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 DeepSeek: The True Open AI Champion The AI landscape is abuzz with talk of massive investments. Microsoft poured billions into OpenAI ChatGPT, Google funds Gemini, Meta backs Llama, Amazon keeps Anthropic alive and now the US government is promoting the Stargate Project with a proposed $500 billion. But while these giants grapple for AI dominance with their wallets, DeepSeek is quietly proving that true innovation thrives on talent, efficiency, and an open-source ethos. DeepSeek, a rising star from China, has shocked the industry by developing cutting-edge AI models that rival the best, all with a fraction of the resources.In a matter of months, and with less than $6 million, they've achieved what others have spent billions striving for. Their secret weapon? Open source. The Power of Open Source DeepSeek embraces the collaborative spirit of open source, fostering a community were ideas and code flow freely. This approach allows them to: Attract Top Talent: Passionate developers fl...

 

 

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 Real AI technologies are unfolding a global technology revolution that could affect the well-being and future of all and everybody, people everywhere. True AI has the potential to transform the advancement of humankind on a scale not seen before. We need to ensure AI technologies are developed responsibly as completing not competing with the human mind and used as a force for good, helping to make people around the world safer, more secure and more prosperous. The question of all questions, the problem of all problems is to protect ourselves from the dangers of the human mind faking AI tools promoted by technological oligopolies and big powers. For today's AI and its machine learning is all about the imitation of human intelligence by machines or computer systems, being really non-AI machine-based systems. By the very definition and design, such human mind-replicating AI systems pose risks to privacy, safety, security, human autonomy and existence, being nothing but human mind att...

 THE 48 LAWS OF POWER A Book written by Robert Greene that offers a Series of Strategies for Obtaining and Maintaining Power in various situations. Here I leave you a summary of the 48 Laws: 1. Don't Outshine the Boss: Make your Superiors feel Superior. Don't expose your Talent too much or you might Trigger their Insecurity. 2.Don't Trust friends too much, use your Enemies: Friends Betray you more easily, but if you Manage to WIN an Enemy, they will be more Loyal. 3. Hide your Intentions: Keep People Off Balance so they can't anticipate your Actions. 4. Always say Less than Necessary: Silence Breeds Power, and Talking too much Reveals your Plans. 5. Protect your Reputation at all Costs: Reputation is the Cornerstone of Power. 6. Call Attention at all Costs: Be Visible to be Relevant. 7. Make others Work for you and Attribute it: Take Advantage of the Work and Effort of others to your Advantage. 8. Make others come to you: Don't Run after Others, make them Look for y...

 

 

 

 

 

 

 Is this the end of LoRA as a fine-tuning approach? Singular Value fine-tuning is here! We have a new paper in town called the "Transformers Squared". It promises to adapt any LLM to any task without external intervention.  The core idea of the paper is to use Singular Value Decomposition (SVD) to factorize the weight matrices of transformers. During training, we learn different singular values for different tasks. More specifically, we learn to scale the singular values for different tasks. Tasks can be as diverse as math reasoning or coding. The possibilities are endless. During inference, we do a two-pass inference. In the first pass, the LLM decides which "scale" to use for which task. In the second pass, the LLM adapts itself to be a specialist in the task and responds to our prompt. How cool is that? Paper Title: Transformer2: Self-adaptive LLMs Paper: https://sakana.ai/transformer-squared Blog: https://arxiv.org/abs/2501.06252 Video Explanation: https://youtu...

 

 𝐏𝐨𝐰𝐞𝐫 𝐨𝐟 𝐇𝐢𝐞𝐫𝐚𝐫𝐜𝐡𝐢𝐜��𝐥 𝐂𝐥𝐮𝐬𝐭𝐞𝐫𝐢𝐧𝐠: 𝐀 𝐆𝐮𝐢𝐝𝐞 𝐭𝐨 𝐃𝐚𝐭𝐚 𝐆𝐫𝐨𝐮𝐩𝐢𝐧𝐠  In the world of Data Science, Hierarchical Clustering stands out for its elegance and versatility. This powerful method helps group similar data points, uncover hidden patterns, and explore relationships within datasets. 🌐   🔑 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐇𝐢𝐞𝐫𝐚𝐫𝐜𝐡𝐢𝐜𝐚𝐥 𝐂𝐥𝐮𝐬𝐭𝐞𝐫𝐢𝐧𝐠? Hierarchical Clustering is an unsupervised learning technique that builds a tree of clusters, called a dendrogram, by progressively merging smaller clusters into larger ones. Here's how it works:    𝐒𝐭𝐚𝐫𝐭 𝐰𝐢𝐭𝐡 𝐈𝐧𝐝𝐢𝐯𝐢𝐝𝐮𝐚𝐥 𝐃𝐚𝐭𝐚 𝐏𝐨𝐢𝐧𝐭𝐬: Initially, each data point is treated as its own cluster.    𝐌𝐞𝐚𝐬𝐮𝐫𝐞 𝐃𝐢𝐬𝐭𝐚𝐧𝐜𝐞𝐬: The distance between clusters is calculated using a defined metric.    𝐌𝐞𝐫𝐠𝐞 𝐭𝐡𝐞 𝐂𝐥𝐨𝐬𝐞𝐬𝐭 𝐂𝐥𝐮𝐬𝐭𝐞𝐫𝐬: The closest clusters are merged, repeating until all points belong to a sin...

