Encog

Enables building and training neural networks in under 10 lines of code with keyword-based arguments, as shown in the XOR example, reducing boilerplate and Java interop.

Supports multiple neural network types like feedforward perceptrons, self-organizing maps, and Hopfield networks, leveraging Encog's mature and optimized framework.

Offers back-propagation, resilient propagation, genetic algorithms, simulated annealing, and NEAT, allowing for diverse and adaptable model training approaches.

Abstracts away the sharp edges of Encog's Java library, providing a seamless, idiomatic Clojure experience for machine learning tasks, as emphasized in the philosophy.