📌 6 Technical Skills That Make You a Senior Data Scientist

🗂 Category: DATA SCIENCE

🕒 Date: 2025-12-15 | ⏱️ Read time: 11 min read

Beyond writing code, these are the design-level decisions, trade-offs, and habits that quietly separate senior…

#DataScience #AI #Python

📌 Geospatial exploratory data analysis with GeoPandas and DuckDB

🗂 Category: PROGRAMMING

🕒 Date: 2025-12-15 | ⏱️ Read time: 13 min read

In this article, I’ll show you how to use two popular Python libraries to carry…

#DataScience #AI #Python

📌 Lessons Learned from Upgrading to LangChain 1.0 in Production

🗂 Category: AGENTIC AI

🕒 Date: 2025-12-15 | ⏱️ Read time: 5 min read

What worked, what broke, and why I did it

#DataScience #AI #Python

Machine Learning Fundamentals

A structured Machine Learning Fundamentals guide covering core concepts, intuition, math basics, ML algorithms, deep learning, and real-world workflows.

https://t.me/DataScienceM 🩷

Try the bot with a large search database within Petligram

Tip: Optimize PyTorch Model Performance with torch.compile

Explanation:
torch.compile (introduced in PyTorch 2.0) is a powerful JIT (Just-In-Time) compiler that automatically transforms your PyTorch model into highly optimized, high-performance code. It works by analyzing your model's computation graph, fusing operations, eliminating redundant computations, and compiling them into efficient kernels (e.g., using Triton for GPU acceleration). This significantly reduces Python overhead and improves memory locality, leading to substantial speedups (often 30-50% or more) during training and inference, especially on GPUs and for larger models, without requiring changes to your model architecture or training loop. The primary dynamic mode intelligently compiles subgraphs as they are encountered, providing a balance of performance and flexibility.

Example:

import torch
import torch.nn as nn
import time

# Define a simple neural network
class SimpleNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(1024, 2048)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(2048, 1024)
        self.dropout = nn.Dropout(0.2)

    def forward(self, x):
        x = self.fc1(x)
        x = self.relu(x)
        x = self.dropout(x)
        x = self.fc2(x)
        return x

# Prepare model and dummy data
device = "cuda" if torch.cuda.is_available() else "cpu"
model = SimpleNet().to(device)
dummy_input = torch.randn(128, 1024).to(device)
dummy_target = torch.randn(128, 1024).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
criterion = nn.MSELoss()
num_iterations = 50

# --- Benchmark without torch.compile ---
print(f"--- Running without torch.compile on {device} ---")
start_time = time.time()
for _ in range(num_iterations):
    optimizer.zero_grad()
    output = model(dummy_input)
    loss = criterion(output, dummy_target)
    loss.backward()
    optimizer.step()
if device == "cuda":
    torch.cuda.synchronize() # Wait for GPU ops to complete
time_uncompiled = time.time() - start_time
print(f"Time without compile: {time_uncompiled:.4f} seconds\n")

# --- Benchmark with torch.compile ---
# Apply torch.compile to the model. This happens once upfront.
# The default backend 'inductor' is typically the best performing.
compiled_model = torch.compile(model)
# Ensure optimizer is correctly set up for the compiled model's parameters
# (in this case, `compiled_model` shares parameters with `model`, so no re-init needed if parameters are the same object)

print(f"--- Running with torch.compile on {device} ---")
start_time = time.time()
for _ in range(num_iterations):
    optimizer.zero_grad()
    output = compiled_model(dummy_input) # Use the compiled model
    loss = criterion(output, dummy_target)
    loss.backward()
    optimizer.step()
if device == "cuda":
    torch.cuda.synchronize() # Wait for GPU ops to complete
time_compiled = time.time() - start_time
print(f"Time with compile: {time_compiled:.4f} seconds")

if time_uncompiled > 0:
    print(f"\nSpeedup: {time_uncompiled / time_compiled:.2f}x")

━━━━━━━━━━━━━━━
By: @DataScienceM ✨

📌 The Machine Learning “Advent Calendar” Day 15: SVM in Excel

🗂 Category: MACHINE LEARNING

🕒 Date: 2025-12-15 | ⏱️ Read time: 12 min read

Instead of starting with margins and geometry, this article builds the Support Vector Machine step…

#DataScience #AI #Python

📌 When (Not) to Use Vector DB

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2025-12-16 | ⏱️ Read time: 8 min read

When indexing hurts more than it helps: how we realized our RAG use case needed…

#DataScience #AI #Python

📌 Separate Numbers and Text in One Column Using Power Query

🗂 Category: DATA SCIENCE

🕒 Date: 2025-12-16 | ⏱️ Read time: 6 min read

An Excel sheet with a column containing numbers and text? What a mess!

