📌 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…

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📌 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

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Machine Learning Fundamentals

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

https://news.1rj.ru/str/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")

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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…

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📌 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…

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📌 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!

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📌 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,…

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📌 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

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📌 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…

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📌 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…

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📌 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

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