ekinakyurek/KnetLayers.jl
Useful Layers for Knet
This project offers pre-built deep learning components to simplify the creation of neural network models. It takes your raw data (like images or text) and allows you to quickly assemble and train models for tasks such as image recognition or sequence processing. Data scientists and machine learning practitioners who use the Knet deep learning framework would find this useful for accelerating their model development.
No commits in the last 6 months.
Use this if you are building deep learning models with Knet and need ready-to-use neural network layers like convolutional, recurrent (LSTM), or dense layers to construct your architecture efficiently.
Not ideal if you are not using the Knet deep learning framework or need extremely custom, low-level control over every aspect of your network's layers without any higher-level abstractions.
Stars
21
Forks
4
Language
Julia
License
MIT
Category
Last pushed
Aug 08, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/ekinakyurek/KnetLayers.jl"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
saturncloud/dask-pytorch-ddp
dask-pytorch-ddp is a Python package that makes it easy to train PyTorch models on dask clusters...
pclubiitk/model-zoo
Implementations of various Deep Learning models in PyTorch and TensorFlow.
neuralmagic/deepsparse
Sparsity-aware deep learning inference runtime for CPUs
theairbend3r/how-to-train-your-neural-net
Deep learning research implemented on notebooks using PyTorch.
neuralmagic/sparsezoo
Neural network model repository for highly sparse and sparse-quantized models with matching...