| fengdu78/deeplearning_ai_books |
14,417 |
|
0 |
0 |
about 4 years ago |
0 |
|
53 |
|
HTML |
| deeplearning.ai（吴恩达老师的深度学习课程笔记及资源） |
| albermax/innvestigate |
1,175 |
|
1 |
1 |
over 2 years ago |
8 |
October 12, 2023 |
55 |
other |
Python |
| A toolbox to iNNvestigate neural networks' predictions! |
| Yukong/Deeplearning.ai-Solutions |
756 |
|
0 |
0 |
over 8 years ago |
0 |
|
3 |
|
|
| Solutions of assignments and translation to Chinese |
| CodingTrain/Toy-Neural-Network-JS |
407 |
|
0 |
0 |
about 3 years ago |
0 |
|
64 |
mit |
JavaScript |
| Neural Network JavaScript library for Coding Train tutorials |
| krocki/dnc |
386 |
|
0 |
0 |
over 4 years ago |
0 |
|
0 |
|
Python |
| Simple RNN, LSTM and Differentiable Neural Computer in pure Numpy |
| gokadin/ai-simplest-network |
317 |
|
0 |
0 |
almost 6 years ago |
0 |
|
0 |
|
Go |
| The simplest form of an artificial neural network explained and demonstrated. |
| jorgenkg/python-neural-network |
278 |
|
1 |
0 |
about 6 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. |
| jostmey/DeepNeuralClassifier |
243 |
|
0 |
0 |
almost 3 years ago |
0 |
|
0 |
|
Julia |
| Deep neural network using rectified linear units to classify hand written symbols from the MNIST dataset. |
| fzenke/spytorch |
218 |
|
0 |
0 |
about 3 years ago |
0 |
|
0 |
|
Jupyter Notebook |
| Tutorial for surrogate gradient learning in spiking neural networks |
| JonasGeiping/invertinggradients |
109 |
|
0 |
0 |
about 4 years ago |
0 |
|
0 |
mit |
Jupyter Notebook |
| Algorithms to recover input data from their gradient signal through a neural network |
