A collection of codes and notebooks implementing various types of neural networks in Numpy. As expected, codes are not optimized, and are incapable of utilizing GPU power!

1. Softmax Classification on MNIST

   • Code: 01 Softmax Regression.py
   • Notebook: 01 Softmax Regression.ipynb

2. Stochastic, mini-Batch, Batch Gradient Descent, and Dataloaders

   • Code: 02 SGD, BGD, mini-BGD.py
   • Notebook: 02 SGD, BGD, mini-BGD.ipynb

3. Optimizers (Moment, RMSProp, Adam)

   • Code:
   • Notebook: 03 Optimizers.ipynb

4. Regularization (L1, L2, Dropout, Batchnorm)

   • Code:
   • Notebook:

5. Convolutional Neural Networks (CNN) on MNIST

   • Code:
   • Notebook:

6. Recurrent Neural Networks (RNN) on MNIST

   • Code:
   • Notebook:

7. Generative Adversarial Networks (GANs) on MNIST

   • Code:
   • Notebook:

1. Sequential Layers

   • Code:
   • Notebook:

2. Autograd

   • Code:
   • Notebook:

1. Linear and CNN Autoencoder on MNIST

   • Code:
   • Notebook:

2. Sentiment Classification and Word Embedding on IMDB Movie Review Dataset

   • Code:
   • Notebook:

3. Character-level RNN

   • Code:
   • Notebook:

1. Principal Component Analysis (PCA)
   • Code:
   • Notebook: PCA.ipynb