Cityseer Api
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Pytorch | 74,794 |  | 3,341 | 8,272 | 3 months ago | 39 | November 15, 2023 | 13,261 | other | Python |
| Tensors and Dynamic neural networks in Python with strong GPU acceleration | ||||||||||
| Pytorch Examples | 4,170 | 2 years ago | 8 | mit | Python | |||||
| Simple examples to introduce PyTorch | ||||||||||
| Machine Learning Experiments | 1,552 | 4 months ago | 7 | mit | Jupyter Notebook | |||||
| 🤖 Interactive Machine Learning experiments: 🏋️models training + 🎨models demo | ||||||||||
| Andrew Ng Notes | 1,367 | a year ago | 2 | Jupyter Notebook | ||||||
| This is Andrew NG Coursera Handwritten Notes. | ||||||||||
| Ilearndeeplearning.py | 1,291 | 4 months ago | 24 | mit | Jupyter Notebook | |||||
| This repository contains small projects related to Neural Networks and Deep Learning in general. Subjects are closely linekd with articles I publish on Medium. I encourage you both to read as well as to check how the code works in the action. | ||||||||||
| Awesome Jax | 1,156 | 4 months ago | 12 | cc0-1.0 | ||||||
| JAX - A curated list of resources https://github.com/google/jax | ||||||||||
| Numpycnn | 531 | 10 months ago | 3 | May 24, 2018 | 1 | Python | ||||
| Building Convolutional Neural Networks From Scratch using NumPy | ||||||||||
| Deep Belief Network | 401 | 2 years ago | 13 | mit | Python | |||||
| A Python implementation of Deep Belief Networks built upon NumPy and TensorFlow with scikit-learn compatibility | ||||||||||
| Convolutional_neural_network | 337 | 5 years ago | 7 | Jupyter Notebook | ||||||
| This is the code for "Convolutional Neural Networks - The Math of Intelligence (Week 4)" By Siraj Raval on Youtube | ||||||||||
| Python Neural Network | 278 | 1 | 4 years ago | 4 | July 29, 2016 | 5 | bsd-2-clause | Python | ||
| This is an efficient implementation of a fully connected neural network in NumPy. The network can be trained by a variety of learning algorithms: backpropagation, resilient backpropagation and scaled conjugate gradient learning. The network has been developed with PYPY in mind. | ||||||||||
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