922.9K
WOSteve brunton is sooo GOATEDDD !!!
#machinelearning #datascience #stem #artificialintelligence
@workiniterations
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#Machinelearning
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EQThe Secret to Perfect Data Models #MachineLearning #PolynomialRegression #Statistics #Math #Manim Ever wondered why your machine learning model isn't performing as expected? In this video, we break down polynomial curve fitting, a fundamental concept in data science and statistics. We explore the visual differences between Degree 1 (Underfitting), Degree 3 (Good Fit), and Degree 11 (Overfitting). Learn how increasing the degree of a polynomial affects how it captures data trends and why the optimal model is crucial for accurate predictions.
@equationsinmotion
254.4K
SOPutting robotics to good use 😬
#tech #robotics #electronics #machinelearning #womenintech
@sopi.iscoding
2.4M
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
557.8K
SOMeet Guangting Yu ( @yugtmacs ):
- Applied Math PhD. Quant
- Finance MS (UMich).
- Worked on alpha discovery using Kalman filters. Published - AI/ML research (NeurIPS).
Quants don’t guess markets. They hunt alpha. Hidden edges in data.
Statistical arbitrage exploits tiny pricing gaps
Factor models like Fama French explain what drives returns.
- Kalman filters separate signal from noise in real time
- Mean reversion bets prices snap back
- Momentum rides trends early
- Machine learning models like XGBoost and neural nets predict moves before they’re obvious.
Covariance Formula:
Cov(X, Y) = E[XY] − E[X]E[Y]
Measures how two variables move together
Positive means they rise together, negative means they move opposite
Core signal used to detect relationships and extract alpha
While most people stare at charts
he’s modeling the system behind them
#quant #machinelearning #startups #ai #founder
@sorhan.hq
1.1M
FOAlexandr Wang was born in 1997 in Los Alamos, New Mexico, and showed an early talent for mathematics and computer science. After enrolling at Massachusetts Institute of Technology (MIT) he left to pursue entrepreneurship and co-founded Scale AI in 2016. The company focused on a critical problem in AI: providing the high-quality data labeling and infrastructure needed to train machine learning systems. As demand for AI accelerated, Scale AI became one of the most important infrastructure companies in the industry.
The company grew fast, raising capital from leading investors and securing contracts with major technology firms and government agencies, helping make Wang one of the youngest self-made billionaires in the world. By 2024, reports valued the company at approximately $13.8 billion, placing it among the most valuable private AI startups globally.
Wang often says progress comes from hyper-focus and relentless effort. His belief is simple: most people stop at one mile, while exceptional builders keep going ten. That extra intensity, applied consistently over time, is what creates separation. In industries moving as fast as AI, the founders willing to outwork, outfocus, and outlast everyone else are often the ones who shape the future.
>Follow us (@foundedceo) for more insights straight from the world’s leading founders & CEO’s 🤝
(Media: Shawn Ryan Show)
@foundedceo
1.2M
DOSomeone just conducted a live experiment that reveals how different AI systems perform when given real money and complete autonomy in financial markets.
Two autonomous AI agents were each given $1,000 and 48 hours to trade independently on Polymarket, a prediction market platform. No human intervention. No guidance. Just pure algorithmic decision making in real time.
Both agents started with identical conditions: same capital, same time window, same market access. The only variable was the underlying AI model powering each agent’s trading decisions.
The results were strikingly different.
The agent running on Claude executed over 5,200 trades during the 48-hour period, transforming the initial $1,000 into approximately $14,200. That’s a return exceeding 1,300%, while simultaneously covering its own API costs throughout the experiment.
The competing OpenClaw agent told a very different story. It executed fewer than 200 trades, experienced a 94% drawdown, and completely depleted its funds before the test period ended.
Same starting capital. Same market conditions. Two AI systems with vastly different outcomes.
This experiment highlights something important as artificial intelligence becomes more sophisticated and autonomous. These systems are no longer just theoretical constructs or controlled demonstrations. They’re capable of operating independently in complex, real world environments where financial decisions have actual consequences.
