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Learn to build your neural network using PyTorch
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Basic and Linear Regression.ipynb
CNN and ResNet.ipynb
Custom dataset and Transfer learning.ipynb
Initialize weight of parameter of model by yourself.ipynb
Logistic regression and Neural Network.ipynb
Practise Notebook_basic.ipynb Adding basic tutorials for Image Mar 16, 2019


This repository contains notebooks to learn Pytorch step by step. All are written in jupyter notebook so that one can tweak the code and check the results for the same. I assume that reader have basic understanding of Machine learning and deep learning.


  • Basic and Linear Regression - Tensors, tensors to numpy and vice versa, training a normal linear regression model
  • Logistic regression and Neural Network - Help you load a dataset, Basic architecture of writing a neural network, build a logistic regression model and neural network.
  • CNN and Resnet - This help you to write a CNN network and a complex network like Resnet so that you can then write any other netwrok by your own. Also it tells how to save and load model and what's the difference between saving the parameter and whole model.
  • Custom Dataset and Trasfer Learning - It helps you to learn how to load a pretrained network and how to freeze layers, how to tweak a pretrained model and Finally some important tips on how to make a custom dataset loader which is of great essence now.
  • Initialize weight of parameter of model by yourself- This notebook help you learn how you can set initial weight of parameter of Network by yourself, without using Pytorch weight initializer. This is very helpful when you have to compare your implementation with a tested library like PyTorch.
  • AAE - This helps you learn how to train a multiple networks simultanelously. Here we are going to train three network (Encoder, Decoder and Discriminator). Also plotting the images and curve is shown in the tutorial.
  • GANS - Moving to more advanced networks, an application of what you have learned above.


  • Pytorch
  • Jupyter Notebook
  • Python 3


Have anything in mind which you think can be helpful, please create an issue and let me know. So that I can incorporate that here. If you wish to contribute feel free to send a pull request.

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