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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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𝐋𝐨𝐠𝐢𝐬𝐭𝐢𝐜 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐞𝐝 𝐬𝐢𝐦𝐩𝐥𝐲
If you’ve just started learning Machine Learning, 𝐋𝐨𝐠𝐢𝐬𝐭𝐢𝐜 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 is one of the most important and misunderstood algorithms.
Here’s everything you need to know 👇
𝟏 ⇨ 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐋𝐨𝐠𝐢𝐬𝐭𝐢𝐜 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧?
It’s 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).
𝟐 ⇨ 𝐇𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬?
It starts like Linear Regression, but instead of outputting continuous values, it passes the result through a 𝐬𝐢𝐠𝐦𝐨𝐢𝐝 𝐟𝐮𝐧𝐜𝐭𝐢𝐨𝐧 to map the result between 0 and 1.
𝘗𝘳𝘰𝘣𝘢𝘣𝘪𝘭𝘪𝘵𝘺 = 𝟏 / (𝟏 + 𝐞⁻(𝐰𝐱 + 𝐛))
Here,
𝐰 = weights
𝐱 = inputs
𝐛 = bias
𝐞 = Euler’s number (approx. 2.718)
𝟑 ⇨ 𝐖𝐡𝐲 𝐧𝐨𝐭 𝐋𝐢𝐧𝐞𝐚𝐫 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧?
Because Linear Regression predicts any number from -∞ to +∞, which doesn’t make sense for probability.
We need outputs between 0 and 1 and that’s where the sigmoid function helps.
𝟒 ⇨ 𝐋𝐨𝐬𝐬 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧 𝐮𝐬𝐞𝐝?
𝐁𝐢𝐧𝐚𝐫𝐲 𝐂𝐫𝐨𝐬𝐬-𝐄𝐧𝐭𝐫𝐨𝐩𝐲
ℒ = −(y log(p) + (1 − y) log(1 − p))
Where y is the actual value (0 or 1), and p is the predicted probability
𝟓 ⇨ 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐢𝐧 𝐫𝐞𝐚𝐥 𝐥𝐢𝐟𝐞:
𝐄𝐦𝐚𝐢𝐥 𝐒𝐩𝐚𝐦 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧
𝐃𝐢𝐬𝐞𝐚𝐬𝐞 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧
𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐂𝐡𝐮𝐫𝐧 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧
𝐂𝐥𝐢𝐜𝐤-𝐓𝐡𝐫𝐨𝐮𝐠𝐡 𝐑𝐚𝐭𝐞 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧
𝐁𝐢𝐧𝐚𝐫𝐲 𝐬𝐞𝐧𝐭𝐢𝐦𝐞𝐧𝐭 𝐜𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧
𝟔 ⇨ 𝐕𝐬. 𝐎𝐭𝐡𝐞𝐫 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐞𝐫𝐬
It’s fast, interpretable, and easy to implement, but it struggles with non-linearly separable data unlike Decision Trees or SVMs.
𝟕 ⇨ 𝐂𝐚𝐧 𝐢𝐭 𝐡𝐚𝐧𝐝𝐥𝐞 𝐦𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐜𝐥𝐚𝐬𝐬𝐞𝐬?
Yes, using One-vs-Rest (OvR) or Softmax in Multinomial Logistic Regression.
𝟖 ⇨ 𝐄𝐱𝐚𝐦𝐩𝐥𝐞 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧
If you’ve just started learning Machine Learning, 𝐋𝐨𝐠𝐢𝐬𝐭𝐢𝐜 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 is one of the most important and misunderstood algorithms.
Here’s everything you need to know 👇
𝟏 ⇨ 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐋𝐨𝐠𝐢𝐬𝐭𝐢𝐜 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧?
It’s 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).
𝟐 ⇨ 𝐇𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬?
It starts like Linear Regression, but instead of outputting continuous values, it passes the result through a 𝐬𝐢𝐠𝐦𝐨𝐢𝐝 𝐟𝐮𝐧𝐜𝐭𝐢𝐨𝐧 to map the result between 0 and 1.
𝘗𝘳𝘰𝘣𝘢𝘣𝘪𝘭𝘪𝘵𝘺 = 𝟏 / (𝟏 + 𝐞⁻(𝐰𝐱 + 𝐛))
Here,
𝐰 = weights
𝐱 = inputs
𝐛 = bias
𝐞 = Euler’s number (approx. 2.718)
𝟑 ⇨ 𝐖𝐡𝐲 𝐧𝐨𝐭 𝐋𝐢𝐧𝐞𝐚𝐫 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧?
Because Linear Regression predicts any number from -∞ to +∞, which doesn’t make sense for probability.
We need outputs between 0 and 1 and that’s where the sigmoid function helps.
𝟒 ⇨ 𝐋𝐨𝐬𝐬 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧 𝐮𝐬𝐞𝐝?
