Jupiter Coder

Hey, I’m Jupiter 👋 I tweet about: 🤖 AI & coding explained simply 💻 My dev journey & DSA learnings 😆 Funny tech takes (because debugging is pain) Follow if you like tech + humor 🚀 I am on a Journey let's see where I go?

Insight's u might like: Why does sunlight get 4x weaker when you double your distance from it? Not because the Sun loses energy. Because the same energy spreads over 4x the area. That's the inverse square law Physics explains it Life reflects it

What any one tell me what is happiness and love????

@GuruShareMarket Parents think we r making excuses not to work but how to tell them we have more urgent to work and earn money that they never had

Look at the generational difference… Your parents at 25: - Married - House - 2 kids - Stable career You at 25: - 3 roommates - claude grok chatgpt subscriptions - 4 LPA IT service job - 10 rejections from interviews...Show more

Can u here me???

Good morning Anyone wake up here

Is knowing the truth always better than living in comfort?

Data Science Reality Check Train: 70% Test: 69% At least it’s not overfitting! Actually, it’s Underfitting Model is stable, but consistently wrong It’s like a GPS that tells everyone "turn left" regardless of destination Stable failure is still failure add some complexity

Stop guessing and start diagnosing: Underfitting: Too simple. Fails everywhere Overfitting: Too complex. Memorizes the past, fails the future. Good Model: Generalizes well the Sweet Spot If your Train and Test scores are both 70% don't celebrate model is just consistently wrong

Machine Learning just like dating High Bias/Underfitting: You have one rigid "type" and ignore everyone else Result: You're lonely High Variance/Overfitting: You over-analyze everything from "Hey" vs "Hi" Result: Total paralysis Finding that sweet spot is basically a miracle

Underfitting 📉 Underfitting is when a model is too "lazy" to learn the actual pattern in your data Both training and testing accuracy stay stubbornly low It happens because the model is too simple to grasp the underlying complexity #MachineLearning #AI

Fixing a Clingy Model 🤖 If your model is overfitting, it’s just memorizing the answer key Diet: Use a simpler model The "Tax": L1/L2 Regularization The Chaos: Dropout to keep neurons on their toes The Buffet: More data & Augmentation Train for intelligence, not just memory!

Underfitting happens when the model is too simple to capture the real pattern in the data. 1. Low training accuracy 2. Low testing accuracy

Overfitting → model memorizes training data Result → high training accuracy, low test accuracy Problem → poor generalization just exploring......

The Cat in the Box 🐱 Overfitting is when your model memorizes the training "box" so perfectly that it fails to generalize to any other shape Training: 100% (I fits!) Testing: 0% (Wrong shape!) Don't let your models lose the big picture by obsessing over noise #MachineLearning

Machine Learning Essentials Overfitting is when a model mistakes random noise for a real signal Training accuracy looks perfect, but testing accuracy crashes on new data It happens because the model memorizes specific details instead of learning the general pattern #AI

Experiment: Photo → Anime using AnimeGANv2. Things I expected to do: Run the model Things I actually did: 1. Fix TensorFlow 1.x code 2. Replace deprecated APIs 3. Read 200 StackOverflow posts Tried all these without AI Worth it though 😄 Original vs Anime result 👇 #AI

Ai ai ai ai ai ............... What left Haha ha It's ai

@AnshikaK7 Death note or attack on Titan or one piece

Machine Learning – Day 1 Concept: Train/Test Split We divide data into: • Training set → model learns patterns • Testing set → model is evaluated This prevents overfitting. #MachineLearning #AI

Why is Swin Transformer becoming a strong backbone for computer vision? Because it solves the biggest problem of Vision Transformers scalability. Key ideas: • Shifted window attention • Hierarchical feature maps • Lower computation cost