First-steps-towards-Deep-Learning VS neuralnet-browser

Compare First-steps-towards-Deep-Learning vs neuralnet-browser and see what are their differences.

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First-steps-towards-Deep-Learning neuralnet-browser
1 2
436 11
- -
0.0 10.0
over 1 year ago over 1 year ago
CSS CSS
GNU General Public License v3.0 only -
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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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.

First-steps-towards-Deep-Learning

Posts with mentions or reviews of First-steps-towards-Deep-Learning. We have used some of these posts to build our list of alternatives and similar projects.

neuralnet-browser

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

What are some alternatives?

When comparing First-steps-towards-Deep-Learning and neuralnet-browser you can also consider the following projects:

darknet - YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )

Food-Recipe-CNN - food image to recipe with deep convolutional neural networks.

awesome-ai-awesomeness - A curated list of awesome awesomeness about artificial intelligence

NeuralNetwork.NET - A TensorFlow-inspired neural network library built from scratch in C# 7.3 for .NET Standard 2.0, with GPU support through cuDNN

deeplearning-notes - Notes for Deep Learning Specialization Courses led by Andrew Ng.

SincNet - SincNet is a neural architecture for efficiently processing raw audio samples.

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

dnn_from_scratch - A high level deep learning library for Convolutional Neural Networks,GANs and more, made from scratch(numpy/cupy implementation).