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#Datast Reel by @darylanselmo - 250215 - 🫓 hummu5 (47 of *89) . trained a model on hummus, applied it to characters in the food trauma world . part of a series focused on fine-tunin
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@darylanselmo
250215 - 🫓 hummu5 (47 of *89) . trained a model on hummus, applied it to characters in the food trauma world . part of a series focused on fine-tuning and dataset prep . python, joycaption (via huggingface), kohya, flux-dev, comfyUI, kling, Udio, premiere pro, topaz video ai . #flux #comfyui #ai #aivideo #weird #surreal #weirdart #finetune #unsettling #dark #hummus #pita #foodtrauma
#Datast Reel by @askdatadawn (verified account) - Let's clean a dataset together in Python in 2 minutes ✌🏽 #dataanalytics #datascience #python #datacleaning
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@askdatadawn
Let’s clean a dataset together in Python in 2 minutes ✌🏽 #dataanalytics #datascience #python #datacleaning
#Datast Reel by @doubleemt (verified account) - scale AI and Mercor could never #ai #webscraping #dataset #aivideo #rag
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@doubleemt
scale AI and Mercor could never #ai #webscraping #dataset #aivideo #rag
#Datast Reel by @aifolksorg (verified account) - 🎯 68.5% - That's how much of the world's AI compute power the U.S. controls. This means America can train bigger, faster, and smarter AI models, yea
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@aifolksorg
🎯 68.5% — That’s how much of the world’s AI compute power the U.S. controls. This means America can train bigger, faster, and smarter AI models, years ahead of most countries. For nations starting now, catching up isn’t just about buying chips, it’s building massive data centers, securing huge power supplies, and overcoming export restrictions. By the time they scale, the U.S. will have already moved to the next frontier. 🚀 Start your AI learning journey at @aifolksorg 😇 [AI compute power, global AI dominance, AI infrastructure, AI chips, AI models, AI supercomputers, AI leadership, AI trends 2025, artificial intelligence future, aifolks.org]
#Datast Reel by @maggieindata (verified account) - Let me know which industry you want to work in the most! 👇👇 The most voted industry in the comment section below will be the basis of my next projec
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@maggieindata
Let me know which industry you want to work in the most! 👇👇 The most voted industry in the comment section below will be the basis of my next project :)
#Datast Reel by @the.datascience.gal (verified account) - Want to become a Machine Learning Engineer in 2025? Build real projects that reflect how ML is done in the industry: 1 → End-to-End ML Pipeline Predi
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@the.datascience.gal
Want to become a Machine Learning Engineer in 2025? Build real projects that reflect how ML is done in the industry: 1 → End-to-End ML Pipeline Predict something useful (like student dropout risk). Clean with Pandas, train with LightGBM, deploy with FastAPI + Docker + AWS. 2 → RAG Chatbot Build a chatbot that answers from your course notes. Use LlamaIndex + FAISS + Llama 3.1. This is how GenAI apps work today. 3 → Fine-Tune LLMs Take an open-source LLM and fine-tune it on your own dataset. Use QLoRA with PEFT. Example: medical Q&A bot. 4 → Model Monitoring Build a fraud detection model and track drift post-deployment using Evidently AI + Weights & Biases. Shows you think beyond training. 5 → Multimodal AI App Photo → nutrition info + recipe. Use CLIP or Florence-2 for vision-text, connect to LLaVA or Qwen-VL, deploy with Streamlit. This stack hits every part of the ML lifecycle—from classic ML to GenAI to production monitoring. [mlprojects, machinelearningengineer, genai, fine-tuning, ragchatbot, mlportfolio, endtoendpipeline, multimodalai, ai2025, llmengineer, mljobs, mlworkflow, productionai]
#Datast Reel by @jayenthakker - If you're a data analytics aspirant, you've probably faced this frustrating loop. You've learned SQL. You know the syntax. But when it comes to real
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@jayenthakker
