1.5K
FIЛовите крутой аналог Google таблиц и Exel со встроенным ИИ 🔥
@fink_gpt
#Erwin Data Modeler Features
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(12)
137.8K
EOПишите АГЕНТ под роликом, если хотите внедрить ИИ-автоматизации в ваши бизнесы
@eorlov_ai
209
DACheckout the Highlights,
@datadecode.de
5.2K
BRAnemômetro para Arduino
@brincando.ideias.lab
503.1K
EVI think GLM 4.5 won this round🧐. Prompt: “Create a 2D Eulerian fluid simulator in a single HTML file using JavaScript and the Canvas API. The simulation should visualize the flow of incompressible and inviscid (zero-viscosity) fluids on a fixed grid. A constant stream of smoke should enter from a tube on the left side of the canvas, and the flow must interact with a circular obstacle placed in its path.”
#ai #artificialinteligence #evotionai #testingai #generativeai #claudevsgpt #gpt5 #claude #programming #chatgpt
@evotionai
19.8K
PAReady to dive into the nitty-gritty of ER diagram creation? 🎨 Let’s break it down step by step! 📝
Step 1: Identify Entities and Attributes 🧩
Start by identifying the entities in your project, such as Users, Products, or Orders. Then, define the attributes for each entity – these are the specific properties or characteristics that describe them. Think UserID, Email, and Role for Users, or ProductID, Name, and Price for Products. 📊
Step 2: Determine Relationships 🤝
Next, establish the relationships between your entities. Is there a one-to-many relationship between Users and Orders, or a many-to-many relationship between Products and Orders? Understanding these connections is crucial for designing a robust database schema. 🌐
Step 3: Draw the ER Diagram 🖋️
Now it’s time to bring your vision to life! Use a tool like Lucidchart or draw.io to create your ER diagram. Place entities as boxes and connect them with lines to represent relationships. Don’t forget to label your relationships with their cardinality – is it one-to-one, one-to-many, or many-to-many? 📈
Step 4: Review and Refine ✨
Take a step back and review your ER diagram. Does it accurately capture the structure and relationships of your database? Are there any missing entities or attributes? Refine your diagram as needed to ensure clarity and completeness. 🔄
Step 5: Iterate and Collaborate 🤝
Finally, remember that creating an ER diagram is an iterative process. Collaborate with your team, gather feedback, and make revisions as your project evolves. By continually refining your ER diagram, you’ll ensure that your database design aligns with your project goals and requirements. 🚀
So, ready to turn your project vision into a visual masterpiece? Let’s ER diagram like a pro! 💻💡
#coding #programming #software #database #sql #web #webdeveloper #webdevelopment
@parthdahiya_
3.6K
BAModels don’t learn from raw data.
They learn from features.
Feature engineering is the process of
turning messy, raw data
into meaningful input a model can understand.
Age → Age group
Timestamp → Day, Month, Season
Text → Numerical representation
Better features = Better predictions.
SAVE this before training your next model.
#featureengineering
#machinelearning
#datascience
#aiml
#mlbasics
#pythonprogramming
#techreels
#typographyinspired
#typographydesign
@bakwaso_pedia
34.9K
WO🚀There are many types of generative models, handling different data modalities across various domains. #generativeai
📌Text-to-text: Models that generate text from input text, like conversational agents. Ex- amples: LLaMa 2, GPT-4, Claude, and PaLM 2.
📌Text-to-image: Models that generate images from text captions. Examples: DALL-E 2, Stable Diffusion, and Imagen.
📌Text-to-audio: Models that generate audio clips and music from text. Examples: Jukebox, AudioLM, and MusicGen.
📌Text-to-video: Models that generate video content from text descriptions. Example: Phenaki and Emu Video.
📌Text-to-speech: Models that synthesize speech audio from input text. Examples: WaveNet and Tacotron.
📌Speech-to-text: Models that transcribe speech to text [also called Automatic Speech Recognition (ASR)]. Examples: Whisper and SpeechGPT.
📌Image-to-text: Models that generate image captions from images. Examples: CLIP and DALL-E 3.
📌Image-to-image: Applications for this type of model are data augmentation such as su- per-resolution, style transfer, and inpainting.
📌Text-to-code: Models that generate programming code from text. Examples: Stable Dif- fusion and DALL-E 3.
📌Video-to-audio: Models that analyze video and generate matching audio. Example: Soun- dify.
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#datascience #researchpaper #womeninstem #datascientist #dataanalyst #machinelearningengineer #datasciencetools #datascienceeducation #machinelearningalgorithms #deeplearningmachine #datadriven #dataanalytics #dataset #machinelearning #deeplearning #computervision #naturallanguageprocessing #largelanguagemodels #sql
@woman.engineer
15.5K
DABuilt a real tool for my business today with Enter Pro. 🤯
@enterpro_official
What if you didn’t have to write endless Python scripts…
clean data manually…
or switch between 5 different tools just to get insights?
I tried building a fully automated data analysis agent in 1 hour…
and it actually works.
You just upload your requirements →
and it plans, codes, analyzes, and even creates reports on its own.
No notebooks.
No debugging headaches.
No tool hopping.
This isn’t just automation…
this is what the future of data analysts looks like.
Try this once… and you won’t go back 👀
#enter #enterpro #60minaifounder #vibecoding #dataanalytics
@datawithashok
46.2K
DHData modeling in Power BI is like building a “map” for your data so tables can talk to each other. With the right relationships (for example, using a Product Key), your visuals become more accurate, structured, and easier to analyze. When the data structure is solid, Power BI from Microsoft isn’t just about good-looking dashboards, it delivers real insights.
#PowerBI #DataModeling #BusinessIntelligence
@dhanyindraswara
22.0K
TEI read all the dm’s asking to shorten the episode length and this time I tried to keep it as short as possible 🤍
Mixture-of-Expert Models in Generative AI:
To put it straight, with MOE you have,
Faster inference with less hardware
Specialized knowledge without bloating the model
Supports scalable training, especially in trillion-parameter models like Google’s Switch Transformer or Meta’s recent Mixtral.
You don’t ask every employee in a company when you have a finance question, you go to the finance team. Same with MoE.
Google’s PaLM uses MoE to combine language, reasoning, and vision capabilities by routing tasks to the right experts, making it super powerful across domains.
That’s MoE for you ✨
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🏷️ Day 11, 50 Day Challenge, Generative Al, Artificial Intelligence, Al, Large Language Models, OpenAl, Al Evolution, Important Concepts, Series, Al Series
@techwithnt
162.2K
AMComment “Verdent" for the link and start turning your ideas into fully functioning apps, websites, games and more..
Building isn’t about rushing code; it’s about clarity and focus. With @verdent__ai I plan ideas in Plan Mode, test different approaches in Workspaces, and execute tasks smoothly with Agent Mode without breaking flow. No context switching, no chaos just a better way to build.
#verdentai #artificalintelligence #aicoding #entrepreneurship #marketing
@amineogg
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