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Deep learning library in plain Numpy.
| Date | Stars |
|---|---|
| 2026-07-31 | 322 |
| 2026-08-06 | 322 |
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# deepnet
Implementations of CNNs, RNNs and cool new techniques in deep learning
Note: deepnet is a work in progress and things will be added gradually. It is not intended for production, use it to learn and study implementations of latest and greatest in deep learning.
## What does it have?
**Network Architecture**
1. Convolutional net
2. Feed forward net
3. Recurrent net (LSTM/GRU coming soon)
**Optimization Algorithms**
1. SGD
2. SGD with momentum
3. Nesterov Accelerated Gradient
4. Adagrad
5. RMSprop
6. Adam
**Regularization**
1. Dropout
2. L1 and L2 Regularization
**Cool Techniques**
1. BatchNorm
2. Xavier Weight Initialization
**Nonlinearities**
1. ReLU
2. Sigmoid
3. tanh
## Usage
1. ```virtualenv .env``` ; create a virtual environment
2. ```source .env/bin/activate``` ; activate the virtual environment
3. ```pip install -r requirements.txt``` ; Install dependencies
4. ```python run_cnn.py {mnist|cifar10}``` ; mnist for shallow cnn and cifar10 for deep cnnExcerpt of 988 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c30fc2995d0c88bf, llm:Description and README: 'Deep learning library in plain Numpy', implements CNNs, RNNs, optimizers (SGD, Adam, Adagrad), regularization, BatchNorm, Xavier, nonlinearities; topics: adagrad, adam-optimizer, batch-normalization, cnn, dropout, nesterov-accelerated-sgd.
matched fp:c30fc2995d0c88bf, llm:Description and README: 'Deep learning library in plain Numpy', implements CNNs, RNNs, optimizers (SGD, Adam, Adagrad), regularization, BatchNorm, Xavier, nonlinearities; topics: adagrad, adam-optimizer, batch-normalization, cnn, dropout, nesterov-accelerated-sgd.