Implementing the Müller-Brown Potential in PyTorch
Guide to implementing the Müller-Brown potential in PyTorch, comparing analytical vs automatic differentiation with …
Neural-Networks
Deconstructing Neural Networks for Time Series Forecasting
Ablation study of neural network components for forecasting, finding gating and attention improve RNNs while recurrence …...
Modern PyTorch Techniques for VAEs: A Tutorial
VAE tutorial using modern PyTorch: torch.distributions, optimization techniques, numerical stability, and implementation …
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 …
GAN Objective Functions: A Comprehensive Guide
Complete guide to GAN objective functions including WGAN, LSGAN, Fisher GAN, and more. Understand which loss function to …
Understanding Generative Adversarial Networks (GANs)
Learn about GANs with intuitive explanations and mathematical foundations. Learn how adversarial networks generate …