#DataScience #AI #Python

📌 The Machine Learning “Advent Calendar” Day 16: Kernel Trick in Excel

🗂 Category: MACHINE LEARNING

🕒 Date: 2025-12-16 | ⏱️ Read time: 8 min read

Kernel SVM often feels abstract, with kernels, dual formulations, and support vectors. In this article,…

#DataScience #AI #Python

📌 Lessons Learned After 8 Years of Machine Learning

🗂 Category: MACHINE LEARNING

🕒 Date: 2025-12-16 | ⏱️ Read time: 7 min read

Deep work, over-identification, sports, and blogging

#DataScience #AI #Python

📌 A Practical Toolkit for Time Series Anomaly Detection, Using Python

🗂 Category: DATA SCIENCE

🕒 Date: 2025-12-17 | ⏱️ Read time: 9 min read

Here’s how to detect point anomalies within each series, and identify anomalous signals across the…

#DataScience #AI #Python

📌 The Machine Learning “Advent Calendar” Day 17: Neural Network Regressor in Excel

🗂 Category: MACHINE LEARNING

🕒 Date: 2025-12-17 | ⏱️ Read time: 7 min read

Neural networks often feel like black boxes. In this article, we build a neural network…

#DataScience #AI #Python

📌 Production-Grade Observability for AI Agents: A Minimal-Code, Configuration-First Approach

🗂 Category: AGENTIC AI

🕒 Date: 2025-12-17 | ⏱️ Read time: 12 min read

LLM-as-a-Judge, regression testing, and end-to-end traceability of multi-agent LLM systems

#DataScience #AI #Python

📌 3 Techniques to Effectively Utilize AI Agents for Coding

🗂 Category: LLM APPLICATIONS

🕒 Date: 2025-12-17 | ⏱️ Read time: 8 min read

Learn how to be an effective engineer with coding agents

#DataScience #AI #Python

1. What is the primary purpose of a loss function in a neural network?
A. To initialize model weights
B. To measure the model’s prediction error
C. To update training data
D. To visualize model performance

Correct answer: B.

2. Which component is responsible for updating model weights during training?
A. Loss function
B. Activation function
C. Optimizer
D. Metric

Correct answer: C.

3. What does an epoch represent during model training?
A. A single weight update
B. One forward pass only
C. One complete pass over the training dataset
D. One mini-batch

Correct answer: C.

4. Which activation function is commonly used in hidden layers to mitigate vanishing gradients?
A. Sigmoid
B. Tanh
C. ReLU
D. Softmax

Correct answer: C.

5. What is the main role of the validation dataset?
A. To update model weights
B. To test final model performance
C. To tune hyperparameters and monitor overfitting
D. To normalize input data

Correct answer: C.

6. Which technique randomly disables neurons during training to reduce overfitting?
A. Batch normalization
B. Dropout
C. Data augmentation
D. Early stopping

Correct answer: B.

7. What problem does regularization primarily address?
A. Underfitting
B. Exploding gradients
C. Overfitting
D. Data leakage

Correct answer: C.

8. Which type of neural network is best suited for image data?
A. Recurrent Neural Network
B. Fully Connected Network
C. Convolutional Neural Network
D. Autoencoder

Correct answer: C.

9. What is the purpose of convolutional filters in CNNs?
A. To reduce dataset size
B. To detect local patterns in data
C. To normalize pixel values
D. To perform classification directly

Correct answer: B.

10. What does pooling primarily achieve in convolutional neural networks?
A. Increases spatial resolution
B. Reduces overfitting by adding noise
C. Reduces spatial dimensions and computation
D. Converts images to vectors

Correct answer: C.

11. Which loss function is most appropriate for multi-class classification?
A. Mean Squared Error
B. Binary Crossentropy
C. Categorical Crossentropy
D. Hinge Loss

Correct answer: C.

12. What is a common symptom of overfitting?
A. High training loss and high validation loss
B. Low training loss and high validation loss
C. High training accuracy and low training loss
D. Low training accuracy and low validation accuracy

Correct answer: B.

13. What does backpropagation compute?
A. Model predictions
B. Loss values only
C. Gradients of the loss with respect to weights
D. Input feature scaling

Correct answer: C.

14. Which Keras method is used to define the training configuration of a model?
A. fit()
B. compile()
C. evaluate()
D. predict()

Correct answer: B.

15. What is transfer learning primarily based on?
A. Training from scratch on small datasets
B. Reusing pre-trained models or layers
C. Random weight initialization
D. Increasing model depth

Correct answer: B.

16. Which type of layer is used to flatten multidimensional input into a vector?
A. Dense
B. Conv2D
C. Flatten
D. Dropout

Correct answer: C.

