Data Scientific vs SPICENet Visualization

Science of Data is a mobile companion to two graduate-level books by Jean Claude Lemoyne Zabaloff — "The Science of Data" and "Data Science Project" — paired with an AI-powered ML Code Generator that produces complete, runnable scripts in Python, R, or MATLAB for any machine learning task you describe. 13 CHAPTERS What is AI? · Foundations · Supervised Learning · Unsupervised Learning · Reinforcement Learning · Deep Learning · Bayesian Inference · Support Vector Machines · Data Science · Knowledge · DS Process · Key Concepts 5 APPENDICES A — Python | B — R | C — MATLAB / Octave / Scilab D — Optimization | E — Public Data Sources ML CODE GENERATOR Describe any ML task or pick from 23 built-in templates and receive a complete, runnable script instantly. Languages — Python, R, MATLAB Templates — Classifiers · Regression · Cross-Validation · ROC Curve · Confusion Matrix · Feature Importance · Pipeline · Clustering · Neural Network · SVM · PCA · Preprocessing · Hyperparameter Tuning · Time Series · Bayesian Inference · Naive Bayes · Bayesian Regression · Metropolis-Hastings · Gibbs Sampling · MCMC Diagnostics · Gradient Descent · Constrained Optimization · Bayesian Optimization Every script includes type hints, WHY comments, labelled output, an expected-output block, and an install-command badge (pip install / install.packages / Requires:). Copy to clipboard or share via any app. Three AI-generated follow-up questions deepen understanding after every session. APP FEATURES · Full-text search across all chapters and appendices · Bookmarks for quick chapter access · Font size control: S / M / L / XL · Dark and light mode · Swipe navigation between chapters · Content tab + Notes / cheat-sheet tab per chapter · GFM table rendering with horizontal scroll · Dracula syntax highlighting — light-green comments, orange function calls, pink definitions, purple numbers IDEAL FOR Graduate ML and data science students, practitioners who want a rigorous pocket reference, engineers moving into ML, and anyone studying Bayesian or probabilistic methods. PRIVACY No account required. No personal data collected or stored. The ML Code Generator sends only your prompt text to Anthropic's Claude API; no personal information is ever transmitted.

SpiceNet is an educational and research-oriented simulation app for exploring SPICEnet, a self-organizing neural model for learning relationships between sensory inputs. Run simulations from generated data or import your own CSV file, adjust network and data parameters, and inspect the results through scatter plots, line charts, histograms, tuning curves, and heatmaps. The app also includes a theory section explaining the SPICEnet model and a benchmark tool for comparing standard Swift computation with Apple’s Accelerate framework. Features: - Run SPICEnet simulations from random data - Import CSV files and select columns for analysis - Adjust neurons, learning rate, initialization range, data count, transformation type, and chunk size - Visualize simulation output with multiple chart types - View explanatory SPICEnet theory content - Run local performance benchmarks - Export benchmark results as a PDF All simulations and file processing run locally on the device. Academic context: SpiceNet was developed by Omar Ashour as part of a bachelor thesis under the supervision of Prof. Dr.-Ing. Cristian Axenie at Technische Hochschule Nürnberg Georg Simon Ohm.