📚 Data Science Riddle
You discover your regression model performs poorly on recent data. The relationships between variables have shifted. What's this called?
You discover your regression model performs poorly on recent data. The relationships between variables have shifted. What's this called?
Anonymous Quiz
39%
Model Overfitting
39%
Concept Drift
10%
Sampling Error
13%
Data Leakage
Regularization: The Art of Keeping Models Humble
Overfitting is the “ego problem” of models. They memorize training data and forget how to generalize.
Regularization is how we humble them.
➡️ L1 (Lasso): Shrinks some weights to zero → performs feature selection.
➡️ L2 (Ridge): Reduces all weights slightly → smooths learning.
➡️ Dropout: Randomly removes neurons during training → prevents co-dependence.
It’s not about punishment but it’s about discipline.
Regularization teaches models to focus on patterns, not exceptions.
💭 Remember: The best models don’t just fit data. They respect uncertainty.
Overfitting is the “ego problem” of models. They memorize training data and forget how to generalize.
Regularization is how we humble them.
➡️ L1 (Lasso): Shrinks some weights to zero → performs feature selection.
➡️ L2 (Ridge): Reduces all weights slightly → smooths learning.
➡️ Dropout: Randomly removes neurons during training → prevents co-dependence.
It’s not about punishment but it’s about discipline.
Regularization teaches models to focus on patterns, not exceptions.
💭 Remember: The best models don’t just fit data. They respect uncertainty.
❤7😁1
Explaining LLMs By BigData Specialist.pdf
4.3 MB
This is our latest post from Instagram page, saved as PDF.
If you want a very comprehensive breakdown on what's LLMs are and how they actually work, you might want to check it out.
Here's our Instagram post: Explaining LLMs
If you want a very comprehensive breakdown on what's LLMs are and how they actually work, you might want to check it out.
Here's our Instagram post: Explaining LLMs
❤9
📚 Data Science Riddle
Why might your SQL join explode the number of rows unexpectedly?
Why might your SQL join explode the number of rows unexpectedly?
Anonymous Quiz
20%
Index missing
40%
Wrong join key
33%
Duplicate keys
8%
Slow query optimizer
Database Querying Using SQL.pdf
136.4 KB
Notes on SQL for data management and analysis, including queries and integration with R, from University of South Carolina.
❤2👏1
📚 Data Science Riddle
A business team wants interpretable insights, not just predictions. What's the best model to start with?
A business team wants interpretable insights, not just predictions. What's the best model to start with?
Anonymous Quiz
32%
Random Forest
36%
Logistic Regression
12%
XGBoost
19%
Deep Neural Net
Forwarded from Cool GitHub repositories
lerobot
This is an end-to-end library for robot learning. It handles the entire pipeline from loading and processing robotics datasets to training policies and deploying them in simulation or on real hardware.
Creator: huggingface
Stars ⭐️: 19,000
Forked by: 3,000
Github Repo:
https://github.com/huggingface/lerobot
#robotics #AI
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
Join @github_repositories_bds for more cool repositories. This channel belongs to @bigdataspecialist group
This is an end-to-end library for robot learning. It handles the entire pipeline from loading and processing robotics datasets to training policies and deploying them in simulation or on real hardware.
Creator: huggingface
Stars ⭐️: 19,000
Forked by: 3,000
Github Repo:
https://github.com/huggingface/lerobot
#robotics #AI
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
Join @github_repositories_bds for more cool repositories. This channel belongs to @bigdataspecialist group
GitHub
GitHub - huggingface/lerobot: 🤗 LeRobot: Making AI for Robotics more accessible with end-to-end learning
🤗 LeRobot: Making AI for Robotics more accessible with end-to-end learning - huggingface/lerobot
❤3
Denoscriptive Statistics and Exploratory Data Analysis.pdf
1 MB
Covers basic numerical and graphical summaries with practical examples, from University of Washington.
❤5👍2👏1
Relational DB Vs Graph DB by BigData Specialist.pdf
4.5 MB
This is our latest post from Instagram, saved as PDF.
It's a comprehensive breakdown(as always) explaining the difference between Relational DB and Graph DB in a fun and easy to grasp way.
⚠️ Spoiler alert:You will love it!
Here's our Instagram post: Relational DB Vs Graph DB
It's a comprehensive breakdown(as always) explaining the difference between Relational DB and Graph DB in a fun and easy to grasp way.
⚠️ Spoiler alert:
Here's our Instagram post: Relational DB Vs Graph DB
❤6👍2
Linear Regression.pdf
834.6 KB
Covers basics of Linear Regression for modeling numerical data, including assumptions and applications in genetics, from University of Washington.
❤5
📚 Data Science Riddle
In a real-world NLP project, your model performs poorly on new slang abbreviations. What's the fix?
In a real-world NLP project, your model performs poorly on new slang abbreviations. What's the fix?
Anonymous Quiz
7%
Add more layers
72%
Use contextual embeddings like BERT
13%
Tune dropout
8%
Increase token length
❤1
📚 Data Science Riddle
A data engineer complains that your model training job is failing in production due to schema mismatch. What's the root fix?
A data engineer complains that your model training job is failing in production due to schema mismatch. What's the root fix?
Anonymous Quiz
12%
Cast data types in code
16%
Skip invalid rows
21%
Retrain with old schema
52%
Use a schema registry
Covariance vs. Correlation: Same Family, Different Story
People use them interchangeably but they measure different things.
Covariance tells you the direction of relationship (positive or negative).
Correlation goes further; it tells you the strength, normalized between -1 and 1.
So while covariance can be 2345.67, correlation says 0.92. clear, interpretable, scale-free.
People use them interchangeably but they measure different things.
Covariance tells you the direction of relationship (positive or negative).
Correlation goes further; it tells you the strength, normalized between -1 and 1.
So while covariance can be 2345.67, correlation says 0.92. clear, interpretable, scale-free.
Covariance shows movement, correlation shows consistency.
❤5👍1
📚 Data Science Riddle
You're Processing a dataset with frequent schema evolution. Which format handles it most gracefully?
You're Processing a dataset with frequent schema evolution. Which format handles it most gracefully?
Anonymous Quiz
10%
ORC
13%
Avro
57%
CSV
19%
Parquet
❤4
Eigenvalues & Eigenvectors — Why PCA Actually Works
You’ve heard of PCA. But what’s really happening underneath?
PCA finds the directions (vectors) where your data varies the most.
Those directions are eigenvectors of the covariance matrix and the eigenvalues tell you how much variance each captures.
You’re basically rotating your data to find its “natural axes.”
You’ve heard of PCA. But what’s really happening underneath?
PCA finds the directions (vectors) where your data varies the most.
Those directions are eigenvectors of the covariance matrix and the eigenvalues tell you how much variance each captures.
You’re basically rotating your data to find its “natural axes.”
PCA isn’t compression — it’s discovering how your data wants to be seen.
❤7👏2