Arraymancer was born in-between linalg and neo and is targeted at machine
learning, with a focus on deep learning and computer-vision (and later speech
and language hopefully).
I started Arraymancer because you need 4-dimensional tensors for deep learning
on images (Number of images, RGB colors, height, width) and 5d for videos or 3D
images.
It takes a Numpy/Julia/Matlab like multidimensional array approach meaning
operations like reshaping, concatenating arrays over an arbitrary dimensions,
permuting dimensions, working with stacked 2d matrices in a 3d tensors is
possible and efficient.
* * *
There are a few twists:
* Like Andrea, I like linear algebra operations like `+` and `*` to mean math
(linear algebra) things, so broadcasting and element-wise matrix product is not
implicit like in Numpy but opt-in: you must use custom operators `.+` and `.*`.
The benefit is that if you have strange results you can grep those.
* Like Nim, I choose to have value semantics for my tensors, meaning it's
copy on assignation. You can opt-in to use "unsafe" operations like
"unsafeReshape" or "unsafeTranspose" that will provide a view over the same
data.
It avoids a lot of beginners gotchas and like broadcasting makes it easy to
grep for potential issues.
Note: For CudaTensors I currently can't due to `=` overloading issues.
* * *
**My short-term plans (consolidating the base):**
* Convolutional neural networks are just around the corner
* Improve CudaTensors support
* Doing demos
* Cover common deep-learning use cases outside of Recurrent Neural Networks.
**Mid-term plans (focus on data):**
* CSV loading and/or conversion from a Nim dataframe package like NimData
* Basic image manipulation (rotating, flipping, blurring ...), in an extra
package like Arraymancer-vision.
* Saving and loading models from disk.
* Provide popular datasets (IRIS, MNIST ...)
* Recurrent neural networks.
**Long-term plans (hardware and format):**
* More hardware support (AMD GPUs at least)
* Provide utilities for general machine learning (and not only deep learning):
* Sparse tensors but like Andrea said, apart on Cuda there is no standard :/
* PCA and SVD matrices decomposition
* Clustering tools like KMeans, DBScans
**Very long-term plans (wishlist):**
* Bindings to gradient boosted trees libraries
([XGboost](https://github.com/dmlc/xgboost) /
[LightGBM](https://github.com/Microsoft/LightGBM))
* Videos loading and analysis
* New backends: OpenCL, Javascript, Apple Metal
**Very (very !) long-term plans (wishlist reloaded):**
* More natural language utilities or bindings like
[spaCy](https://github.com/explosion/spaCy)
* REST API for example, send Arraymancer an image and it returns what was
detected in it.
* [Bayesian Neural
Networks](https://alpha-i.co/blog/MNIST-for-ML-beginners-The-Bayesian-Way.html)
which works with uncertainty built-in
**What is not a focus:**
* Linear algebra/LAPACK not used in Machine Learning (like solving equations)
* 2D/3D geometry and quaternions
([nim-glm](https://github.com/stavenko/nim-glm) is probably more suited than
either neo or Arraymancer)
