<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Xray AI Lab</title><description>Tutorials and notes on mathematics, Python, data science, machine learning and deep learning. Each article starts from the math and ends with code.</description><link>https://www.xrayailab.com/</link><language>en</language><item><title>AI Agents Explained: How to Build an LLM Agent with Python</title><link>https://www.xrayailab.com/blog/ai-agents-explained-build-llm-agent-python/</link><guid isPermaLink="true">https://www.xrayailab.com/blog/ai-agents-explained-build-llm-agent-python/</guid><description>AI agents explained: learn how LLM agents use tools, state, and a loop, then build a safe local agent in Python with Ollama, tests, and security guardrails.</description><pubDate>Sun, 25 Oct 2026 00:00:00 GMT</pubDate><category>NLP &amp; Generative AI</category></item><item><title>How to Evaluate LLMs: A Practical Guide to Testing AI Responses</title><link>https://www.xrayailab.com/blog/how-to-evaluate-llms-practical-guide/</link><guid isPermaLink="true">https://www.xrayailab.com/blog/how-to-evaluate-llms-practical-guide/</guid><description>Learn how to evaluate LLM responses with test sets, code-based graders, human review, and LLM-as-a-judge, plus a runnable Python harness and common pitfalls.</description><pubDate>Sun, 25 Oct 2026 00:00:00 GMT</pubDate><category>NLP &amp; Generative AI</category><category>LLM Evaluation</category><category>LLM-as-a-Judge</category><category>Python</category><category>Prompt Engineering</category><category>Testing</category></item><item><title>Backpropagation Explained: How Neural Networks Learn</title><link>https://www.xrayailab.com/blog/backpropagation-explained-how-neural-networks-learn/</link><guid isPermaLink="true">https://www.xrayailab.com/blog/backpropagation-explained-how-neural-networks-learn/</guid><description>Learn how backpropagation works: the chain rule, a fully worked numeric example, a NumPy implementation with gradient checking, and common pitfalls.</description><pubDate>Sat, 24 Oct 2026 00:00:00 GMT</pubDate><category>Deep Learning</category><category>Backpropagation</category><category>Neural Networks</category><category>Gradient Descent</category><category>Chain Rule</category><category>Python</category></item><item><title>Data Cleaning with Python and Pandas: A Complete Practical Guide</title><link>https://www.xrayailab.com/blog/data-cleaning-python-pandas-guide/</link><guid isPermaLink="true">https://www.xrayailab.com/blog/data-cleaning-python-pandas-guide/</guid><description>Learn a repeatable pandas data cleaning workflow: audit, fix text and types, parse dates, handle invalid values, remove duplicates, flag outliers, and validate.</description><pubDate>Sat, 24 Oct 2026 00:00:00 GMT</pubDate><category>Data Science</category><category>Data Cleaning</category><category>Pandas</category><category>Python</category><category>Data Preprocessing</category><category>Data Quality</category></item><item><title>Data Quality in Data Science: How to Detect and Fix Bad Data with Python</title><link>https://www.xrayailab.com/blog/data-quality-python-detect-fix-bad-data/</link><guid isPermaLink="true">https://www.xrayailab.com/blog/data-quality-python-detect-fix-bad-data/</guid><description>Learn to measure data quality across six dimensions, detect and fix bad data with pandas, enforce rules with pandera, and see how bad data hurts models.</description><pubDate>Sat, 24 Oct 2026 00:00:00 GMT</pubDate><category>Data Science</category><category>Data Quality</category><category>Data Validation</category><category>Pandas</category><category>Pandera</category><category>Python</category><category>Data Cleaning</category></item><item><title>Exploratory Data Analysis (EDA) with Python: A Complete Project</title><link>https://www.xrayailab.com/blog/eda-python-complete-project/</link><guid isPermaLink="true">https://www.xrayailab.com/blog/eda-python-complete-project/</guid><description>Run a complete exploratory data analysis in Python with pandas and seaborn on the Palmer Penguins data, from data quality checks to a Simpson&apos;s paradox.</description><pubDate>Sat, 24 Oct 2026 00:00:00 GMT</pubDate><category>Data Science</category><category>Exploratory Data Analysis</category><category>EDA</category><category>Pandas</category><category>Seaborn</category><category>Python</category><category>Data Visualization</category></item><item><title>Probability for Machine Learning: Bayes &amp; Distributions</title><link>https://www.xrayailab.com/blog/probability-for-machine-learning-bayes-theorem-distributions/</link><guid isPermaLink="true">https://www.xrayailab.com/blog/probability-for-machine-learning-bayes-theorem-distributions/</guid><description>Learn the probability behind machine learning: Bayes&apos; theorem, key distributions, MLE and MAP, with worked examples and Python code you can run and check.