Projected change in population for every European country between 2025 and 2100.
๐ช๐บ Europe: -152.2M (-20%)
๐บ๐ฆ Ukraine: -23.8M
๐ฎ๐น Italy: -23.8M
๐ต๐ฑ Poland: -18.8M
๐ท๐บ Russia: -17.6M
๐ช๐ธ Spain: -14.8M
๐ฉ๐ช Germany: -13.1M
๐ท๐ด Romania: -8.1M
๐ง๐พ Belarus: -4.6M
๐ฌ๐ท Greece: -3.7M
๐ง๐ฌ Bulgaria: -3.2M
๐ท๐ธ Serbia: -3.0M
๐จ๐ฟ Czechia: -2.4M
๐ญ๐บ Hungary: -2.2M
๐ธ๐ฐ Slovakia: -2.1M
๐ง๐ฆ Bosnia & Herzegovina: -1.8M
๐ฆ๐น Austria: -1.7M
๐ญ๐ท Croatia: -1.7M
๐ต๐น Portugal: -1.7M
๐ฑ๐น Lithuania: -1.6M
๐ฆ๐ฑ Albania: -1.6M
๐ฒ๐ฉ Moldova: -1.5M
๐ซ๐ฎ Finland: -1.0M
๐ฒ๐ฐ North Macedonia: -950.8K
๐ฑ๐ป Latvia: -928.2K
๐ณ๐ฑ Netherlands: -839.3K
๐ง๐ช Belgium: -697.8K
๐ฝ๐ฐ Kosovo: -579.4K
๐ช๐ช Estonia: -518.7K
๐ธ๐ฎ Slovenia: -485.0K
๐ฒ๐ช Montenegro: -306.7K
๐ณ๐ด Norway: -209.5K
๐ฒ๐น Malta: -185.5K
๐ฉ๐ฐ Denmark: -139.3K
๐ฎ๐ธ Iceland: -35.7K
๐ฆ๐ฉ Andorra: -35.7K
๐ฎ๐ช Ireland: -21.9K
๐ธ๐ฒ San Marino: -2.4K
๐ฒ๐จ Monaco: +9.1K
๐ฑ๐ฎ Liechtenstein: +3.5K
๐ฑ๐บ Luxembourg: +67.5K
๐จ๐ญ Switzerland: +158.7K
๐ธ๐ช Sweden: +710.3K
๐ซ๐ท France: +1.8M
๐ฌ๐ง UK: +4.8M
Data is sourced from the UN World Population Prospects 2024.
๐ช๐บ Europe: -152.2M (-20%)
๐บ๐ฆ Ukraine: -23.8M
๐ฎ๐น Italy: -23.8M
๐ต๐ฑ Poland: -18.8M
๐ท๐บ Russia: -17.6M
๐ช๐ธ Spain: -14.8M
๐ฉ๐ช Germany: -13.1M
๐ท๐ด Romania: -8.1M
๐ง๐พ Belarus: -4.6M
๐ฌ๐ท Greece: -3.7M
๐ง๐ฌ Bulgaria: -3.2M
๐ท๐ธ Serbia: -3.0M
๐จ๐ฟ Czechia: -2.4M
๐ญ๐บ Hungary: -2.2M
๐ธ๐ฐ Slovakia: -2.1M
๐ง๐ฆ Bosnia & Herzegovina: -1.8M
๐ฆ๐น Austria: -1.7M
๐ญ๐ท Croatia: -1.7M
๐ต๐น Portugal: -1.7M
๐ฑ๐น Lithuania: -1.6M
๐ฆ๐ฑ Albania: -1.6M
๐ฒ๐ฉ Moldova: -1.5M
๐ซ๐ฎ Finland: -1.0M
๐ฒ๐ฐ North Macedonia: -950.8K
๐ฑ๐ป Latvia: -928.2K
๐ณ๐ฑ Netherlands: -839.3K
๐ง๐ช Belgium: -697.8K
๐ฝ๐ฐ Kosovo: -579.4K
๐ช๐ช Estonia: -518.7K
๐ธ๐ฎ Slovenia: -485.0K
๐ฒ๐ช Montenegro: -306.7K
๐ณ๐ด Norway: -209.5K
๐ฒ๐น Malta: -185.5K
๐ฉ๐ฐ Denmark: -139.3K
๐ฎ๐ธ Iceland: -35.7K
๐ฆ๐ฉ Andorra: -35.7K
๐ฎ๐ช Ireland: -21.9K
๐ธ๐ฒ San Marino: -2.4K
๐ฒ๐จ Monaco: +9.1K
๐ฑ๐ฎ Liechtenstein: +3.5K
๐ฑ๐บ Luxembourg: +67.5K
๐จ๐ญ Switzerland: +158.7K
๐ธ๐ช Sweden: +710.3K
๐ซ๐ท France: +1.8M
๐ฌ๐ง UK: +4.8M
Data is sourced from the UN World Population Prospects 2024.
