I just released the 0.5 version of my multidimensional arrays (Numpy-like) + Deep Learning library (PyTorch-like) that I've written from scratch in Nim.
Key highlights of this version:
- Sequence/time series prediction end-to-end example[1]
- Text generation with Char-RNN on Shakespeare and Jane Austen work end-to-end example[2]
- IMDB dataset
- read and write: Numpy .npy files, images (jpg, png, bmp, tga) and H5
- KMeans clustering
- GRU, Embedding layers
- Adam optimizer
- Yann Lecun, Xavier Glorot and Kaiming He initialisations
- fancy indexing
- tensor splitting, chunking stacking with autograd support
And in the ecosystem:
- a neural network training demo with live input and loss monitoring[3]
- Nim wrapper for the Arcade Learning Environment to agent on Atari games[4]
Nim[5] is a high performance compiled language with a syntax similar to Python. Nim compiles to C, C++ or Javascript.
I just released the 0.5 version of my multidimensional arrays (Numpy-like) + Deep Learning library (PyTorch-like) that I've written from scratch in Nim.
Key highlights of this version:
And in the ecosystem: Nim[5] is a high performance compiled language with a syntax similar to Python. Nim compiles to C, C++ or Javascript.