Palindrome Number Problem: String vs Mathematical Reversal

A simple problem maybe more complex than expected. Still not getting it right after more than 20 iterations, I mean with realistic and random knob/tab shape. Tried a few LLMs. Question: given a photo folder create Python code to generate a jigsaw puzzle like patchwork from these pictures. Here is my latest test, feeding outputs between tests. First iterations were about basic knobs/tabs shapes creation. Getting realistic knob/tabs shapes took more work. Mismatch between boundary shapes and pictures orientation not respected. The latest generated code now has three passes. You may to try on your side. #ArtificialIntelligence #MachineLearning #LargeLanguageModels #LLM #PythonDevelopment #SoftwareEngineering #ComputerVision #ImageProcessing #GenerativeAI #AIEngineering #PromptEngineering #AlgorithmDesign #DataScience #DeepLearning #TechInnovation #ProblemSolving #SoftwareDevelopment #CodingLife #Debugging #EngineeringChallenges #RAndD #AppliedAI #AIProjects #OpenSourceAI

Ridge Regression is like adding a speed limiter to your model: * No limit → it goes fast, but risks crashing (overfitting) * Too strict → it barely moves (underfitting) * Just right → smooth, stable, reliable The hyperparameter Alpha is the secret sauce. A small tweak in this parameter can completely change how your model behaves. In this post, I break it down with: ✔ Simple intuition (no heavy math) ✔ A simple Python example ✔ Visual comparison of different alpha values 👉 Read it here: https://lnkd.in/eqyYMMBC #DataScience #MachineLearning #AI #Python #Analytics

Master Figures, Lines & Arrows in Matplotlib! The matplotlib module can plot geometric figures such as rectangles, circles, and triangles. These figures can then illustrate mathematical, technical, and physical relationships. This blog post demonstrates the creative options of matplotlib through three examples by illustrating the Pythagorean theorem: a gear representation, a pointer diagram, and a current-carrying conductor in a homogeneous magnetic field. #Python #DataViz #Matplotlib #CodeMagic #RheinwerkComputingBlog Dive in now and transform your graphs! https://hubs.la/Q04byPg90

Your model isn't bad. Your features are. 80% of ML performance comes from feature engineering. Not from picking XGBoost over Random Forest. Not from tuning n_estimators. From the hours you spend turning raw columns into something a model can actually learn from. Free notebook covers: → Polynomial & interaction features (the trick most beginners skip) → Log transforms for skewed distributions → Binning continuous variables (and when it hurts more than it helps) → Date/time feature extraction (hour, day of week, is_holiday) → Categorical encoding beyond one-hot (target, frequency) → Text feature extraction (length, word count, TF-IDF basics) → Scaling strategies (standardize vs normalize vs neither) If your model is stuck at 70% accuracy, the fix is usually in the features, not the algorithm. https://lnkd.in/gj7SgH7y Day 1 of 7. Every day this week: a hands-on notebook. #DataScience #FeatureEngineering #MachineLearning #Python #MLEngineering #InterviewPrep #Pandas #Sklearn

Types of Skewness are Negatively Skewed, Symmectric (Not Skewed), Positively Skewed. Finding Skewness using "Karl Pearson's Coefficient of Skewness" self made statistical function. The function automatically selects between a mode-based or median-based approach when computing skewness. #python #DataScience #statistics #skewness #mode #median #distribution #negative #positive #left #right #KarlPearson

Day 18 of #66DaysOfData Built a 3-method causal incrementality engine in Python today: → Difference-in-Differences (statsmodels, HC3 robust SEs) → Bayesian Structural Time Series (CausalImpact, covariate-adjusted counterfactual) → Synthetic Control (convex donor-pool optimization via scipy) Link - https://lnkd.in/ecSztK3v Each method runs independently on the same launch event — then I compare lift estimates + CIs across all three to build consensus. #DataScience #DataEngineer #Day18 #66DaysOfData Ken Jee

📉 Understanding Confusion Matrix in Machine Learning While working on a classification problem, I explored how confusion matrices help evaluate model performance beyond just accuracy. 🔹 What is a Confusion Matrix? It is a table that compares actual values with predicted values, helping us understand where the model is correct and where it makes mistakes. 🔹 Why it matters: Shows class-wise performance Identifies misclassifications Provides deeper insights than accuracy alone 🔹 Key Insight: A good model will have high values along the diagonal (correct predictions) and low values elsewhere (errors). Confusion matrices are essential for analyzing classification models and understanding their strengths and weaknesses. #machinelearning #datascience #analytics #python #learninginpublic

Recently, I implemented both Linear Regression and Logistic Regression from scratch using Python and NumPy, emphasizing vectorization and mathematical understanding. For both models, I utilized two feature vectors and created animated visualizations of the learning process, illustrating how the decision boundary (for Logistic Regression) and regression plane/line (for Linear Regression) evolve step by step during gradient descent. A major goal was to ensure the code was scalable, efficient, and mathematically transparent. Key aspects of the implementation include: • Fully vectorized code, avoiding unnecessary loops • Significant speed improvement due to vectorization, enhancing training efficiency • Generalization capability from 2 features to n-feature input spaces • Structured for straightforward future implementation of Lasso (L1) and Ridge (L2) regularization • Visualization currently limited to 2D feature space for interpretability Building these models from scratch deepened my understanding of the underlying mathematics, particularly gradient descent, cost functions, normalization, decision boundaries, and parameter updates. Writing the algorithm myself proved to be a more insightful learning experience than simply using a library. Code can be found at: https://lnkd.in/ghqdDxMg #MachineLearning #LinearRegression #LogisticRegression #LearningByBuilding

Standard LLM chains are great for straight lines, but real-world problems are messy. They need loops, second guesses, and human check-ins. LangGraph solves that problem. It’s the "pro mode" for LangChain. I just put together a quick post on how to get started with this framework with a simple code example. Here’s the TL;DR: ✅ Define State Early: Use TypedDict to give your agent a reliable memory. ✅ Conditional Edges: Let the LLM decide when to pivot or retry. ✅ Persistence: Never lose progress again with built-in checkpointers. ✅ Human-in-the-Loop: Add "pause" buttons for high-stakes actions. ✅ Keep Nodes Pure: Small, focused functions make debugging a breeze. Check out the full breakdown below! 👇 https://lnkd.in/e_T-bCaG #AI #DataScience #Python #LangChain #LangGraph #MachineLearning #LLM

Just completed the Gradient Descent lab in Andrew Ng's ML Specialization — and it genuinely clicked for me here. The concept: instead of guessing the best values for w and b in a linear model, gradient descent finds them automatically by repeatedly moving in the direction that reduces error. What I built from scratch in Python: ✅ compute_cost() — measures how wrong the model is ✅ compute_gradient() — calculates which direction to move ✅ gradient_descent() — runs 10,000 iterations to find optimal parameters What surprised me most: → Starting from w=0, b=0, the algorithm found w≈200, b≈100 for a house price dataset → The cost dropped rapidly at first, then slowed as it approached the minimum — exactly like rolling a ball to the bottom of a bowl → Setting the learning rate too high (α = 0.8) caused the model to completely diverge — cost shot up instead of down That last point was the most valuable. Seeing divergence visually made the theory real. Building these functions line by line beats reading about them any day. #MachineLearning #Python #AndrewNg #LearningInPublic #DataScience #GradientDescent