EigenAlpha vs SPICENet Visualization

EigenAlpha: Linear Algebra Core Skills Training Unlock the visual and spatial power of Linear Algebra. EigenAlpha is an interactive learning console designed for university students, engineers, and machine learning enthusiasts who want to develop a deep, intuitive, and mathematical grasp of vector spaces, matrices, and linear mappings. Skip rote memorization. EigenAlpha combines structured curriculum progression with stunning visual diagnostics to help you bridge the gap between abstract equations and geometric transformations. KEY FEATURES: Structured Course Progression: Move step-by-step through core Linear Algebra units—from basis vector spans and matrix dimensions to QR decompositions, eigenvalues, and Gram-Schmidt orthogonalization. Macro Skill Diagnostics: View your real-time accuracy across 6 core mathematical domains, including Matrix Arithmetic, Algorithmic Reduction (Gaussian Elimination), Spectral Decomposition, and Invariant Inspection. Applied Crossover Case Studies: Explore how abstract matrix concepts drive state-of-the-art modern technologies. Follow step-by-step reasoning progressions for real-world scenarios, including Google PageRank spectral convergence. Conceptual Diagrams & Pitfalls: Review custom high-contrast visuals mapping out transformations and eigenvalues. Learn to bypass common pitfalls and exam tips carefully curated by math researchers. Reset & Restore Control: Maintain full command over your learning log. Reset diagnostic progress to re-test your accuracy, and easily restore premium license entitlements across all your iOS devices. Accessibility Integrated: Fully optimized for Dynamic Type font scaling and screen reader VoiceOver narration. WHO IS IT FOR? - STEM Undergrads: Excel in college-level Linear Algebra courses and diagnostic exams. - AI & Machine Learning Developers: Understand the vector transformations, dimension spaces, and spectral decomposition behind neural networks and PCA. - 3D Developers & Graphics Engineers: Master translation, rotation, and projection matrices.

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.