</description><pubDate>Sat, 24 Oct 2026 00:00:00 GMT</pubDate><category>Mathematics</category><category>Probability</category><category>Bayes&apos; Theorem</category><category>Probability Distributions</category><category>Machine Learning Math</category><category>Maximum Likelihood</category><category>Python</category></item><item><title>Agentic AI in 2026: What Research Reveals About Autonomous Agents</title><link>https://www.xrayailab.com/blog/agentic-ai-2026-research-autonomous-agents/</link><guid isPermaLink="true">https://www.xrayailab.com/blog/agentic-ai-2026-research-autonomous-agents/</guid><description>Agentic AI explained through research: agent architectures, planning, tools, memory, what autonomous agents can really do in 2026, and where they still fail.</description><pubDate>Fri, 23 Oct 2026 00:00:00 GMT</pubDate><category>Research Notes</category><category>agentic ai</category><category>ai agents</category><category>llm agents</category><category>react</category><category>tool use</category><category>agent memory</category><category>ai research</category></item><item><title>Calculus for Machine Learning: Derivatives, Partial Derivatives, and Gradients</title><link>https://www.xrayailab.com/blog/calculus-for-machine-learning-derivatives-partial-derivatives-gradients/</link><guid isPermaLink="true">https://www.xrayailab.com/blog/calculus-for-machine-learning-derivatives-partial-derivatives-gradients/</guid><description>Learn derivatives visually, then see how partial derivatives and gradients drive neural-network training, with worked examples and verified Python code.</description><pubDate>Fri, 23 Oct 2026 00:00:00 GMT</pubDate><category>Mathematics</category><category>Calculus</category><category>Gradients</category><category>Gradient Descent</category><category>Backpropagation</category><category>Machine Learning Math</category></item><item><title>Computer Vision Explained: Tasks, Models, and Applications</title><link>https://www.xrayailab.com/blog/computer-vision-explained/</link><guid isPermaLink="true">https://www.xrayailab.com/blog/computer-vision-explained/</guid><description>What is computer vision? Learn the core tasks, the key models from CNNs to Vision Transformers, real applications, and runnable Python examples.</description><pubDate>Fri, 23 Oct 2026 00:00:00 GMT</pubDate><category>Computer Vision</category><category>computer vision</category><category>deep learning</category><category>object detection</category><category>image segmentation</category><category>vision transformers</category><category>python</category></item><item><title>Gradient Descent Explained with Python: Math, Visualization, and Code</title><link>https://www.xrayailab.com/blog/gradient-descent-explained-python-math-visualization-code/</link><guid isPermaLink="true">https://www.xrayailab.com/blog/gradient-descent-explained-python-math-visualization-code/</guid><description>Learn how gradient descent minimizes a loss function. Derive the gradients, code it in NumPy, and test learning rates with a plotted experiment.</description><pubDate>Fri, 23 Oct 2026 00:00:00 GMT</pubDate><category>Mathematics</category><category>Gradient Descent</category><category>Optimization</category><category>Linear Regression</category><category>Python</category><category>NumPy</category><category>Mathematics for Machine Learning</category></item><item><title>Linear Algebra for Machine Learning: Vectors, Matrices, and Eigenvalues (with NumPy)</title><link>https://www.xrayailab.com/blog/linear-algebra-for-machine-learning/</link><guid isPermaLink="true">https://www.xrayailab.com/blog/linear-algebra-for-machine-learning/</guid><description>Learn linear algebra for machine learning: vectors, dot products, matrix multiplication, and eigenvalues, with runnable NumPy examples and PCA.</description><pubDate>Fri, 23 Oct 2026 00:00:00 GMT</pubDate><category>Mathematics</category><category>linear algebra</category><category>machine learning</category><category>numpy</category><category>eigenvalues</category><category>pca</category><category>python</category></item><item><title>Real-Time Object Detection with YOLO and Python</title><link>https://www.xrayailab.com/blog/real-time-object-detection-yolo-python/</link><guid isPermaLink="true">https://www.xrayailab.com/blog/real-time-object-detection-yolo-python/</guid><description>Learn real-time object detection with YOLO in Python: bounding boxes, confidence scores, video inference, and tracking objects across frames.