𝐁𝐢𝐧𝐚𝐫𝐲 𝐂𝐫𝐨𝐬𝐬-𝐄𝐧𝐭𝐫𝐨𝐩𝐲
ℒ = −(y log(p) + (1 − y) log(1 − p))
Where y is the actual value (0 or 1), and p is the predicted probability
𝟓 ⇨ 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐢𝐧 𝐫𝐞𝐚𝐥 𝐥𝐢𝐟𝐞:
𝐄𝐦𝐚𝐢𝐥 𝐒𝐩𝐚𝐦 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧
𝐃𝐢𝐬𝐞𝐚𝐬𝐞 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧
𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐂𝐡𝐮𝐫𝐧 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧
𝐂𝐥𝐢𝐜𝐤-𝐓𝐡𝐫𝐨𝐮𝐠𝐡 𝐑𝐚𝐭𝐞 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧
𝐁𝐢𝐧𝐚𝐫𝐲 𝐬𝐞𝐧𝐭𝐢𝐦𝐞𝐧𝐭 𝐜𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧
𝟔 ⇨ 𝐕𝐬. 𝐎𝐭𝐡𝐞𝐫 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐞𝐫𝐬
It’s fast, interpretable, and easy to implement, but it struggles with non-linearly separable data unlike Decision Trees or SVMs.
𝟕 ⇨ 𝐂𝐚𝐧 𝐢𝐭 𝐡𝐚𝐧𝐝𝐥𝐞 𝐦𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐜𝐥𝐚𝐬𝐬𝐞𝐬?
Yes, using One-vs-Rest (OvR) or Softmax in Multinomial Logistic Regression.
𝟖 ⇨ 𝐄𝐱𝐚𝐦𝐩𝐥𝐞 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧
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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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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Top 50 LLM Interview Questions!
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Data Science Machine Learning Data Analysis pinned «Intent | AI-Enhanced Telegram 🌐 Supports real-time translation in 86 languages 💬 Simply swipe up during chat to let AI automatically generate contextual replies 🎙 Instant AI enhanced voice-to-text conversion 🧠 Built-in mainstream models including GPT-4o, Claude…»
𝗦𝘆𝘀𝘁𝗲𝗺_𝗗𝗲𝘀𝗶𝗴𝗻_𝗥𝗼𝗮𝗱𝗺𝗮𝗽_𝗳𝗼𝗿_𝗠𝗔𝗔𝗡𝗚_&_𝗕𝗲𝘆𝗼𝗻𝗱.pdf
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𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝗳𝗼𝗿 𝗠𝗔𝗔𝗡𝗚 & 𝗕𝗲𝘆𝗼𝗻𝗱 🚀
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. It’s a must-have.
From Netflix, Amazon, Uber, YouTube, Reddit, Inc., to Twitter, these case studies and topic breakdowns will help you build real-world architectural thinking.
📌 Save this post. Spend 40 mins/day. Stay consistent.
➊ 𝗠𝘂𝘀𝘁-𝗞𝗻𝗼𝘄 𝗖𝗼𝗿𝗲 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀
👉 System Design Basics
🔗 https://bit.ly/3SuUR0Y)
👉 Horizontal & Vertical Scaling
🔗 https://bit.ly/3slq5xh)
👉 Load Balancing & Message Queues
🔗 https://bit.ly/3sp0FP4)
👉 HLD vs LLD, Hashing, Monolith vs Microservices
🔗 https://bit.ly/3DnEfEm)
👉 Caching, Indexing, Proxies
🔗 https://bit.ly/3SvyVDc)
👉 Networking, CDN, How Browsers Work
🔗 https://bit.ly/3TOHQRb
👉 DB Sharding, CAP Theorem, Schema Design
🔗 https://bit.ly/3CZtfLN
👉 Concurrency, OOP, API Layering
🔗 https://bit.ly/3sqQrhj
👉 Estimation, Performance Optimization
🔗 https://bit.ly/3z9dSPN
👉 MapReduce, Design Patterns
🔗 https://bit.ly/3zcsfmv
👉 SQL vs NoSQL, Cloud Architecture
🔗 https://bit.ly/3z8Aa49)
➋ 𝗠𝗼𝘀𝘁 𝗔𝘀𝗸𝗲𝗱 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀
🔗 https://bit.ly/3Dp40Ux
🔗 https://bit.ly/3E9oH7K
➌ 𝗖𝗮𝘀𝗲 𝗦𝘁𝘂𝗱𝘆 𝗗𝗲𝗲𝗽 𝗗𝗶𝘃𝗲𝘀 (𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗧𝗵𝗲𝘀𝗲!)
