30.1M
AR#WorldOfAImagination provides a unique visual experience that captures the interplay between human creation and machine computation. Explore this captivating exhibition at #ARTECHOUSENYC.🎨✨💜 #ARTECHOUSE
@artechouse
#Deepled
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WOBlink.new is an AI-powered full-stack app builder that turns simple English prompts into fully working, hosted web or mobile apps — now with the ability to create AI agents using nothing but instructions.
Positioned as a “vibe coding” platform, it lets users build serious products like SaaS tools, games, or internal software by chatting with an AI instead of writing code. The system doesn’t just design interfaces — it creates the frontend, backend logic, databases, and APIs in one flow.
Blink also handles the hard parts automatically. It sets up databases, authentication, hosting, custom domains, SSL, and global delivery. Its AI can debug its own code, fix errors, and iterate without constant user intervention. Users can even recreate existing apps by describing them or sharing a link.
With built-in support for agentic AI, apps can include their own autonomous agents that search the web, analyze data, or generate content. It’s built for vibe coders, students, and startup founders who want to move from idea to product fast — without touching traditional code.
For the latest updates on AI, future tech & groundbreaking innovations — follow @WorldOf.AIx 🤖🚀
#AI #ArtificialIntelligence #FutureTech #MachineLearning #DeepLearning #TechNews #Innovation #AIUpdates #TechReels #AIFuture #TechTrends #AITools #NeuralNetworks #AICommunity #blinkai
@worldof.aix
5.2K
D4“Relatable? Then hit follow for more 😌🔥”
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@d4datascience
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@d4datascience
3.1M
DIModular Stadium Light -600w
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#decoration #event #events #eventos #eventplanner
@dipak_dharme
129.3K
THOriginal Video by @codcoders Have you ever noticed that when you’re feeling happy, sad, or even just curious — Instagram starts showing videos that match your exact mood?
That’s not coincidence. It’s computation.
Behind this subtle personalization lies a powerful stack of AI-driven recommendation systems — from the YouTube Recommendation Model to advanced deep learning architectures like DIN (Deep Interest Network), DIEN (Deep Interest Evolution Network), and BERT4Rec.
These systems don’t just recommend content.
They transform behavioral signals into emotion-driven insights.
By analyzing:
• Watch time
• Click-through behavior
• Skips and scroll velocity
• Replays
• Interaction patterns
AI models build dynamic behavioral embeddings that approximate emotional states and evolving interests.
The result?
Feeds that adapt in real time.
Content that aligns with your mood.
A hyper-personalized experience powered by predictive modeling.
This is where behavior meets emotion.
And this is where the future of AI-driven content lives.
There are more deep-tech breakdowns like this on the page.
👉 Follow us for cutting-edge insights on AI, algorithms, and the hidden systems shaping your digital world.
@theaiorbit
166.1K
DI960w Modular Stadium Light
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@dipak_dharme
1.2M
ST🌅 BEST TIME TO STUDY (Scientifically Proven!)
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⚡ Stop studying randomly. Start studying scientifically.
👉 Follow @studyedia_ for daily brain-boosting study hacks✨💫
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@studyedia_
346.5K
WO🚀 How to Become an AI Engineer in 2026 👩🏻💻Save for later
Step-by-step Roadmap
🔹 PHASE 1: Foundations (0–3 Months)
Don’t skip this. Weak foundations = stuck later.
1️⃣ Programming (Must-Have)
Python: loops, functions, OOP
Libraries: NumPy, Pandas, Matplotlib / Seaborn
📌 Practice daily:
LeetCode (easy)
HackerRank (Python)
2️⃣ Math for AI (Enough, not PhD level)
Focus only on:
Linear Algebra (vectors, matrices)
Probability & Statistics
Basic Calculus (idea of gradients)
📌 Conceptual understanding is enough — no heavy theory.
