Forwarded from Jobs and Internships India
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Forwarded from GROUP FOR PROGRAMMERS🖥
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Forwarded from GROUP FOR PROGRAMMERS🖥
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Forwarded from GROUP FOR PROGRAMMERS🖥
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Forwarded from GROUP FOR PROGRAMMERS🖥
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Forwarded from GROUP FOR PROGRAMMERS🖥
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Forwarded from GROUP FOR PROGRAMMERS🖥
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Forwarded from GROUP FOR PROGRAMMERS🖥
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Forwarded from GROUP FOR PROGRAMMERS🖥
𝗧𝗵𝗼𝘀𝗲 𝘄𝗵𝗼 𝘄𝗮𝗻𝘁 𝗿𝗲𝗳𝗲𝗿𝗿𝗮𝗹𝘀 𝗮𝗻𝗱 𝗝𝗼𝗯𝘀 𝗮𝗻𝗱 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽𝘀 𝗼𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝗶𝗲𝘀 𝗳𝗿𝗼𝗺 𝗧𝗼𝗽 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗕𝗮𝘀𝗲𝗱, 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 𝗕𝗮𝘀𝗲𝗱 𝗮𝗻𝗱 𝗦𝘁𝗮𝗿𝘁 𝘂𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗹𝗶𝗸𝗲 𝗔𝗺𝗮𝘇𝗼𝗻, 𝗚𝗼𝗼𝗴𝗹𝗲, 𝗔𝗽𝗽𝗹𝗲, 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁, 𝗜𝗕𝗠, 𝗧𝗖𝗦, 𝗖𝗼𝗴𝗻𝗶𝘇𝗮𝗻𝘁, 𝗪𝗶𝗽𝗿𝗼, 𝗖𝗧𝗦, 𝗚𝗼𝗹𝗱𝗺𝗮𝗻 𝗦𝗮𝗰𝗵𝘀, 𝗢𝗹𝗮, 𝗨𝗯𝗲𝗿, 𝗭𝗼𝗺𝗮𝘁𝗼, 𝗦𝘄𝗶𝗴𝗴𝘆, 𝘂𝗽𝗚𝗿𝗮𝗱, 𝗖𝘂𝗿𝗲 𝗙𝗶𝘁, 𝗛𝗮𝗰𝗸𝗲𝗿𝗿𝗮𝗻𝗸, 𝗚𝗲𝗲𝗸𝘀𝗳𝗼𝗿𝗴𝗲𝗲𝗸𝘀 𝗮𝗻𝗱 𝗺𝗮𝗻𝘆 𝗺𝗼𝗿𝗲, 𝗰𝗮𝗻 𝗷𝗼𝗶𝗻 𝘁𝗵𝗲 𝗯𝗲𝗹𝗼𝘄 network.
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𝗟𝗮𝘁𝗲𝘀𝘁 𝗝𝗼𝗯𝘀 𝗮𝗻𝗱 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽𝘀 𝗨𝗽𝗱𝗮𝘁𝗲𝘀 𝗳𝗼𝗿 𝟮𝟬𝟭𝟳, 𝟮𝟬𝟭𝟴, 𝟮𝟬𝟭𝟵, 𝟮𝟬𝟮𝟬, 𝟮𝟬𝟮𝟭, 𝟮𝟬𝟮𝟮, 𝟮𝟬𝟮𝟯, 𝟮𝟬𝟮𝟰, 𝟮𝟬𝟮𝟱, 𝟮𝟬𝟮𝟲, 𝟮𝟬𝟮𝟳, 𝟮𝟬𝟮𝟴 𝗮𝗻𝗱 𝟮𝟬𝟮𝟵 𝗕𝗮𝘁𝗰𝗵.