👉 Design Netflix
🔗 https://bit.ly/3GrAUG1
👉 Design Reddit
🔗 https://bit.ly/3OgGJrL
👉 Design Messenger
🔗 https://bit.ly/3DoAAXi
👉 Design Instagram
🔗 https://bit.ly/3BFeHlh
👉 Design Dropbox
🔗 https://bit.ly/3SnhncU
👉 Design YouTube
🔗 https://bit.ly/3dFyvvy
👉 Design Tinder
🔗 https://bit.ly/3Mcyj3X
👉 Design Yelp
🔗 https://bit.ly/3E7IgO5
👉 Design WhatsApp
🔗 https://bit.ly/3M2GOhP
👉 Design URL Shortener
🔗 https://bit.ly/3xP078x
👉 Design Amazon Prime Video
🔗https://bit.ly/3hVpWP4
👉 Design Twitter
🔗 https://bit.ly/3qIG9Ih
👉 Design Uber
🔗 https://bit.ly/3fyvnlT
👉 Design TikTok
🔗 https://bit.ly/3UUlKxP
👉 Design Facebook Newsfeed
🔗 https://bit.ly/3RldaW7
👉 Design Web Crawler
🔗 https://bit.ly/3DPZTBB
👉 Design API Rate Limiter
🔗 https://bit.ly/3BIVuh7
➍ 𝗙𝗶𝗻𝗮𝗹 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀
👉 All Solved Case Studies
🔗 https://bit.ly/3dCG1rc
👉 Design Terms & Terminology
🔗 https://bit.ly/3Om9d3H
👉 Complete Basics Series
🔗https://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. It’s a must-have.
From Netflix, Amazon, Uber, YouTube, Reddit, Inc., to Twitter, these case studies and topic breakdowns will help you build real-world architectural thinking.
📌 Save this post. Spend 40 mins/day. Stay consistent.
➊ 𝗠𝘂𝘀𝘁-𝗞𝗻𝗼𝘄 𝗖𝗼𝗿𝗲 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀
👉 System Design Basics
🔗 https://bit.ly/3SuUR0Y)
👉 Horizontal & Vertical Scaling
🔗 https://bit.ly/3slq5xh)
👉 Load Balancing & Message Queues
🔗 https://bit.ly/3sp0FP4)
👉 HLD vs LLD, Hashing, Monolith vs Microservices
🔗 https://bit.ly/3DnEfEm)
👉 Caching, Indexing, Proxies
🔗 https://bit.ly/3SvyVDc)
👉 Networking, CDN, How Browsers Work
🔗 https://bit.ly/3TOHQRb
👉 DB Sharding, CAP Theorem, Schema Design
🔗 https://bit.ly/3CZtfLN
👉 Concurrency, OOP, API Layering
🔗 https://bit.ly/3sqQrhj
👉 Estimation, Performance Optimization
🔗 https://bit.ly/3z9dSPN
👉 MapReduce, Design Patterns
🔗 https://bit.ly/3zcsfmv
👉 SQL vs NoSQL, Cloud Architecture
🔗 https://bit.ly/3z8Aa49)
➋ 𝗠𝗼𝘀𝘁 𝗔𝘀𝗸𝗲𝗱 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀
🔗 https://bit.ly/3Dp40Ux
🔗 https://bit.ly/3E9oH7K
➌ 𝗖𝗮𝘀𝗲 𝗦𝘁𝘂𝗱𝘆 𝗗𝗲𝗲𝗽 𝗗𝗶𝘃𝗲𝘀 (𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗧𝗵𝗲𝘀𝗲!)
👉 Design Netflix
🔗 https://bit.ly/3GrAUG1
👉 Design Reddit
🔗 https://bit.ly/3OgGJrL
👉 Design Messenger
🔗 https://bit.ly/3DoAAXi
👉 Design Instagram
🔗 https://bit.ly/3BFeHlh
👉 Design Dropbox
🔗 https://bit.ly/3SnhncU
👉 Design YouTube
🔗 https://bit.ly/3dFyvvy
👉 Design Tinder
🔗 https://bit.ly/3Mcyj3X
👉 Design Yelp
🔗 https://bit.ly/3E7IgO5
👉 Design WhatsApp
🔗 https://bit.ly/3M2GOhP
👉 Design URL Shortener
🔗 https://bit.ly/3xP078x
👉 Design Amazon Prime Video
🔗https://bit.ly/3hVpWP4
👉 Design Twitter
🔗 https://bit.ly/3qIG9Ih
👉 Design Uber
🔗 https://bit.ly/3fyvnlT
👉 Design TikTok
🔗 https://bit.ly/3UUlKxP
👉 Design Facebook Newsfeed
🔗 https://bit.ly/3RldaW7
👉 Design Web Crawler
🔗 https://bit.ly/3DPZTBB
👉 Design API Rate Limiter
🔗 https://bit.ly/3BIVuh7
➍ 𝗙𝗶𝗻𝗮𝗹 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀
👉 All Solved Case Studies
🔗 https://bit.ly/3dCG1rc
👉 Design Terms & Terminology
🔗 https://bit.ly/3Om9d3H
👉 Complete Basics Series
🔗https://bit.ly/3rG1cfr
#SystemDesign #TechInterviews #MAANGPrep #BackendEngineering #ScalableSystems #HLD #LLD #SoftwareArchitecture #DesignCaseStudies #CloudArchitecture #DataEngineering #DesignPatterns #LoadBalancing #Microservices #DistributedSystems
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