🤖🧠 Wren AI: Transforming Business Intelligence with Generative AI

🗓️ 28 Oct 2025
📚 AI News & Trends

In the evolving world of data and analytics, one thing is certain — the ability to transform raw data into actionable insights defines success. Organizations today are generating more data than ever before, yet accessing and understanding that data remains a significant challenge. Traditional business intelligence tools require technical expertise, SQL knowledge and manual configuration. ...

#WrenAI #GenerativeAI #BusinessIntelligence #DataAnalytics #AI #Insights

📌 Deep Reinforcement Learning: 0 to 100

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2025-10-28 | ⏱️ Read time: 24 min read

Using RL to teach robots to fly a drone

💡 Building a Simple Convolutional Neural Network (CNN)

Constructing a basic Convolutional Neural Network (CNN) is a fundamental step in deep learning for image processing. Using TensorFlow's Keras API, we can define a network with convolutional, pooling, and dense layers to classify images. This example sets up a simple CNN to recognize handwritten digits from the MNIST dataset.

import tensorflow as tf
from tensorflow.keras import layers, models
from tensorflow.keras.datasets import mnist
import numpy as np

# 1. Load and preprocess the MNIST dataset
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

# Reshape images for CNN: (batch_size, height, width, channels)
# MNIST images are 28x28 grayscale, so channels = 1
train_images = train_images.reshape((60000, 28, 28, 1)).astype('float32') / 255
test_images = test_images.reshape((10000, 28, 28, 1)).astype('float32') / 255

# 2. Define the CNN architecture
model = models.Sequential()

# First Convolutional Block
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))

# Second Convolutional Block
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))

# Flatten the 3D output to 1D for the Dense layers
model.add(layers.Flatten())

# Dense (fully connected) layers
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax')) # Output layer for 10 classes (digits 0-9)

# 3. Compile the model
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# Print a summary of the model layers
model.summary()

# 4. Train the model (uncomment to run training)
# print("\nTraining the model...")
# model.fit(train_images, train_labels, epochs=5, batch_size=64, validation_split=0.1)

# 5. Evaluate the model (uncomment to run evaluation)
# print("\nEvaluating the model...")
# test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
# print(f"Test accuracy: {test_acc:.4f}")

Code explanation: This script defines a simple CNN using Keras. It loads and normalizes MNIST images. The Sequential model adds Conv2D layers for feature extraction, MaxPooling2D for downsampling, a Flatten layer to transition to 1D, and Dense layers for classification. The model is then compiled with an optimizer, loss function, and metrics, and a summary of its architecture is printed. Training and evaluation steps are included as commented-out examples.

#Python #DeepLearning #CNN #Keras #TensorFlow

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By: @DataScienceM ✨

💡 Python: Simple K-Means Clustering Project

K-Means is a popular unsupervised machine learning algorithm used to partition n observations into k clusters, where each observation belongs to the cluster with the nearest mean (centroid). This simple project demonstrates K-Means on the classic Iris dataset using scikit-learn to group similar flower species based on their measurements.

import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
import numpy as np

# 1. Load the Iris dataset
iris = load_iris()
X = iris.data # Features (sepal length, sepal width, petal length, petal width)
y = iris.target # True labels (0, 1, 2 for different species) - not used by KMeans

# 2. (Optional but recommended) Scale the features
# K-Means is sensitive to the scale of features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# 3. Define and train the K-Means model
# We know there are 3 species in Iris, so we set n_clusters=3
kmeans = KMeans(n_clusters=3, random_state=42, n_init=10) # n_init is important for robust results
kmeans.fit(X_scaled)

# 4. Get the cluster assignments for each data point
labels = kmeans.labels_

# 5. Get the coordinates of the cluster centroids
centroids = kmeans.cluster_centers_

# 6. Visualize the clusters (using first two features for simplicity)
plt.figure(figsize=(8, 6))

# Plot each cluster
colors = ['red', 'green', 'blue']
for i in range(3):
    plt.scatter(X_scaled[labels == i, 0], X_scaled[labels == i, 1],
                s=50, c=colors[i], label=f'Cluster {i+1}', alpha=0.7)

# Plot the centroids
plt.scatter(centroids[:, 0], centroids[:, 1],
            s=200, marker='X', c='black', label='Centroids', edgecolor='white')

plt.title('K-Means Clustering on Iris Dataset (Scaled Features)')
plt.xlabel('Scaled Sepal Length')
plt.ylabel('Scaled Sepal Width')
plt.legend()
plt.grid(True)
plt.show()

# You can also compare with true labels (for evaluation, not part of clustering process itself)
# print("True labels:", y)
# print("K-Means labels:", labels)

Code explanation: This script loads the Iris dataset, scales its features using StandardScaler, and then applies KMeans to group the data into 3 clusters. It visualizes the resulting clusters and their centroids using a scatter plot with the first two scaled features.

#Python #MachineLearning #KMeans #Clustering #DataScience

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By: @DataScienceM ✨

📌 Using Claude Skills with Neo4j

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2025-10-28 | ⏱️ Read time: 11 min read

A hands-on exploration of Claude Skills and their potential applications in Neo4j

📌 Water Cooler Small Talk, Ep. 9: What “Thinking” and “Reasoning” Really Mean in AI and LLMs

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2025-10-28 | ⏱️ Read time: 9 min read

Understanding how AI models “reason” and why it’s not what humans do when we think

📌 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,…

📌 Training Naive Bayes… Really Fast

🗂 Category: MACHINE LEARNING

🕒 Date: 2024-05-31 | ⏱️ Read time: 14 min read

Performance tuning in Julia

📌 Terraforming Dataform

🗂 Category: DATA ENGINEERING

🕒 Date: 2024-05-31 | ⏱️ Read time: 7 min read

Dataform 101, Part 2: Provisioning with Least Privilege Access Control

🧠 Quiz: What is the primary objective of data mining?

A) To physically store large volumes of data
B) To discover patterns, trends, and useful insights from large datasets
C) To design and implement database management systems
D) To encrypt and secure sensitive data

✅ Correct answer: B

Explanation: Data mining is a process used to extract valuable, previously unknown patterns, trends, and knowledge from large datasets. Its goal is to find actionable insights that can inform decision-making.

#DataMining #BigData #Analytics

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By: @DataScienceM ✨

📌 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

📌 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…

📌 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

📌 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

📌 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

🤖🧠 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

📌 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

🤖🧠 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

🤖🧠 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

📌 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…