Regularized_autoencoders Rae
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Data Science Ipython Notebooks | 25,668 | 6 months ago | 34 | other | Python | |||||
| Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines. | ||||||||||
| Keract | 1,030 | 5 months ago | 2 | mit | Python | |||||
| Layers Outputs and Gradients in Keras. Made easy. | ||||||||||
| Workshopscipy | 396 | 6 years ago | mit | Jupyter Notebook | ||||||
| A workshop for scientific computing in Python. ( December 2017 ) | ||||||||||
| Numpy_neural_network | 307 | 3 years ago | 4 | apache-2.0 | Jupyter Notebook | |||||
| 仅使用numpy从头开始实现神经网络,包括反向传播公式推导过程; numpy构建全连接层、卷积层、池化层、Flatten层；以及图像分类案例及精调网络案例等,持续更新中... ... | ||||||||||
| Regularized_autoencoders Rae | 104 | 2 years ago | 13 | gpl-3.0 | Python | |||||
| Assignment2 2017 | 59 | 7 years ago | Python | |||||||
| (Spring 2017) Assignment 2: GPU Executor | ||||||||||
| Deep Residual Networks Pyfunt | 45 | 7 years ago | mit | Python | ||||||
| Python implementation of "Deep Residual Learning for Image Recognition" (http://arxiv.org/abs/1512.03385 - MSRA, winner team of the 2015 ILSVRC and COCO challenges). | ||||||||||
| Numpy Neuralnet Exercise | 42 | 6 years ago | Python | |||||||
| Implementation of key concepts of neuralnetwork via numpy | ||||||||||
| Mnist For Numpy | 39 | 4 years ago | 2 | mit | Python | |||||
| A simple, easy to use MNIST loader written in Python 3 | ||||||||||
| Mobula | 38 | 4 years ago | 4 | September 17, 2017 | mit | Python | ||||
| A Lightweight & Flexible Deep Learning (Neural Network) Framework in Python | ||||||||||
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