📌 The Proximity of the Inception Score as an Evaluation Criterion

🗂 Category: DEEP LEARNING

🕒 Date: 2026-02-03 | ⏱️ Read time: 7 min read

The neighborhood of synthetic data

#DataScience #AI #Python

📌 Routing in a Sparse Graph: a Distributed Q-Learning Approach

🗂 Category: MACHINE LEARNING

🕒 Date: 2026-02-03 | ⏱️ Read time: 10 min read

Distributed agents need only decide one move ahead.

#DataScience #AI #Python

📌 YOLOv2 & YOLO9000 Paper Walkthrough: Better, Faster, Stronger

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2026-02-03 | ⏱️ Read time: 24 min read

From YOLOv1 to YOLOv2: prior box, k-means, Darknet-19, passthrough layer, and more

#DataScience #AI #Python

📌 Creating a Data Pipeline to Monitor Local Crime Trends

🗂 Category: DATA SCIENCE

🕒 Date: 2026-02-03 | ⏱️ Read time: 19 min read

A walkthough of creating an ETL pipeline to extract local crime data and visualize it…

#DataScience #AI #Python

🎯 Want to Upskill in IT? Try Our FREE 2026 Learning Kits!

SPOTO gives you free, instant access to high-quality, updated resources that help you study smarter and pass exams faster.
✅ Latest Exam Materials:
Covering #Python, #Cisco, #PMI, #Fortinet, #AWS, #Azure, #AI, #Excel, #comptia, #ITIL, #cloud & more!
✅ 100% Free, No Sign-up:
All materials are instantly downloadable

✅ What’s Inside:
・📘IT Certs E-book: https://bit.ly/3Mlu5ez
・📝IT Exams Skill Test: https://bit.ly/3NVrgRU
・🎓Free IT courses: https://bit.ly/3M9h5su
・🤖Free PMP Study Guide: https://bit.ly/4te3EIn
・☁️Free Cloud Study Guide: https://bit.ly/4kgFVDs

👉 Become Part of Our IT Learning Circle! resources and support:
https://chat.whatsapp.com/FlG2rOYVySLEHLKXF3nKGB

💬 Want exam help? Chat with an admin now!
wa.link/8fy3x4

📌 How to Work Effectively with Frontend and Backend Code

🗂 Category: LLM APPLICATIONS

🕒 Date: 2026-02-04 | ⏱️ Read time: 6 min read

Learn how to be an effective full-stack engineer with Claude Code

#DataScience #AI #Python

📌 How to Build Your Own Custom LLM Memory Layer from Scratch

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2026-02-04 | ⏱️ Read time: 16 min read

Step-by-step guide to building autonomous memory retrieval systems

#DataScience #AI #Python

📌 Plan–Code–Execute: Designing Agents That Create Their Own Tools

🗂 Category: AGENTIC AI

🕒 Date: 2026-02-04 | ⏱️ Read time: 24 min read

The case against pre-built tools in Agentic Architectures

#DataScience #AI #Python

📌 AWS vs. Azure: A Deep Dive into Model Training – Part 2

🗂 Category: DATA SCIENCE

🕒 Date: 2026-02-04 | ⏱️ Read time: 12 min read

This article covers how Azure ML’s persistent, workspace-centric compute resources differ from AWS SageMaker’s on-demand,…

#DataScience #AI #Python

📌 Mechanistic Interpretability: Peeking Inside an LLM

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2026-02-05 | ⏱️ Read time: 19 min read

Are the human-like cognitive abilities of LLMs real or fake? How does information travel through…

#DataScience #AI #Python

📌 Why Is My Code So Slow? A Guide to Py-Spy Python Profiling

🗂 Category: PROGRAMMING

🕒 Date: 2026-02-05 | ⏱️ Read time: 10 min read

Stop guessing and start diagnosing performance issues using Py-Spy

#DataScience #AI #Python

📌 The Rule Everyone Misses: How to Stop Confusing loc and iloc in Pandas

🗂 Category: DATA SCIENCE

🕒 Date: 2026-02-05 | ⏱️ Read time: 9 min read

A simple mental model to remember when each one works (with examples that finally click).

#DataScience #AI #Python

📌 Pydantic Performance: 4 Tips on How to Validate Large Amounts of Data Efficiently

🗂 Category: DATA ENGINEERING

🕒 Date: 2026-02-06 | ⏱️ Read time: 8 min read

The real value lies in writing clearer code and using your tools right

#DataScience #AI #Python

📌 Prompt Fidelity: Measuring How Much of Your Intent an AI Agent Actually Executes

🗂 Category: AGENTIC AI

🕒 Date: 2026-02-06 | ⏱️ Read time: 32 min read

How much of your AI agent’s output is real data versus confident guesswork?

#DataScience #AI #Python

📌 What I Am Doing to Stay Relevant as a Senior Analytics Consultant in 2026

🗂 Category: DATA ANALYSIS

🕒 Date: 2026-02-07 | ⏱️ Read time: 7 min read

Learn how to work with AI, while strengthening your unique human skills that technology cannot…

#DataScience #AI #Python

🚀 Machine Learning Workflow: Step-by-Step Breakdown
Understanding the ML pipeline is essential to build scalable, production-grade models.

👉 Initial Dataset
Start with raw data. Apply cleaning, curation, and drop irrelevant or redundant features.
Example: Drop constant features or remove columns with 90% missing values.

👉 Exploratory Data Analysis (EDA)
Use mean, median, standard deviation, correlation, and missing value checks.
Techniques like PCA and LDA help with dimensionality reduction.
Example: Use PCA to reduce 50 features down to 10 while retaining 95% variance.

👉 Input Variables
Structured table with features like ID, Age, Income, Loan Status, etc.
Ensure numeric encoding and feature engineering are complete before training.

👉 Processed Dataset
Split the data into training (70%) and testing (30%) sets.
Example: Stratified sampling ensures target distribution consistency.

👉 Learning Algorithms
Apply algorithms like SVM, Logistic Regression, KNN, Decision Trees, or Ensemble models like Random Forest and Gradient Boosting.
Example: Use Random Forest to capture non-linear interactions in tabular data.

👉 Hyperparameter Optimization
Tune parameters using Grid Search or Random Search for better performance.
Example: Optimize max_depth and n_estimators in Gradient Boosting.

👉 Feature Selection
Use model-based importance ranking (e.g., from Random Forest) to remove noisy or irrelevant features.
Example: Drop features with zero importance to reduce overfitting.

👉 Model Training and Validation
Use cross-validation to evaluate generalization. Train final model on full training set.
Example: 5-fold cross-validation for reliable performance metrics.

👉 Model Evaluation
Use task-specific metrics:
- Classification – MCC, Sensitivity, Specificity, Accuracy
- Regression – RMSE, R², MSE
Example: For imbalanced classes, prefer MCC over simple accuracy.

💡 This workflow ensures models are robust, interpretable, and ready for deployment in real-world applications.

https://news.1rj.ru/str/DataScienceM

📌 The Death of the “Everything Prompt”: Google’s Move Toward Structured AI

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2026-02-09 | ⏱️ Read time: 16 min read

How the new Interactions API enables deep-reasoning, stateful, agentic workflows.

#DataScience #AI #Python

📌 The Machine Learning Lessons I’ve Learned Last Month

🗂 Category: MACHINE LEARNING

🕒 Date: 2026-02-09 | ⏱️ Read time: 5 min read

Delayed January: deadlines, downtimes, and flow times

#DataScience #AI #Python