"Deep Learning with PyTorch: Zero to GANs" is a beginner-friendly online course offering a practical and coding-focused introduction to deep learning using the PyTorch framework. Enroll now to start learning.

* Watch live hands-on tutorials on YouTube
* Train models on cloud Jupyter notebooks
* Build an end-to-end real-world course project
* Earn a verified certificate of accomplishment

Visit the [Course Community Forum](https://jovian.ai/forum/c/pytorch-zero-to-gans/18) to ask questions and get help.

Deep Learning with PyTorch: Zero to GANs

**Submission Deadline**: Jan 9, 2021 11:59 PM GMT

The objective of this assignment is to develop a solid understanding of PyTorch tensors. In this assignment you will:
* Pick 5 interesting functions related to PyTorch tensors by [reading the documentation](https://pytorch.org/docs/stable/torch.html)
* Create a Jupyter notebook using a starter template to illustrate their usage
* Upload and showcase your Jupyter notebook on your Jovian profile
* (optional) Write a blog post to accompany and showcase your Jupyter notebook 
* Share your work with the community and exchange feedback with other participants

* Explore the PyTorch documentation website
* Demonstrate usage of some tensor operations
* Publish your Jupyter notebook & share your work

Notebook Link

Assignment 1 - All About torch.Tensor

**Submission Deadline**: Jan 9, 2021 11:59 PM GMT

In this assignment, you’re going to use information like a person’s age, sex, BMI, no. of children, and smoking habit to predict the price of yearly medical bills. You will train a model with the following steps:

* Download and explore the dataset
* Prepare the dataset for training
* Create a linear regression model
* Train the model to fit the data
* Make predictions using the trained model

* Download and explore a real-world dataset
* Create a linear regression model using PyTorch
* Train multiple models and make predictions

Assignment 2 - Train Your First Model

**Submission Deadline**: Jan 16, 2021 11:59 PM GMT

The ability to try many different neural network architectures to address a problem is what makes deep learning really powerful, especially compared to shallow learning techniques like linear regression, logistic regression, etc. In this assignment, you will:
* Explore the CIFAR10 dataset
* Set up a training pipeline to train a neural network on a GPU
* Experiment with different network architectures & hyperparameters

* Explore the CIFAR10 image dataset
* Create a pipeline for training on GPUs
* Hyperparameter tuning & optimization

Assignment 3 - Feed Forward Neural Networks

**Submission Deadline**: Jan 16, 2021 11:59 PM GMT

For the course project, you will pick a dataset of your choice and apply the concepts learned in this course to train deep learning models end-to-end with PyTorch, experimenting with different hyperparameters & metrics.

1. Find a dataset online (see the "Where to Find Datasets" section below)
2. Understand and describe the modeling objective clearly 
    1. What type of data is it? (images, text, audio, etc.)
    2. What type of problem is it? (regression, classification, generative modeling, etc.)
3. Clean the data if required and perform exploratory analysis (plot graphs, ask questions)
4. Modeling
    1. Define a model (network architecture)
    2. Pick some hyperparameters
    3. Train the model
    4. Make predictions on samples
    5. Evaluate on the test dataset
    6. Save the model weights
    7. Record the metrics
    8. Try different hyperparameters & regularization
5. Conclusions - summarize your learning & identify opportunities for future work
6. Publish and submit your Jupyter notebook
7. (Optional) Write a blog post to describe your experiments and summarize your work. Use Medium or Github pages. 

Note: There is no starter notebook for the course project. Please use the "New Notebook" button on Jovian to create a new notebook, "Run on Colab" to execute it, and "jovian.commit" to record versions.

Example notebooks for reference: 

- https://jovian.ai/aakashns/simple-cnn-starter
- https://jovian.ai/aakashns/transfer-learning-pytorch
- https://jovian.ai/aakashns/06b-anime-dcgan
- https://jovian.ai/aakashns/05b-cifar10-resnet

* Discover & explore a large real-world dataset
* Train a convolutional neural network from scratch
* Document, present, and publish your work online

Blog Link

Course Project - Train a Deep Learning Model from Scratch

Hindi version: https://youtu.be/7yRkvPxE7Hs

We start this lesson with the basics of PyTorch: tensors, gradients, and Autograd. We then proceed to implement linear regression and gradient descent from scratch, first using matrix operations and then using PyTorch built-ins. Notebooks used in this lesson:

* PyTorch Basics: [jovian.ai/aakashns/01-pytorch-basics](https://jovian.ai/aakashns/01-pytorch-basics)
* Linear Regression: [jovian.ai/aakashns/02-linear-regression](https://jovian.ai/aakashns/02-linear-regression)
* Machine Learning: [jovian.ai/aakashns/machine-learning-intro](https://jovian.ai/aakashns/machine-learning-intro)

Ask questions on the [course discussion forum](https://jovian.ai/forum/c/pytorch-zero-to-gans/lecture-1-pytorch-basics-linear-regression/63).