As machine learning models continue advancing and autonomous agents become more capable, we’re entering a phase where AI systems may actively compete against each other in financial markets, making split second decisions that humans simply cannot match in speed or volume.
The question isn’t whether this technology works. The question is what happens next as these systems scale.
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Don’t forget to Share and Follow @dontlookback.ai
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#AI #technology #trading #autonomousAI
artificial intelligence, machine learning, algorithmic trading, prediction markets, autonomous agents, financial technology, AI models, Claude AI, automated systems, market analysis, fintech innovation, intelligent automation
@dontlookback.ai
![#Machinelearning Reel by @shiftwithd - Next JAVA kuda nerchukovali 🙌🏻
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#telugu #foryoupage #telugucomedyvideos #shiftwithd #machinelearning
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SHNext JAVA kuda nerchukovali 🙌🏻
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#telugu #foryoupage #telugucomedyvideos #shiftwithd #machinelearning
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@shiftwithd
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
1.0M
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
5.1M
EXAl is revolutionizing bioacoustics by using machine learning to analyze, decode, and interpret complex animal vocalizations and behaviors. Nature and Earth Species Project report that Al is transforming whale songs, elephant rumbles, and bird calls into meaningful data, identifying patterns related to emotions, social structures, and specific communication signals.
Key breakthroughs in Al-driven animal communication:
Decoding Context and Meaning: Researchers are using deep learning to map animal sounds to specific behaviors, such as identifying alarm calls in prairie dogs or social chatter among dolphins.
Specific Species Discoveries: Al has found that African savanna elephants and common marmoset monkeys may give names to their companions.
Active Two-Way Communication Attempts: In a landmark study, researchers used Al to identify sperm whale calls, and in a Medium post, a whale engaged in a 20-minute, turn-taking conversation with scientists.
Predictive Modeling: The Earth Species Project is developing Al models that can predict what an animal will say next, aiming for a "digital Rosetta Stone" for species communication
#ai #AI #artificial
@explainingthefact
1.9M
TEThe man who helped build modern AI is now warning the world about it.
Geoffrey Hinton, often called the “Godfather of AI,” played a key role in developing neural networks and backpropagation — the same foundation behind today’s powerful AI systems. In 2024, he was honored with the Nobel Prize for his contributions.
But here’s the twist…
Hinton is no longer just celebrating AI — he’s warning us.
According to him, AI is already shaping reality in ways we don’t fully understand. From echo chambers on social media to large-scale surveillance and advanced scams, the impact is no longer theoretical — it’s happening now.
He also raised deeper concerns about the future:
AI systems becoming smarter than humans, autonomous decision-making machines, and technologies that could be misused if controlled by profit-driven companies instead of ethical systems.
His biggest message?
We are moving fast… but not carefully enough.
The question is no longer “What can AI do?”
It’s “Should we control it before it controls us?”
What do you think — opportunity or danger? 🤔
Credits: @nobelprize
DM for credit or removal.
#artificialintelligence #futuretechnology #machinelearning
#aivideo #technews
@techaivault
✨ #Machinelearning Discovery Guide
Instagram hosts 20 million posts under #Machinelearning, creating one of the platform's most vibrant visual ecosystems. This massive collection represents trending moments, creative expressions, and global conversations happening right now.
#Machinelearning is one of the most engaging trends on Instagram right now. With over 20 million posts in this category, creators like @explainingthefact, @shiftwithd and @wdf_ai are leading the way with their viral content. Browse these popular videos anonymously on Pictame.
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💡 Top performing posts average 3.4M views (2.0x above average). Moderate competition - consistent posting builds momentum.
Post consistently 3-5 times/week at times when your audience is most active
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💡 Top performing content gets over 10K views - focus on engaging first 3 seconds
✍️ Detailed captions with story work well - average caption length is 751 characters
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✨ Many verified creators are active (42%) - study their content style for inspiration
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