Exponential Random Number Generation: Two Classic Algorithms Compared
Compare inverse transform sampling and von Neumann's rejection method for exponential random numbers with Python …
Algorithms
Implementing the Müller-Brown Potential in PyTorch
Guide to implementing the Müller-Brown potential in PyTorch, comparing analytical vs automatic differentiation with …
Deconstructing Neural Networks for Time Series Forecasting
Ablation study of neural network components for forecasting, finding gating and attention improve RNNs while recurrence …...
Kabsch Algorithm: NumPy, PyTorch, TensorFlow, and JAX
Learn about the Kabsch algorithm for optimal point alignment with implementations in NumPy, PyTorch, TensorFlow, and JAX …
A Framework for Multi-Arm Bandit Problems
Framework for understanding multi-arm bandit algorithms through five dimensions. Covers exploration vs exploitation and …
HyperNEAT: Scaling Neuroevolution with Geometric Patterns
How HyperNEAT uses indirect encoding and geometric patterns to evolve large-scale neural networks with biological …
NEAT: Evolving Neural Network Topologies
Learn about NEAT's approach to evolving neural networks: automatic topology design, historical markings, and speciation …
Breaking Down Machine Learning for the Average Person
Discover how machine learning actually works through three fundamental approaches, explained with everyday examples you …
Fundamentals of Logic: Knowledge Representation
Learn about the foundations of AI logic: syntax, semantics, entailment, and inference algorithms for building …