If you’re a data analytics aspirant, you’ve probably faced this frustrating loop. You’ve learned SQL. You know the syntax. But when it comes to real-world projects, you feel lost. ⤷ 𝘞𝘩𝘢𝘵 𝘥𝘢𝘵𝘢𝘴𝘦𝘵𝘴 𝘴𝘩𝘰𝘶𝘭𝘥 𝘺𝘰𝘶 𝘶𝘴𝘦? ⁣ ⤷ 𝘞𝘩𝘢𝘵 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴 𝘱𝘳𝘰𝘣𝘭𝘦𝘮𝘴 𝘴𝘩𝘰𝘶𝘭𝘥 𝘺𝘰𝘶 𝘴𝘰𝘭𝘷𝘦?⁣ ⤷ 𝘏𝘰𝘸 𝘥𝘰 𝘺𝘰𝘶 𝘱𝘳𝘰𝘷𝘦 𝘺𝘰𝘶𝘳 𝘴𝘬𝘪𝘭𝘭𝘴 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 𝘢 𝘫𝘰𝘣? The answer: 𝐁𝐮𝐢𝐥𝐝 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬 𝐭𝐡𝐚𝐭 𝐬𝐡𝐨𝐰𝐜𝐚𝐬𝐞 𝐲𝐨𝐮𝐫 𝐒𝐐𝐋 𝐞𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞. Here are 5 SQL projects that mimic real-world business problems. 𝟏. 𝐒𝐚𝐥𝐞𝐬 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 Goal: Analyze sales trends, customer purchase behavior, and revenue growth. ⤷ Dataset: E-commerce sales transactions ⤷ https://lnkd.in/dFeUvA7B 𝟐. 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐒𝐞𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐮𝐬𝐢𝐧𝐠 𝐑𝐅𝐌 Goal: Classify customers into segments based on Recency, Frequency, and Monetary value. ⤷ Dataset: Retail customer transactions ⤷ https://lnkd.in/dk5XryjD 𝟑. 𝐄𝐦𝐩𝐥𝐨𝐲𝐞𝐞 𝐀𝐭𝐭𝐫𝐢𝐭𝐢𝐨𝐧 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 Goal: Identify key reasons for employee churn using SQL analytics. ⤷ Dataset: HR Employee Data ⤷ https://lnkd.in/dneHKFzg 𝟒. 𝐅𝐫𝐚𝐮𝐝 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐢𝐧 𝐓𝐫𝐚𝐧𝐬𝐚𝐜𝐭𝐢𝐨𝐧𝐬 Goal: Identify suspicious financial transactions. ⤷ Dataset: Banking transactions ⤷ https://lnkd.in/dv5RkWTE 𝟓. 𝐌𝐨𝐯𝐢𝐞 𝐑𝐚𝐭𝐢𝐧𝐠𝐬 & 𝐔𝐬𝐞𝐫 𝐁𝐞𝐡𝐚𝐯𝐢𝐨𝐫 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 Goal: Analyze user preferences and movie popularity. ⤷ Dataset: IMDB or Netflix user ratings ⤷ https://lnkd.in/dX3Sq2Ex Which project are you excited to start first? -- Follow @jayenthakker and @metricminds.in ➕ Dedicated to helping aspiring data analysts thrive in their careers. -- #datavisualization #dataanalyst #datascience #sql #data #ai #python #metricminds #trending #excel #foryoupage #careerswitch #career #india #LearnWithMe #trainhard
#Datast Reel by @garshitpost - orang nanya Membuat AI khusus untuk meme itu seperti mencoba membuat robot yang punya selera humor lebih tajam dari kebanyakan orang di pesta. Jadi,
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@garshitpost
orang nanya Membuat AI khusus untuk meme itu seperti mencoba membuat robot yang punya selera humor lebih tajam dari kebanyakan orang di pesta. Jadi, berikut adalah langkah-langkah untuk menciptakan AI meme yang lucu: Mulai dengan "Pelatihan Meme" Ajarkan AI tentang semua jenis meme yang ada, mulai dari meme yang cuma bisa bikin kamu geleng-geleng kepala sampai yang bikin perut sakit karena ketawa. Misalnya, ajarkan dia tentang meme seperti "Distracted Boyfriend", "Mocking SpongeBob", atau meme "Grumpy Cat". Pahami Pola Humor AI perlu memahami ironi, sarkasme, dan absurditas. Misalnya, dia harus tahu kapan harus membuat meme yang terkesan sangat serius tapi sebenarnya enggak serius sama sekali, atau kapan harus menggunakan teks yang bikin orang bingung—dan itu lucu. Gunakan Dataset Meme Ambil ribuan contoh meme dari internet (pastikan semuanya legal, jangan ambil yang ada hak ciptanya, ya!) dan beri mereka label yang tepat. Misalnya, "Meme Lucu", "Meme Gelap", atau "Meme yang Harusnya Enggak Lucu tapi Ternyata Lucu". Pelajari Wajah dan Ekspresi Ajarkan AI mengenali ekspresi wajah yang bisa bikin meme lebih hidup. Ini penting untuk saat dia perlu memilih gambar atau wajah yang tepat untuk sebuah meme. Misalnya, jika dia melihat wajah bingung, AI bisa membuat teks seperti “Aku juga bingung sama hidup ini”. Buat Algoritma Kreatif AI harus tahu bagaimana memadukan gambar dan teks yang sering kali absurd. Kalau dia bisa membuat meme yang cocok dengan situasi apapun (misalnya, "Ketika kamu bangun terlambat tapi masih ada waktu untuk ngopi"), itu sudah pasti kemenangan besar! Uji dengan Pengguna Jangan cuma percaya pada AI, ya! Tes meme yang dia buat ke teman-temanmu. Kalau mereka ngakak, berarti AI sudah berhasil menjadi ahli meme. Dan voilà, kamu punya AI yang siap membuat meme yang bisa bikin siapa saja tertawa, bahkan kalau itu meme tentang kucing yang enggak ngerti teknologi! #meme #shitpostindonesia #memeindonesia #humor #jokes #memelucu #foryoupage #fypシ #memeindo
#Datast Reel by @aibutsimple - Principal Component Analysis (PCA) is an unsupervised machine learning technique used for dimensionality reduction while preserving as much variance a
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@aibutsimple