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𝗟𝗮𝘁𝗲𝘀𝘁 𝗝𝗼𝗯𝘀 𝗮𝗻𝗱 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽𝘀 𝗨𝗽𝗱𝗮𝘁𝗲𝘀 𝗳𝗼𝗿 𝟮𝟬𝟭𝟳, 𝟮𝟬𝟭𝟴, 𝟮𝟬𝟭𝟵, 𝟮𝟬𝟮𝟬, 𝟮𝟬𝟮𝟭, 𝟮𝟬𝟮𝟮, 𝟮𝟬𝟮𝟯, 𝟮𝟬𝟮𝟰, 𝟮𝟬𝟮𝟱, 𝟮𝟬𝟮𝟲, 𝟮𝟬𝟮𝟳, 𝟮𝟬𝟮𝟴 𝗮𝗻𝗱 𝟮𝟬𝟮𝟵 𝗕𝗮𝘁𝗰𝗵.
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Forwarded from GROUP FOR PROGRAMMERS🖥
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Combining Plots in Matplotlib 📊
In Matplotlib, you can easily combine multiple plots in a single window using the subplot() function. Simply create the necessary plots, specify their layout, add titles, and you'll get a clear visualization for easy data comparison.
#Matplotlib #DataVisualization #Python #DataScience #Coding #Plotting
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In Matplotlib, you can easily combine multiple plots in a single window using the subplot() function. Simply create the necessary plots, specify their layout, add titles, and you'll get a clear visualization for easy data comparison.
#Matplotlib #DataVisualization #Python #DataScience #Coding #Plotting
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🚶𝗛𝘂𝗿𝗿𝘆 𝗨𝗽, 𝗳𝗲𝘄 𝘀𝗲𝗮𝘁𝘀 𝗹𝗲𝗳𝘁!
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🔍 5 resources most ML people never stumble on
These are the things practitioners quietly rely on but rarely share.
1. Google's "Rules of Machine Learning"
43 numbered rules from Google engineers on when to add complexity, how to catch training/serving skew, and when a heuristic beats a model. Written from real production postmortems.
2. Chip Huyen's ML Systems Design notes
Free breakdown of how companies actually design ML systems: data pipelines, feature stores, serving latency, model monitoring. The stuff no ML course teaches.
3. Full Stack Deep Learning
Free course built on one premise: training the model is the easy 20%. Covers deployment, cost tradeoffs, data labeling, and how models fail in production.
4. alphaXiv
Same papers as arXiv, but with inline comment threads under each section, sometimes answered by the paper's own authors. Turns a static PDF into an ongoing discussion.
5. Sebastian Raschka's "Ahead of AI"
Newsletter that dissects specific architecture and training decisions (why this optimizer, why this attention variant) at a depth most blogs skip.
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4️⃣ Deep Learning
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[ https://t.me/deeplearningofficial ]
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These are the things practitioners quietly rely on but rarely share.
1. Google's "Rules of Machine Learning"
43 numbered rules from Google engineers on when to add complexity, how to catch training/serving skew, and when a heuristic beats a model. Written from real production postmortems.
2. Chip Huyen's ML Systems Design notes
Free breakdown of how companies actually design ML systems: data pipelines, feature stores, serving latency, model monitoring. The stuff no ML course teaches.
3. Full Stack Deep Learning
Free course built on one premise: training the model is the easy 20%. Covers deployment, cost tradeoffs, data labeling, and how models fail in production.
4. alphaXiv
Same papers as arXiv, but with inline comment threads under each section, sometimes answered by the paper's own authors. Turns a static PDF into an ongoing discussion.
5. Sebastian Raschka's "Ahead of AI"
Newsletter that dissects specific architecture and training decisions (why this optimizer, why this attention variant) at a depth most blogs skip.
Claim your Free $5 Bonus Here:
https://bit.ly/3wUxw09
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1️⃣ Big Data
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2️⃣ Machine Learning
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[ https://t.me/machinelearning_official ]
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3️⃣ Cloud Computing
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---
4️⃣ Deep Learning
📎 Channel Link:
[ https://t.me/deeplearningofficial ]
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All the best 👍👍