 🔍📊 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 💡📈 Data science uses a variety of powerful techniques to turn raw data into actionable insights. Here's a simplified overview: 𝟏. 𝐃𝐚𝐭𝐚 𝐂𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐨𝐧 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬: ・Web Scraping: Extract data from websites. ・Data Mining: Uncover patterns from large datasets. ・Surveys: Collect data through questionnaires. ・APIs: Access data programmatically. ・Data Acquisition: Gather data from various sources. 𝟐. 𝐃𝐚𝐭𝐚 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬: ・Missing Data Imputation: Fill in missing values. ・Outlier Detection & Treatment: Identify and address anomalies. ・Categorical Encoding: Convert categories into numeric values. ・Feature Scaling: Normalize data for consistency. 𝟑. 𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬: ・Bar Charts: Compare categorical data. ・Histograms: Display data distribution. ・Scatter Plots: Show relationships between variables. ・Heatmaps: Visualize data intensity. ・Box Plots: S...

 FireDucks makes Pandas 125x Faster (changing one line of code) 🧠 Pandas has some major limitations: - Pandas only uses a single CPU core. - It often creates memory-heavy DataFrames. - Its eager (immediate) execution prevents global optimization of operation sequences. FireDucks is a highly optimized, drop-in replacement for Pandas with the same API.  There are three ways to use it: 1) Load the extension:  ↳ %𝐥𝐨𝐚𝐝_𝐞𝐱𝐭 𝗳𝗶𝗿𝗲𝗱𝘂𝗰𝗸𝘀.𝐩𝐚𝐧𝐝𝐚𝐬; 𝗶𝗺𝗽𝗼𝗿𝘁 𝗽𝗮𝗻𝗱𝗮𝘀 𝗮𝘀 𝗽𝗱 2) Import FireDucks instead of Pandas:  ↳ 𝐢𝐦𝐩𝐨𝐫𝐭 𝗳𝗶𝗿𝗲𝗱𝘂𝗰𝗸𝘀.𝐩𝐚𝐧𝐝𝐚𝐬 𝐚𝐬 𝐩𝐝 3) If you have a Python script, execute is as follows:  ↳ 𝗽𝘆𝘁𝗵𝗼𝗻3 -𝗺 𝗳𝗶𝗿𝗲𝗱𝘂𝗰𝗸𝘀.𝗽𝗮𝗻𝗱𝗮𝘀 𝗰𝗼𝗱𝗲.𝗽𝘆 Done! ✔️ A performance comparison of FireDucks vs. DuckDB, Polars, and Pandas is shown in the video below. Official benchmarks indicate: ↳ Modin: ~1.0x faster than Pandas ↳ Polars: ~57x faster than Pandas ↳ FireDucks: ~125x faster than Pandas Credit- Ultan...

 Top 10 GitHub Repositories to Ace Your Next Analytics Interview These repositories offer an extensive u range of resources, tutorials, and projects to help you excel in data science and analytics interviews: 1. Machine Learning Interview - 9.1k Stars Link: https://lnkd.in/g68_2wR7 2. 500+ AI Projects List with Code - 20.2k Stars Link: https://lnkd.in/g2wwkU6c 3. 100 Days of ML Code - 45.2k Stars Link: https://lnkd.in/ggu4zHp3 4. Awesome Data Science - 25k Stars Link: https://lnkd.in/gnvvpZjj 5. Data Science For Beginners - 28.1k Stars Link: https://lnkd.in/gJacHejc 6. Data Science Masters - 24.9k Stars Link: https://lnkd.in/gXbY6R6C 7. Awesome Artificial Intelligence - 10.8k Stars Link: https://lnkd.in/gwjPBXkq 8. Homemade Machine Learning - 23k Stars Link: https://lnkd.in/giM26Ak2 9. Data Science Interviews - 8.9k Stars Link: https://lnkd.in/gEPM9TYg 10. Data Science Best Resources - 2.9k Stars Link: https://lnkd.in/g8Q6ammy Follow Data Spoof  to learn Python programming, da...