Sabitlenmiş Tweet
We had our first board meeting!
Let's see how it ends!
Dear Algorithm, help me connect tech (Benguluru) audience!
English
Kamal kishor
95 posts
Kamal kishor
@Lifebeyond_9pm
IIT Madras | AIML Engineer | Curious | Judgemental | Building Something 🚧 https://t.co/siCVdRTUGk
Bengaluru, India Katılım Mayıs 2026
22 Takip Edilen4 Takipçiler
@dextersgirll aiengineeringfromscratch.com
You Can follow this resource
English
Trying to build a network of developers, builders, and learners.
If you're into coding, open source, startups, AI, or software engineering, drop a follow and let's connect.
#Programming #SoftwareEngineer #BuildInPublic #Tech
English
Gradient descent is the engine behind training almost every ML model!
Core:
Imagine model has a param w, and a loss function that measures how wrong your predictions are. loss surface is curve your goal is to find the value of w where the loss is lowest.
w(i+1) = w(i)-α*gradient
English
Kamal kishor retweetledi
We had our first board meeting!
Let's see how it ends!
Dear Algorithm, help me connect tech (Benguluru) audience!
English
CatBoost is an ensemble method. It builds decision trees sequentially, where each new tree learns to correct the mistakes (residuals) of all previous trees. The final prediction is a weighted sum of all trees. This is the same foundation as XGBoost and LightGBM.
#MachineLearning
English
Kamal kishor@Lifebeyond_9pm
CatBoost is an ensemble method. It builds decision trees sequentially, where each new tree learns to correct the mistakes (residuals) of all previous trees. The final prediction is a weighted sum of all trees. This is the same foundation as XGBoost and LightGBM. #MachineLearning
QME
Kamal kishor@Lifebeyond_9pm
CatBoost is an ensemble method. It builds decision trees sequentially, where each new tree learns to correct the mistakes (residuals) of all previous trees. The final prediction is a weighted sum of all trees. This is the same foundation as XGBoost and LightGBM. #MachineLearning
QME
Kamal kishor@Lifebeyond_9pm
CatBoost is an ensemble method. It builds decision trees sequentially, where each new tree learns to correct the mistakes (residuals) of all previous trees. The final prediction is a weighted sum of all trees. This is the same foundation as XGBoost and LightGBM. #MachineLearning
English
Kamal kishor@Lifebeyond_9pm
CatBoost is an ensemble method. It builds decision trees sequentially, where each new tree learns to correct the mistakes (residuals) of all previous trees. The final prediction is a weighted sum of all trees. This is the same foundation as XGBoost and LightGBM. #MachineLearning
English
Kamal kishor@Lifebeyond_9pm
CatBoost is an ensemble method. It builds decision trees sequentially, where each new tree learns to correct the mistakes (residuals) of all previous trees. The final prediction is a weighted sum of all trees. This is the same foundation as XGBoost and LightGBM. #MachineLearning
QME
@amanhostednft x.com/Lifebeyond_9pm…
If you want to understand ML models in easy way, follow me!
Kamal kishor@Lifebeyond_9pm
CatBoost is an ensemble method. It builds decision trees sequentially, where each new tree learns to correct the mistakes (residuals) of all previous trees. The final prediction is a weighted sum of all trees. This is the same foundation as XGBoost and LightGBM. #MachineLearning
English
Kamal kishor@Lifebeyond_9pm
CatBoost is an ensemble method. It builds decision trees sequentially, where each new tree learns to correct the mistakes (residuals) of all previous trees. The final prediction is a weighted sum of all trees. This is the same foundation as XGBoost and LightGBM. #MachineLearning
English
Kamal kishor@Lifebeyond_9pm
CatBoost is an ensemble method. It builds decision trees sequentially, where each new tree learns to correct the mistakes (residuals) of all previous trees. The final prediction is a weighted sum of all trees. This is the same foundation as XGBoost and LightGBM. #MachineLearning
QME
When you crack your head trying to run machine learning models for your weekend class 😵💫💻....
English
Kamal kishor@Lifebeyond_9pm
CatBoost is an ensemble method. It builds decision trees sequentially, where each new tree learns to correct the mistakes (residuals) of all previous trees. The final prediction is a weighted sum of all trees. This is the same foundation as XGBoost and LightGBM. #MachineLearning
QME
Why CatBoost:
-Ordered boosting
-Native categorical feature handling
Demand forecasting involves lots of categorical features (SKU, store, and brand) mixed with numerics.
English
Kamal kishor retweetledi
Bro to Bro: build your x account now
Just say “hello” and gain 680 mutuals here.
English
Hi brands i am here building on X with 2 followers.
Currently searching for promo opportunities it would be great if so DM with offers 🥹
Peace 🤍✨
English