🔹 PHASE 2: Machine Learning (3–6 Months)
Learn:
Supervised & Unsupervised Learning
Feature Engineering
Model Evaluation
Algorithms:
Linear & Logistic Regression
KNN
Decision Trees
Random Forest
SVM
K-Means
Tools:
Scikit-learn
📌 Project Ideas:
House price prediction
Student performance prediction
Credit risk model
🔹 PHASE 3: Deep Learning & AI (6–10 Months)
Learn:
Neural Networks & Backpropagation
CNN (Images)
RNN / LSTM (Text)
Transformers (Basics)
Frameworks:
TensorFlow or PyTorch (choose ONE)
📌 Project Ideas:
Face mask detection
Image classifier
Spam email detector
Basic chatbot
🔹 PHASE 4: Modern AI (2025–2026) 🔥
This is where the JOBS are coming from.
Learn:
Generative AI
Large Language Models (LLMs)
Prompt Engineering
RAG (Retrieval-Augmented Generation)
Fine-tuning models
Tools:
OpenAI API
Hugging Face
LangChain
Vector Databases (FAISS / Pinecone)
📌 Project Ideas:
AI PDF Chat App
Resume Analyzer
AI Study Assistant
AI Customer Support Bot
🔹 PHASE 5: MLOps & Deployment (CRITICAL)
Learn:
Git & GitHub
Docker (basics)
FastAPI / Flask
Cloud basics (AWS or GCP)
Deploy:
ML models as APIs
AI apps on the cloud
📌 Recruiters LOVE deployed projects.
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#datascientist #aiengineer #codinglife #softwaredeveloper #programming
@woman.engineer
1.9M
SEFind out how normal you are through facial ai
This website not only guesses how normal you are but it also teaches you how facial recognition is used throughout the world.
What scores did you get?
Enjoy!
@setupspawn
18.8K
HAApakah kamu pernah mengalami sistem ujian yang seperti ini? Ceritakan pengalamanmu yah…
#Ujian #exambrowser #pendidikan #midsemester #deeplearning
@harnoiasrinlg
4.8M
COSignal analysis using deep learning
Jokes aside, I built an ECG Signal Analysis deep learning model using CNN-LSTM architecture to detect heart arrhythmias.
The reason why I combined CNN (Convolutional Neural Networks) and LSTM recurrent neural networks is because of their excellent spatial pattern recognition which is important for PQRST waves.
CNN excels at spatial pattern recognition
LSTM captures temporal dependencies (rhythm patterns)
So in the end I got a crazy robust model for medical time-series analysis! I built with PyTorch which is now the industry standard and here’s how I did it:
1️⃣ Get the Data - Downloaded MIT-BIH Arrhythmia dataset (87K+ heartbeat samples)
2️⃣ Explore & Visualize - Plotted ECG signals to understand patterns across 5 arrhythmia types
3️⃣ Handle Imbalance - Used class weighting since 83% of data was normal beats
4️⃣ Normalize Signals - Scaled data for better neural network performance
5️⃣ Build CNN Layers - 3 convolutional blocks extract spatial features from ECG waves
6️⃣ Add LSTM Layers - Bidirectional LSTM captures temporal patterns in heartbeats
7️⃣ Combine Architectures - CNN finds patterns, LSTM understands sequences = powerful combo!
8️⃣ Train the Model - 50 epochs with Adam optimizer and learning rate scheduling
9️⃣ Evaluate Performance - Confusion matrix, ROC curves, 95.51% accuracy on dangerous arrhythmias!
🔟 Deploy & Predict - Model ready to classify new ECG signals in real-time
Follow @codingmermaid.ai for more content like this and comment “PROJECT” if you want to tweak it or use it as reference for your portfolio!
@codingmermaid.ai
✨ #Deepled Entdeckungsleitfaden
Instagram hostet 1K Beiträge unter #Deepled und schafft damit eines der lebendigsten visuellen Ökosysteme der Plattform.
Entdecken Sie die neuesten #Deepled Inhalte ohne Anmeldung. Die beeindruckendsten Reels unter diesem Tag, besonders von @artechouse, @codingmermaid.ai and @d4datascience, erhalten massive Aufmerksamkeit.
Was ist in #Deepled im Trend? Die meistgesehenen Reels-Videos und viralen Inhalte sind oben zu sehen.
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