* PyTorch basics: tensors, gradients, and autograd
* Linear regression & gradient descent from scratch
* Using PyTorch modules: nn.Linear & nn.functional

Lesson 1 - PyTorch Basics and Gradient Descent

Hindi Version: https://youtu.be/qmzgJvVP9XA

This lesson covers how to work with images from the MNIST dataset, creating training & validation datasets to avoid overfitting, and training a logistic regression model using the softmax activation & the cross-entropy loss. Notebooks used in this lesson: 

* Logistic regression: https://jovian.ai/aakashns/03-logistic-regression
* Logistic regression starter: https://jovian.ai/aakashns/mnist-logistic-minimal
* Linear regression starter: https://jovian.ai/aakashns/housing-linear-minimal

Ask questions on the [community discussion forum](https://jovian.ai/forum/t/lecture-2-working-with-images-logistic-regression/13735).

* Training-validation split on the MNIST dataset
* Logistic regression, softmax & cross-entropy
* Model training, evaluation & sample predictions

Lesson 2 - Working with Images and Logistic Regression

Hindi Version: https://youtu.be/9MqR_BsogDw

In this lesson, we learn how to build and train a deep neural network with hidden layers and non-linear activations using cloud-based GPUs. Notebooks used in this lesson:

* Training deep neural networks on a GPU: https://jovian.ai/aakashns/04-feedforward-nn
* Starter notebook for training a model: https://jovian.ai/aakashns/fashion-feedforward-minimal
* Data visualization cheatsheet: https://jovian.ai/aakashns/dataviz-cheatsheet

Ask questions and get help on the [community discussion forum](https://jovian.ai/forum/c/pytorch-zero-to-gans/lecture-3-training-deep-neural-networks-on-a-gpu/67).

* Multilayer neural networks using nn.Module
* Activation functions, non-linearity & backprop
* Training models faster using cloud GPUs

Lesson 3 - Training Deep Neural Networks on a GPU

Hindi version: https://youtu.be/OTyv_G3OL9I

This lesson introduces convolutional neural networks (CNNs) that are well-suited for image datasets and computer vision problems. We also cover underfitting, overfitting and techniques to improve model performance. Notebooks used in this lesson:

* Convolutional Neural Networks: https://jovian.ai/aakashns/05-cifar10-cnn
* CIFAR10 Feed Forward Model: https://jovian.ai/aakashns/03-cifar10-feedforward

Ask questions and get help on the [course discussion forum](https://jovian.ai/forum/c/pytorch-zero-to-gans/lecture-4-image-classification-with-convolutiona/69).

* Working with 3-channel RGB images 
* Convolutions, kernels & features maps
* Training curve, underfitting & overfitting

Lesson 4 - Image Classification with Convolutional Neural Networks

Hindi Version: https://youtu.be/S2zNff6rgOg

This lesson covers some advanced techniques like data augmentation, regularization, and adding residual layers to convolutional neural networks. We train a state-of-the-art model from scratch in just five minutes. Notebooks used in this lesson: 

* ResNets, Regularization & Data Augmentation: https://jovian.ai/aakashns/05b-cifar10-resnet
* Simple CNN Starter Notebook: https://jovian.ai/aakashns/simple-cnn-starter
* Transfer Learning with CNNs: https://jovian.ai/aakashns/transfer-learning-pytorch
* Image Classification with CNNs: https://jovian.ai/aakashns/05-cifar10-cnn

Ask questions and get help on the [course discussion forum](https://jovian.ai/forum/c/pytorch-zero-to-gans/lecture-5-data-augmentation-regularization/72)

* Adding residual layers with batchnorm to CNNs
* Learning rate annealing, weight decay & more
* Training a state-of-the-art model in 5 minutes

Lesson 5 - Data Augmentation, Regularization & ResNets

Hindi Version: https://youtu.be/87xD5NEmLhA

In this lesson, we apply the techniques covered in previous lessons for generating images for anime faces using Generative Adversarial Networks or GANs. Notebooks used in this tutorial:

* Generating anime faces using GANs: https://jovian.ai/aakashns/06b-anime-dcgan
* Generating handwritten digits using GANs: https://jovian.ai/aakashns/06-mnist-gan
* Transfer Learning using pretrained models: https://jovian.ai/aakashns/transfer-learning-pytorch
* Zero to GANs using Tensorflow: https://jovian.ai/kartik.godawat/collections/tf-zero-to-gan

Ask questions and get help on the [course discussion forum](https://jovian.ai/forum/c/pytorch-zero-to-gans/lecture-6-image-generation-using-gans/73).

* Generating fake digits & anime faces with GANs
* Training generator and discriminator networks
* Transfer learning for image classification

Lesson 6: Generative Adversarial Networks and Transfer Learning

Aakash is the co-founder and CEO of [Jovian.ml](https://jovian.ml), a project management and collaboration platform for machine learning. Prior to starting [Jovian.ml](https://jovian.ml), Aakash worked as a software engineer (APIs & Data Platforms) at Twitter in Ireland & San Francisco and graduated from Indian Institute of Technology, Bombay. He’s also an avid blogger, open source contributor and online educator. 
https://www.linkedin.com/in/aakashns/

Instructor - Aakash N S