Deep learning implementation

This repository contains some deep learning implementation using PyTorch.

Notebooks

Tenosrs.ipynb : In this Notebook we come to know about basic tensor operations and conversion of numpy arrays to tensors.

Linear_regression.ipynb : In this Notebook we use LinearRegression to classify temp,rainfall, humidity vs crop yeild of apples and oranges.

Logistic_regression.ipynb : In this Notebook we use LogisticRegression to classify MNIST dataset.

Feed_forward_NN.ipynb : In this Notebook we use Feed_forward_NN to train model for MNIST dataset. We use Feed_forward_NN to overcome the linear relation that we tackled in LogisticRegression.

Convolutional_neural_network.ipynb : In this Notebook we use CNN to classify CIFAR10 dataset .

ResNets.ipynb : In this Notebook we implemented ResNet9 architecture to classify CIFAR10 data set as this contains residual blocks which helps to gain more accuracy than regular CNNs.

GAN.ipynb : In this Notebook we generate anime faces from random noise. to run this Notebook please download anime-faces dataset and place in folder 'data'.

Transfer_learning.ipynb : In this Notebook we used pretrained ResNet34 model to train model instead of training from scartch.

Fashion_MNIST.ipynb : In this Notebook we classified fashion mnist dataset.