Machine Learning

📌 Orchestrating a Dynamic Time-series Pipeline in Azure 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-05-31 | ⏱️ Read time: 9 min read Explore how to build, trigger, and parameterize a time-series data pipeline with ADF and Databricks,…

📌 Bringing Vision-Language Intelligence to RAG with ColPali 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-10-29 | ⏱️ Read time: 8 min read Unlocking the value of non-textual contents in your knowledge base

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📌 4 Techniques to Optimize Your LLM Prompts for Cost, Latency and Performance 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-10-29 | ⏱️ Read time: 8 min read Learn how to greatly improve the performance of your LLM application

💡 SciPy: Scientific Computing in Python SciPy is a fundamental library for scientific and technical computing in Python. Built on NumPy, it provides a wide range of user-friendly and efficient numerical routines for tasks like optimization, integration, linear algebra, and statistics.

import numpy as np
from scipy.optimize import minimize

# Define a function to minimize: f(x) = (x - 3)^2
def f(x):
    return (x - 3)**2

# Find the minimum of the function with an initial guess
res = minimize(f, x0=0)

print(f"Minimum found at x = {res.x[0]:.4f}")
# Output:
# Minimum found at x = 3.0000

• Optimization: scipy.optimize.minimize is used to find the minimum value of a function. • We provide the function (f) and an initial guess (x0=0). • The result object (res) contains the solution in the .x attribute.

from scipy.integrate import quad

# Define the function to integrate: f(x) = sin(x)
def integrand(x):
    return np.sin(x)

# Integrate sin(x) from 0 to pi
result, error = quad(integrand, 0, np.pi)

print(f"Integral result: {result:.4f}")
print(f"Estimated error: {error:.2e}")
# Output:
# Integral result: 2.0000
# Estimated error: 2.22e-14

• Numerical Integration: scipy.integrate.quad calculates the definite integral of a function over a given interval. • It returns a tuple containing the integral result and an estimate of the absolute error.

from scipy.linalg import solve

# Solve the linear system Ax = b
# 3x + 2y = 12
#  x -  y = 1

A = np.array([[3, 2], [1, -1]])
b = np.array([12, 1])

solution = solve(A, b)
print(f"Solution (x, y): {solution}")
# Output:
# Solution (x, y): [2.8 1.8]

• Linear Algebra: scipy.linalg provides more advanced linear algebra routines than NumPy. • solve(A, b) efficiently finds the solution vector x for a system of linear equations defined by a matrix A and a vector b.

from scipy import stats

# Create two independent samples
sample1 = np.random.normal(loc=5, scale=2, size=100)
sample2 = np.random.normal(loc=5.5, scale=2, size=100)

# Perform an independent t-test
t_stat, p_value = stats.ttest_ind(sample1, sample2)

print(f"T-statistic: {t_stat:.4f}")
print(f"P-value: {p_value:.4f}")
# Output (will vary):
# T-statistic: -1.7432
# P-value: 0.0829

• Statistics: scipy.stats is a powerful module for statistical analysis. • ttest_ind calculates the T-test for the means of two independent samples. • The p-value helps determine if the difference between sample means is statistically significant (a low p-value, e.g., < 0.05, suggests it is). #SciPy #Python #DataScience #ScientificComputing #Statistics ━━━━━━━━━━━━━━━ By: @DataScienceM ✨

💡 Pandas Cheatsheet A quick guide to essential Pandas operations for data manipulation, focusing on creating, selecting, filtering, and grouping data in a DataFrame. 1. Creating a DataFrame The primary data structure in Pandas is the DataFrame. It's often created from a dictionary.

import pandas as pd

data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 32, 28],
        'City': ['New York', 'Paris', 'New York']}
df = pd.DataFrame(data)

print(df)
#       Name  Age       City
# 0    Alice   25   New York
# 1      Bob   32      Paris
# 2  Charlie   28   New York

• A dictionary is defined where keys become column names and values become the data in those columns. pd.DataFrame() converts it into a tabular structure. 2. Selecting Data with .loc and .iloc Use .loc for label-based selection and .iloc for integer-position based selection.

# Select the first row by its integer position (0)
print(df.iloc[0])

# Select the row with index label 1 and only the 'Name' column
print(df.loc[1, 'Name'])

# Output for df.iloc[0]:
# Name       Alice
# Age           25
# City    New York
# Name: 0, dtype: object
#
# Output for df.loc[1, 'Name']:
# Bob

• .iloc[0] gets all data from the row at index position 0. • .loc[1, 'Name'] gets the data at the intersection of index label 1 and column label 'Name'. 3. Filtering Data Select subsets of data based on conditions.