๐1
Finally Europe begins the understand that AI is more than just a trend - its a technological revolution and necessity if we want to stay on top as a nation.
The only two questions are:
- Are we too late or do we still have a chance to catch up?
- And also how exactly we want to participate. A separate frontier model will probably be unnecessary. AI infrastructure and hyperscalers in the EU, on the other hand, will be necessary
The only two questions are:
- Are we too late or do we still have a chance to catch up?
- And also how exactly we want to participate. A separate frontier model will probably be unnecessary. AI infrastructure and hyperscalers in the EU, on the other hand, will be necessary
๐2
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To truly master Artificial Intelligence, focus on these key areas:
0. Understanding AI Fundamentals: Learn the basic concepts of AI, including search algorithms, knowledge representation, and decision trees.
1. Mastering Machine Learning: Since ML is a core part of AI, dive into supervised, unsupervised, and reinforcement learning techniques.
2. Exploring Deep Learning: Learn neural networks, CNNs, RNNs, and GANs to handle tasks like image recognition, NLP, and generative models.
3. Working with Natural Language Processing (NLP): Understand how machines process human language for tasks like sentiment analysis, translation, and chatbots.
4. Learning Reinforcement Learning: Study how agents learn by interacting with environments to maximize rewards (e.g., in gaming or robotics).
5. Building AI Models: Use popular frameworks like TensorFlow, PyTorch, and Keras to build, train, and evaluate your AI models.
6. Ethics and Bias in AI: Understand the ethical considerations and challenges of implementing AI responsibly, including fairness, transparency, and bias.
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Like if you need similar content ๐๐
Hope this helps you ๐
#ai #datascience
Itโs the cutting-edge field that enables machines to think, learn, and act like humans.
To truly master Artificial Intelligence, focus on these key areas:
0. Understanding AI Fundamentals: Learn the basic concepts of AI, including search algorithms, knowledge representation, and decision trees.
1. Mastering Machine Learning: Since ML is a core part of AI, dive into supervised, unsupervised, and reinforcement learning techniques.
2. Exploring Deep Learning: Learn neural networks, CNNs, RNNs, and GANs to handle tasks like image recognition, NLP, and generative models.
3. Working with Natural Language Processing (NLP): Understand how machines process human language for tasks like sentiment analysis, translation, and chatbots.
4. Learning Reinforcement Learning: Study how agents learn by interacting with environments to maximize rewards (e.g., in gaming or robotics).
5. Building AI Models: Use popular frameworks like TensorFlow, PyTorch, and Keras to build, train, and evaluate your AI models.
6. Ethics and Bias in AI: Understand the ethical considerations and challenges of implementing AI responsibly, including fairness, transparency, and bias.
7. Computer Vision: Master image processing techniques, object detection, and recognition algorithms for AI-powered visual applications.
8. AI for Robotics: Learn how AI helps robots navigate, sense, and interact with the physical world.
9. Staying Updated with AI Research: AI is an ever-evolving fieldโstay on top of cutting-edge advancements, papers, and new algorithms.
Artificial Intelligence is a multidisciplinary field that blends computer science, mathematics, and creativity.
๐ก Embrace the journey of learning and building systems that can reason, understand, and adapt.
โณ With dedication, hands-on practice, and continuous learning, youโll contribute to shaping the future of intelligent systems!
Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.me/datasciencefun
Like if you need similar content ๐๐
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#ai #datascience
๐2
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Cloud Services Cheatsheet โ๏ธ
โค5๐2
Natural Language Processing Projects.pdf
13.2 MB
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871.9 KB
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A brief introduction to object oriented programming OOP in JavaScript programming language in a practical way with simple examples
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