17. What is the main advantage of mini-batch gradient descent?
A. Exact gradient computation
B. No memory usage
C. Faster convergence with stable updates
D. Eliminates need for an optimizer

Correct answer: C.

18. Which metric is commonly used to evaluate classification models?
A. Mean Absolute Error
B. R-squared
C. Accuracy
D. Perplexity

Correct answer: C.

19. What is the primary goal of early stopping?
A. Speed up data loading
B. Prevent overfitting by stopping training at the right time
C. Increase model capacity
D. Improve gradient flow

Correct answer: B.

20. Which framework is primarily used in the book to implement deep learning models?
A. PyTorch
B. Scikit-learn
C. Keras with TensorFlow backend
D. MXNet

Correct answer: C.

https://t.me/DataScienceM

1. What is the main numerical reason batch normalization accelerates training?
A. It increases model capacity
B. It reduces internal covariate shift
C. It removes the need for regularization
D. It replaces activation functions

Correct answer: B.

2. Why are sigmoid activations problematic in deep networks?
A. They are non-differentiable
B. They produce sparse activations
C. They saturate and cause vanishing gradients
D. They require large learning rates

Correct answer: C.

3. What happens when the learning rate is set too high?
A. Training converges slowly
B. The model overfits
C. The loss oscillates or diverges
D. Gradients vanish

Correct answer: C.

4. In convolutional layers, what determines the receptive field size?
A. Number of filters
B. Kernel size and depth
C. Activation function
D. Optimizer type

Correct answer: B.

5. Why is weight sharing important in CNNs?
A. It increases model depth
B. It reduces computational cost and parameters
C. It improves gradient descent accuracy
D. It prevents exploding gradients

Correct answer: B.

6. What is the primary function of padding in convolutional networks?
A. Increase number of channels
B. Reduce overfitting
C. Preserve spatial dimensions
D. Normalize input values

Correct answer: C.

7. Which condition most strongly indicates data leakage?
A. High training accuracy
B. Low training loss
C. Validation performance better than training
D. Slow convergence

Correct answer: C.

8. Why are recurrent neural networks difficult to train on long sequences?
A. High memory usage
B. Nonlinear activations
C. Vanishing and exploding gradients
D. Large batch sizes

Correct answer: C.

9. What architectural feature allows LSTMs to mitigate vanishing gradients?
A. Residual connections
B. Gated cell state
C. Dropout layers
D. Weight decay

Correct answer: B.

10. In sequence modeling, what does teacher forcing refer to?
A. Using larger batch sizes
B. Feeding ground-truth outputs during training
C. Freezing embedding layers
D. Shuffling time steps

Correct answer: B.

11. Why is softmax unsuitable for multi-label classification?
A. It is not differentiable
B. It enforces mutually exclusive class probabilities
C. It cannot handle sparse targets
D. It causes gradient explosion

Correct answer: B.

12. What does L2 regularization mathematically penalize?
A. Absolute values of weights
B. Squared magnitude of weights
C. Number of parameters
D. Gradient variance

Correct answer: B.

13. Why does mean squared error perform poorly for classification?
A. It is computationally expensive
B. It ignores class imbalance
C. It provides weak gradients for confident wrong predictions
D. It cannot be minimized

Correct answer: C.

14. What is the main advantage of global average pooling?
A. Increases spatial resolution
B. Adds trainable parameters
C. Reduces overfitting by eliminating dense layers
D. Improves gradient flow

Correct answer: C.

15. Why are pretrained embeddings useful in NLP tasks?
A. They reduce input sequence length
B. They encode semantic relationships learned from large corpora
C. They eliminate the need for tokenization
D. They prevent overfitting entirely

Correct answer: B.

16. What does gradient clipping primarily prevent?
A. Overfitting
B. Vanishing gradients
C. Exploding gradients
D. Data leakage

Correct answer: C.

17. Why is shuffling training data between epochs important?
A. To increase batch size
B. To improve memory usage
C. To reduce bias in gradient updates
D. To stabilize validation loss

Correct answer: C.

18. What is the main risk of excessive model capacity?
A. Slow inference
B. Underfitting
C. Overfitting
D. Numerical instability

Correct answer: C.

19. Why is cross-entropy preferred over accuracy as a training objective?
A. Accuracy is non-differentiable
B. Accuracy requires larger datasets
C. Cross-entropy reduces model size
D. Cross-entropy prevents overfitting

Correct answer: A.

20. What is the core assumption behind convolutional neural networks?
A. Features are independent
B. Data is linearly separable
C. Local patterns are spatially correlated
D. Labels are mutually exclusive

Correct answer: C.

https://t.me/DataScienceM