MyGrad VS dnn_from_scratch

Compare MyGrad vs dnn_from_scratch and see what are their differences.

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MyGrad dnn_from_scratch
1 1
186 29
- -
3.0 0.0
25 days ago almost 3 years ago
Python Python
MIT License -
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

MyGrad

Posts with mentions or reviews of MyGrad. We have used some of these posts to build our list of alternatives and similar projects.

dnn_from_scratch

Posts with mentions or reviews of dnn_from_scratch. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing MyGrad and dnn_from_scratch you can also consider the following projects:

SmallPebble - Minimal deep learning library written from scratch in Python, using NumPy/CuPy.

deepxde - A library for scientific machine learning and physics-informed learning

pytorch_sparse - PyTorch Extension Library of Optimized Autograd Sparse Matrix Operations

HyperGAN - Composable GAN framework with api and user interface

ALAE - [CVPR2020] Adversarial Latent Autoencoders

guesslang - Detect the programming language of a source code

open-lpr - Open Source and Free License Plate Recognition Software

Mask-RCNN-TF2 - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow 2.0

t81_558_deep_learning - T81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis

gan-vae-pretrained-pytorch - Pretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch.

the-gan-zoo - A list of all named GANs!

cnn-in-welding - Detecting Faults and Measuring Severity in Welding using Radiographic Images