Yapnn

Yapnn is an open-source neural network library designed for building, training, and deploying machine learning models. It offers a modular, Python-based API intended to be approachable for beginners while flexible enough for researchers to prototype novel architectures. The library provides core components such as layers, activation functions, loss criteria, optimizers, and utilities for data handling, visualization, and evaluation.

Under the hood, Yapnn uses a tensor abstraction and supports automatic differentiation with a dynamic computation graph. It exposes common layer types including dense (fully connected), convolutional, recurrent (RNN, LSTM, GRU), and normalization layers, along with a range of activation functions. Training is facilitated by optimizers such as SGD and Adam, with utilities for learning rate scheduling and batching. Models can be serialized to disk and inspected or modified via the API; it also supports interoperability with NumPy arrays and basic dataset pipelines. The runtime targets CPUs by default and offers optional GPU acceleration through CUDA, enabling modest speedups on compatible hardware.

Yapnn originated as an open-source project maintained by a community of contributors. It is commonly used in education and small-scale research due to its readable API and lightweight footprint. Compared with larger frameworks, Yapnn has a smaller ecosystem of extensions and community resources, but it remains a practical choice for quick experiments and instructional use.