</description><pubDate>Fri, 23 Oct 2026 00:00:00 GMT</pubDate><category>Computer Vision</category><category>object detection</category><category>yolo</category><category>object tracking</category><category>real-time</category><category>computer vision</category><category>python</category><category>ultralytics</category></item><item><title>Image Segmentation with Segment Anything: A Python Tutorial</title><link>https://www.xrayailab.com/blog/segment-anything-python-tutorial/</link><guid isPermaLink="true">https://www.xrayailab.com/blog/segment-anything-python-tutorial/</guid><description>Learn image segmentation with Segment Anything (SAM) in Python: point and box prompts, masks, bounding boxes, automatic mode, and honest limitations.</description><pubDate>Fri, 23 Oct 2026 00:00:00 GMT</pubDate><category>Computer Vision</category><category>segment anything</category><category>SAM</category><category>image segmentation</category><category>computer vision</category><category>foundation models</category><category>python</category></item><item><title>Vector RAG vs. Graph RAG: What Does the Research Say?</title><link>https://www.xrayailab.com/blog/vector-rag-vs-graph-rag-research-experiment/</link><guid isPermaLink="true">https://www.xrayailab.com/blog/vector-rag-vs-graph-rag-research-experiment/</guid><description>Vector RAG vs Graph RAG compared: what the papers found, the trade-offs, and a small reproducible Python experiment on multi-hop retrieval.</description><pubDate>Fri, 23 Oct 2026 00:00:00 GMT</pubDate><category>Research Notes</category><category>rag</category><category>graph rag</category><category>vector search</category><category>retrieval augmented generation</category><category>knowledge graph</category><category>multi-hop qa</category><category>python</category></item><item><title>Vision-Language Models Explained: CLIP, Captioning, VQA</title><link>https://www.xrayailab.com/blog/vision-language-models-explained/</link><guid isPermaLink="true">https://www.xrayailab.com/blog/vision-language-models-explained/</guid><description>Learn how vision-language models work: image encoders, CLIP embeddings, captioning, and visual question answering, with a runnable Python demo.</description><pubDate>Fri, 23 Oct 2026 00:00:00 GMT</pubDate><category>Computer Vision</category><category>vision-language models</category><category>VLM</category><category>CLIP</category><category>image captioning</category><category>visual question answering</category><category>multimodal ai</category><category>python</category></item><item><title>Machine Learning for Threat Detection: A Python Guide</title><link>https://www.xrayailab.com/blog/machine-learning-threat-detection/</link><guid isPermaLink="true">https://www.xrayailab.com/blog/machine-learning-threat-detection/</guid><description>Learn how machine learning detects cyber threats, from supervised models to anomaly detection, with a runnable Python example and honest limits.</description><pubDate>Thu, 22 Oct 2026 00:00:00 GMT</pubDate><category>Machine Learning</category><category>cybersecurity</category><category>machine learning</category><category>anomaly detection</category><category>threat detection</category><category>Python</category><category>scikit-learn</category><category>isolation forest</category></item><item><title>RAG Explained: Build a Retrieval-Augmented Generation App with Python</title><link>https://www.xrayailab.com/blog/ragexplained/</link><guid isPermaLink="true">https://www.xrayailab.com/blog/ragexplained/</guid><description>Learn how Retrieval-Augmented Generation (RAG) works, why it outperforms basic LLMs for custom data, and how to build a working RAG app in Python.</description><pubDate>Thu, 22 Oct 2026 00:00:00 GMT</pubDate><category>NLP &amp; Generative AI</category><category>Generative AI</category><category>Python</category><category>Machine Learning</category><category>LLM</category><category>Data Science</category></item><item><title>AI Agents Explained: How They Work, Tools, and Real-World Examples in 2026</title><link>https://www.xrayailab.com/blog/ai-agents-explained/</link><guid isPermaLink="true">https://www.xrayailab.com/blog/ai-agents-explained/</guid><description>Learn how AI agents work, explore real-world applications, compare top frameworks, and build a simple AI agent with Python.</description><pubDate>Wed, 21 Oct 2026 00:00:00 GMT</pubDate><category>Tools &amp; Tutorials</category><category>Artificial Intelligence</category><category>AI Agents</category><category>Python</category><category>Machine Learning</category></item><item><title>Build Your First AI Agent with Python: A Beginner-Friendly Tutorial</title><link>https://www.xrayailab.com/blog/build-first-ai-agent-python/</link><guid isPermaLink="true">https://www.xrayailab.com/blog/build-first-ai-agent-python/</guid><description>Learn to build an AI agent with Python step by step. Set up your environment, implement tool calling, run the project, and troubleshoot common errors.