Principal Component Analysis (PCA) is an unsupervised machine learning technique used for dimensionality reduction while preserving as much variance as possible in a dataset. It transforms the original correlated variables into a new set of uncorrelated variables called principal components. The process begins by centering the data (which is subtracting the mean), then computing the covariance matrix to capture the relationships between variables. Eigenvectors and their corresponding eigenvalues are then calculated from the covariance matrix. The eigenvectors represent the directions/principal components of the highest variance, while the eigenvalues quantify the amount of variance in each direction. By selecting the top “k” eigenvectors with the highest eigenvalues, PCA projects the data into a lower dimensional space, simplifying analysis and visualization while retaining the most important information. C: deepia Join our AI community for more posts like this @aibutsimple 🤖
#Datast Reel by @missamychan (verified account) - Data reveals that single people predictably are drawn to certain qualities (no surprise).    But these singles are overlooking people who could be a g
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@missamychan
Data reveals that single people predictably are drawn to certain qualities (no surprise).    But these singles are overlooking people who could be a great partner, because these ‘shiny qualities’ they are optimizing for don’t seem to impact relationship satisfaction in the long run.    What do you think? Let me know in the comments.    From research conducted by social psychologist Samantha Joel and 85 scientists who built a dataset of over 11,000 couples. You can also read “Don’t Trust Your Gut by Seth Stephens-Davidowitz to dive into the research more.     👉follow @missamychan   #datingtips #shortking #shortmen #datingcoach #vancouverinfluencer #secureattachment
#Datast Reel by @austin.marchese (verified account) - Why 100,000 families just agreed to let robots watch them 👇 Figure AI just partnered with real estate giant Brookfield to create the world's largest
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@austin.marchese
Why 100,000 families just agreed to let robots watch them 👇 Figure AI just partnered with real estate giant Brookfield to create the world’s largest humanoid robot training dataset. They’re getting access to over 100,000 residential units plus millions of square feet of commercial space. But here’s the genius part nobody’s talking about. Instead of programming robots to do specific tasks, they’re just recording humans doing everyday things—opening fridges, climbing stairs, doing laundry. Their AI model called Helix watches all this footage and learns purely from observation. And this solves the biggest problem in robotics. See, every home is completely different. Different layouts, different furniture, different obstacles. Traditional robots would need years of custom programming for each house. But Figure’s robots can now respond to commands like ‘go to the fridge’ in any house, because they learned by watching people in thousands of different homes. But to me, this answers a question that nobody’s asking: Why do these robots have to look like people? Well, every door, staircase, light switch, and appliance in your house was built for human proportions. Not wheels. Not four legs. Human bodies. So by making robots LOOK LIKE HUMANS, they can use everything we’ve already built without changing a single thing about our homes. It’s backward compatibility for the physical world. If you want these things to be used, you can’t rebuild everything from scratch. We’re watching the birth of robots that can navigate any human space by simply observing how humans do it. The humanoid form isn’t about mimicking humans—it’s about fitting into the world we already built.
#Datast Reel by @artificialintelligence.co (verified account) - Korean interactive exhibit visualizes convolutional neural networks (CNNs) and Al image recognition, possibly using MNIST dataset, blending technology
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@artificialintelligence.co
Korean interactive exhibit visualizes convolutional neural networks (CNNs) and Al image recognition, possibly using MNIST dataset, blending technology with cultural heritage in an engaging display. [Media Credit: lucas_flatwhite/x]

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