Miniflow
Recreation of core tensorflow functionality: linear and sigmoid layers, forward and back prop, stochastic gradient descent
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Deeprl Agents | 2,004 | 5 years ago | 44 | mit | Jupyter Notebook | |||||
| A set of Deep Reinforcement Learning Agents implemented in Tensorflow. | ||||||||||
| Fcn.tensorflow | 1,255 | 2 years ago | 25 | mit | Jupyter Notebook | |||||
| Tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation (http://fcn.berkeleyvision.org) | ||||||||||
| Enzyme | 1,142 | a month ago | 137 | other | LLVM | |||||
| High-performance automatic differentiation of LLVM and MLIR. | ||||||||||
| Tf Explain | 934 |  | 1 | 7 | 2 years ago | 8 | November 18, 2021 | 41 | mit | Python |
| Interpretability Methods for tf.keras models with Tensorflow 2.x | ||||||||||
| Cppnumericalsolvers | 766 | 2 years ago | 2 | mit | C++ | |||||
| a lightweight C++17 library of numerical optimization methods for nonlinear functions (Including L-BFGS-B for TensorFlow) | ||||||||||
| Wgan Tensorflow | 557 | 7 years ago | 11 | Jupyter Notebook | ||||||
| a tensorflow implementation of WGAN | ||||||||||
| Wassersteingan.tensorflow | 415 | 7 years ago | 7 | mit | Python | |||||
| Tensorflow implementation of Wasserstein GAN - arxiv: https://arxiv.org/abs/1701.07875 | ||||||||||
| Tensorflow Tutorial Samples | 356 | 3 years ago | Python | |||||||
| TensorFlow2教程 TensorFlow 2.0 Tutorial 入门教程实战案例 | ||||||||||
| Tensorflow Resources | 327 | 7 years ago | 1 | mit | Python | |||||
| Curated Tensorflow code resources to help you get started with Deep Learning. | ||||||||||
| Hands On Deep Learning Algorithms With Python | 241 | 4 years ago | 1 | Jupyter Notebook | ||||||
| Master Deep Learning Algorithms with Extensive Math by Implementing them using TensorFlow | ||||||||||
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Python
Tensorflow
Gradient
Gradient Descent