1.8M
MADo you think we can build a solid model at the end of this year? #formula1 #machinelearning #programming
@mar_antaya
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#Machinelearning
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EQThe Secret Behind Every Trend Line ! #LeastSquares #LinearRegression #DataScience #Math #Statistics #MachineLearning Ever wondered how software finds the perfect line through messy data points? This short animation explains the Least Squares Method, the backbone of linear regression. We visualize the difference between data points and the trend line as physical squares, showing exactly what it means to minimize the sum of squared errors. Watch as the line adjusts its slope and intercept until it finds the optimal fit for the data set.
@equationsinmotion
801.6K
AIK-Nearest Neighbours (KNN) is a simple and intuitive supervised machine learning algorithm that makes predictions based on how similar things are to each other. They can be used for classification and regression.
Imagine you have a scatter plot with red and blue points, where red points represent one class and blue points represent another class. Now, let’s say you get a new data point you haven’t seen before, and want to know if it should be red or blue.
KNN looks at the “K” closest points (a hyperparameter that you set) to this new one — say, the 3 nearest points.
If 2 out of those 3 are red and 1 is blue, the new point is classified as red. It’s like asking your closest neighbors what they are and choosing the majority answer. Although simple, KNN performs surprisingly well based on the principle of proximity.
Want to get better at machine learning? Accelerate your ML learning with our Weekly AI Newsletter—educational, easy to understand, mathematically explained, and completely free (link in bio 🔗).
C: visually explained
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#machinelearning #statistics #mathematics #math #physics #computerscience #coding #science #education #datascience #knn
@aibutsimple
1.8M
REPython wasn't built to be trendy - it was built to be useful.
Guido van Rossum created Python because C was powerful but unsafe, and shell scripts were too limited in scope.
He wanted a language that was easier to use, safer than C, and smart enough to handle things like memory management and bounds checking - without slowing developers down.
That decision is why Python powers Al, machine learning, data science, automation, and startups worldwide today.
Sometimes the best tech isn't invented to impress it's invented to solve a real problem.
Do you think Python is still the best beginner-friendly language in 2026?
Follow @realbigbrainai to stay up to date with the latest Al news.
@realbigbrainai
190.7K
SMLearning something new everyday 📚
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#softwareengineer #codingisfun #growthmindset #machinelearning #sydneyengineers #algorithms #deeplearning
@smith.iscoding
2.4M
SOme every day 🥹🥲
#research #computerscience #datascience #computervision #girlwhocodes #codinglife #softwareengineer #artificialintelligence #studygram #machinelearning #womenintech #womenwhocode #tech #learningdiary #researchpaper
@sopi.iscoding
196.8K
ILk-Nearest Neighbors 📍
#ia #machinelearning #ciencia #knearestneighbors #math
@illariy.ai
1.2M
MIWhat happens when you ask AI a question?
In just milliseconds, your words are broken into tokens, converted into vectors, and processed through layers of neural networks. Attention mechanisms determine what matters most, while mathematical operations shape meaning and context — all before an answer appears.
This reel highlights the core ideas behind how modern AI systems process language.
#AI #ArtificialIntelligence #MachineLearning #DeepLearning #NeuralNetworks #LanguageModels #LLM #AIExplained #HowAIWorks #TechEducation #AIVisualization #MLBasics #NLP #DataScience #FutureOfAI
@ming_llm
1.9M
AIThis isn’t a movie scene.
It’s not CGI.
It’s AI-generated chaos in the most entertaining way possible 🤖😂
An 18-second clip just went viral showing a woman stepping into a sleek Panasonic-branded exosuit… and then instantly sprinting away like a cybernetic superhero. The internet clocked it at 1.6 million views in hours. But what really broke brains wasn’t the speed. It was the physics.
Her foot compresses unnaturally.
Her legs glitch mid-stride.
The suit looks powerful… and slightly terrifying.
And suddenly everyone realized:
We’ve entered the era where AI can generate robotics fantasies that look almost real. Almost.
This clip playfully references Panasonic’s real-world exosuits like the ATOUN Model Y, built for industrial back support and worker safety. But AI took that concept and cranked it to sci-fi mode, creating a wearable machine that looks like it could either save your spine… or snap it in half 💀🤖
What’s fascinating here isn’t just the humor.
It’s what this reveals about AI video generation, robotics perception, and human trust in machines.
And that’s the real moment.
AI isn’t just making art.
It’s shaping how we imagine technology before engineers even build it.
Exoskeletons are real.
AI-generated robotics are real.
The line between prototype and parody is disappearing fast.
Today it’s funny.
Tomorrow it’s product design.
Would you wear an AI-designed exosuit if it promised superhuman strength… but glitched like this? 👇😳
#AI #ArtificialIntelligence #Robotics #Exoskeleton #FutureTech #AIVideo #TechHumor #GenerativeAI #MachineLearning #DeepTech #WearableTech #HumanMachine #CyberFuture #TechReels #Futurism #InnovationDaily
This viral AI-generated video shows a woman entering a Panasonic-style robotic exosuit that activates and sprints away, blending humor with uncanny realism. Viewers noticed AI artifacts like distorted foot compression and glitchy movement, sparking debate about wearable robotics safety and realism. Inspired by real Panasonic exoskeletons such as the ATOUN Model Y, the clip highlights how generative AI is reshaping robotics perception, digital content creation, and futuristic technology storytelling.
@aitechadvice
850.0K
SOResearcher name: Guangting Yu, Dailey Labs
The next wave of AI isn’t just chat. It’s simulation.
As models get better at learning dynamics, differential equations, and world models, AI moves closer to running experiments, testing ideas in simulated environments, and helping engineer real systems before they’re built.
Simulation engineering may be one of the biggest frontiers in AI. 🌊
#startuplife #founder #tech #machinelearning #ai
@sorhan.hq
954.8K
WDIf I had to learn AI from scratch today, I would start here.
3 sites that explain Math, ML, and LLMs Concepts visually — faster than any course.
Comment “VISUAL” — I’ll DM you all 3 links.
#softwareengineering #developers #csstudents #programming #machinelearning
@wdf_ai
259.0K
DAMain Challenges in Machine Learning:
1. Insufficient or Poor-Quality Data
Lack of labeled data for supervised learning.
Noisy, incomplete, or biased data can lead to poor models.
2. Overfitting and Underfitting
Overfitting: Model performs well on training data but poorly on new data.
Underfitting: Model is too simple to capture the underlying pattern.
3. High Computational Cost
Training complex models (e.g., deep learning) requires powerful hardware and GPUs.
4. Scalability
Models trained on small datasets may not scale well to real-world data.
5. Model Interpretability
Many powerful models (like deep neural networks) act as "black boxes" with low transparency.
6. Data Privacy and Security
Legal and ethical concerns around collecting and using personal data (e.g., GDPR).
7. Bias and Fairness
Models can inherit or amplify biases present in training data, leading to unfair outcomes.
8. Deployment and Maintenance
Moving from prototype to production can be complex (MLOps needed).
Continuous monitoring and updating are essential.
9. Choosing the Right Algorithm
Selecting the most suitable model and tuning it can be time-consuming and non-trivial.
10. Domain Knowledge
Understanding the domain is crucial to feature selection, data preparation, and result interpretation.
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@datasciencebrain
✨ Guide de Découverte #Machinelearning
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