic RAG
3. MCP-powered Financial Analyst
4. MCP-powered Voice Agent
5. A Unified MCP Server
6. MCP-powered Shared Memory for Claude Desktop and Cursor
7. MCP-powered RAG over Complex Docs
8. MCP-powered Synthetic Data Generator
9. MCP-powered Deep Researcher
10. MCP-powered RAG over Videos
11. MCP-powered Audio Analysis Toolkit
(75 pages, 10+ projects & visual explainers)
Over the last half year, content has been published about the Modular Computation Protocol (MCP), which has gained significant interest and engagement from the AI community. In response to this enthusiasm, all tutorials have been gathered in one place, featuring:
* The fundamentals of MCP
* Explanations with visuals and code
* 11 hands-on projects for AI engineers
Projects included:
1. Build a 100% local MCP Client
2. MCP-powered Agentic RAG
3. MCP-powered Financial Analyst
4. MCP-powered Voice Agent
5. A Unified MCP Server
6. MCP-powered Shared Memory for Claude Desktop and Cursor
7. MCP-powered RAG over Complex Docs
8. MCP-powered Synthetic Data Generator
9. MCP-powered Deep Researcher
10. MCP-powered RAG over Videos
11. MCP-powered Audio Analysis Toolkit
#MCP #ModularComputationProtocol #AIProjects #DeepLearning #ArtificialIntelligence #RAG #VoiceAI #SyntheticData #AIAgents #AIResearch #TechWriting #OpenSourceAI #AI #python
โ๏ธ Our Telegram channels: https://t.iss.one/addlist/0f6vfFbEMdAwODBk๐ฑ Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Please open Telegram to view this post
VIEW IN TELEGRAM
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