# Select rows where Age is greater than 27
filtered_df = df[df['Age'] > 27]
print(filtered_df)
#       Name  Age       City
# 1      Bob   32      Paris
# 2  Charlie   28   New York

• The expression df['Age'] > 27 creates a boolean Series (True/False). • Using this Series as an index df[...] returns only the rows where the value was True. 4. Grouping and Aggregating The "group by" operation involves splitting data into groups, applying a function, and combining the results.

# Group by 'City' and calculate the mean age for each city
city_ages = df.groupby('City')['Age'].mean()
print(city_ages)
# City
# New York    26.5
# Paris       32.0
# Name: Age, dtype: float64

• .groupby('City') splits the DataFrame into groups based on unique city values. • ['Age'].mean() then calculates the mean of the 'Age' column for each of these groups. #Python #Pandas #DataAnalysis #DataScience #Programming ━━━━━━━━━━━━━━━ By: @DataScienceM ✨

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📌 “Systems thinking helps me put the big picture front and center” 🗂 Category: AUTHOR SPOTLIGHTS 🕒 Date: 2025-10-30 | ⏱️ Read time: 6 min read Shuai Guo on deep research agents, analytical AI vs LLM-based agents, and systems thinking

📌 Build LLM Agents Faster with Datapizza AI 🗂 Category: AGENTIC AI 🕒 Date: 2025-10-30 | ⏱️ Read time: 8 min read Intro Organizations are increasingly investing in AI as these new tools are adopted in everyday…

🤖🧠 MiniMax-M2: The Open-Source Revolution Powering Coding and Agentic Intelligence 🗓️ 30 Oct 2025 📚 AI News & Trends Artificial intelligence is evolving faster than ever, but not every innovation needs to be enormous to make an impact. MiniMax-M2, the latest release from MiniMax-AI, demonstrates that efficiency and power can coexist within a streamlined framework. MiniMax-M2 is an open-source Mixture of Experts (MoE) model designed for coding tasks, multi-agent collaboration and automation workflows. With ... #MiniMaxM2 #OpenSource #MachineLearning #CodingAI #AgenticIntelligence #MixtureOfExperts

🤖🧠 MLOps Basics: A Complete Guide to Building, Deploying and Monitoring Machine Learning Models 🗓️ 30 Oct 2025 📚 AI News & Trends Machine Learning models are powerful but building them is only half the story. The true challenge lies in deploying, scaling and maintaining these models in production environments – a process that requires collaboration between data scientists, developers and operations teams. This is where MLOps (Machine Learning Operations) comes in. MLOps combines the principles of DevOps ... #MLOps #MachineLearning #DevOps #ModelDeployment #DataScience #ProductionAI

📌 Building a Rules Engine from First Principles 🗂 Category: ALGORITHMS 🕒 Date: 2025-10-30 | ⏱️ Read time: 17 min read How recasting propositional logic as sparse algebra leads to an elegant and efficient design

🤖🧠 Reflex: Build Full-Stack Web Apps in Pure Python — Fast, Flexible and Powerful 🗓️ 29 Oct 2025 📚 AI News & Trends Building modern web applications has traditionally required mastering multiple languages and frameworks from JavaScript for the frontend to Python, Java or Node.js for the backend. For many developers, switching between different technologies can slow down productivity and increase complexity. Reflex eliminates that problem. It is an innovative open-source full-stack web framework that allows developers to ... #Reflex #FullStack #WebDevelopment #Python #OpenSource #WebApps

📌 Automating Data Pipelines with Python & GitHub Actions 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-05-30 | ⏱️ Read time: 10 min read A simple (and free) way to run data workflows

📌 What 10 Years at Uber, Meta and Startups Taught Me About Data Analytics 🗂 Category: CAREER ADVICE 🕒 Date: 2024-05-30 | ⏱️ Read time: 11 min read Advice for Data Scientists and Managers

This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

📌 Interpretable Features in Large Language Models 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024-05-30 | ⏱️ Read time: 9 min read And other interesting tidbits from the new Anthropic Paper

📌 PyTorch Introduction – Training a Computer Vision Algorithm 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-05-30 | ⏱️ Read time: 11 min read In this post of the PyTorch Introduction, we’ll learn how to train a computer vision…

📌 Computing Minimum Sample Size for A/B Tests in Statsmodels: How and Why 🗂 Category: DATA SCIENCE 🕒 Date: 2024-05-31 | ⏱️ Read time: 11 min read A deep-dive into how and why Statsmodels uses numerical optimization instead of closed-form formulas

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