</description><pubDate>Wed, 21 Oct 2026 00:00:00 GMT</pubDate><category>Tools &amp; Tutorials</category><category>Artificial Intelligence</category><category>Python</category><category>AI Agents</category><category>Tutorials</category></item><item><title>Data Cleaning with Python and Pandas: A Practical Guide - Update </title><link>https://www.xrayailab.com/blog/data-cleaning-python-pandas-practical-guide/</link><guid isPermaLink="true">https://www.xrayailab.com/blog/data-cleaning-python-pandas-practical-guide/</guid><description>Learn data cleaning with Python and pandas: fix text, dates, types, duplicates and missing values with a tested pipeline you can check against ground truth.</description><pubDate>Tue, 20 Oct 2026 00:00:00 GMT</pubDate><category>NLP &amp; Generative AI</category><category>Data Cleaning</category><category>pandas</category><category>Python</category><category>Data Preprocessing</category><category>Missing Values</category><category>Data Quality</category></item><item><title>Deep Learning Roadmap: What to Learn, in What Order, and Why</title><link>https://www.xrayailab.com/blog/deep-learning-roadmap-DL00/</link><guid isPermaLink="true">https://www.xrayailab.com/blog/deep-learning-roadmap-DL00/</guid><description>Discover a structured deep learning roadmap covering math prerequisites, environment setup, and dedicated tracks for computer vision, NLP, and generative AI.</description><pubDate>Sun, 18 Oct 2026 00:00:00 GMT</pubDate><category>Deep Learning</category><category>Deep Learning</category><category>Roadmap</category><category>PyTorch</category><category>Beginners</category><category>Neural Networks</category><category>Mathematics</category></item><item><title>Mathematics for Machine Learning: A Complete Study Map</title><link>https://www.xrayailab.com/blog/mathematics-for-machine-learning-study-map/</link><guid isPermaLink="true">https://www.xrayailab.com/blog/mathematics-for-machine-learning-study-map/</guid><description>A beginner-friendly roadmap to the mathematics behind machine learning: linear algebra, geometry, calculus, probability, and optimization, and how they connect.</description><pubDate>Sat, 17 Oct 2026 00:00:00 GMT</pubDate><category>Machine Learning</category><category>Mathematics</category><category>Machine Learning</category><category>Linear Algebra</category><category>Calculus</category><category>Probability</category><category>Optimization</category><category>Roadmap</category></item><item><title>Python for Data Science: A Beginner’s Guide</title><link>https://www.xrayailab.com/blog/python-for-data-science-beginners/</link><guid isPermaLink="true">https://www.xrayailab.com/blog/python-for-data-science-beginners/</guid><description>Learn Python for data science from scratch: core syntax, NumPy, Pandas, Matplotlib, Seaborn, scikit-learn, a student scores project, and a learning roadmap.</description><pubDate>Sat, 17 Oct 2026 00:00:00 GMT</pubDate><category>Data Science</category><category>Python</category><category>Data Science</category><category>NumPy</category><category>Pandas</category><category>Matplotlib</category><category>Seaborn</category><category>Scikit-learn</category><category>Beginners</category></item><item><title>Python Programming: A Complete Guide to Concepts, Examples, and Exercises</title><link>https://www.xrayailab.com/blog/python-programming-complete-guide/</link><guid isPermaLink="true">https://www.xrayailab.com/blog/python-programming-complete-guide/</guid><description>Learn Python with worked examples: functions, lambdas, recursion, classes, number problems, series, and patterns, plus 28 graded exercises with solutions.</description><pubDate>Sat, 17 Oct 2026 00:00:00 GMT</pubDate><category>Python</category><category>python</category><category>python tutorial</category><category>functions</category><category>object-oriented programming</category><category>recursion</category><category>pattern printing</category><category>data science</category><category>exercises</category></item><item><title>CNN Explained with Python: Build an Image Classifier</title><link>https://www.xrayailab.com/blog/cnn-explained-with-python-build-an-image-classifier/</link><guid isPermaLink="true">https://www.xrayailab.com/blog/cnn-explained-with-python-build-an-image-classifier/</guid><description>Understand convolution and pooling with runnable NumPy examples, then build, train, and evaluate a CNN image classifier in PyTorch (with a Keras version).</description><pubDate>Fri, 24 Oct 0206 00:00:00 GMT</pubDate><category>Deep Learning</category><category>CNN</category><category>Convolutional Neural Networks</category><category>PyTorch</category><category>Image Classification</category><category>Fashion-MNIST</category><category>Python</category></item></channel></rss>