Deep Learning with PyTorch: Zero to GANs

"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

The course is self-paced and there are no deadlines. There are no prerequisites for this course.

Course Prerequisites

• Programming basics (functions & loops)
• Linear algebra basics (vectors & matrices)
• Calculus basics (derivatives & slopes)
• No prior knowledge of deep learning required

Lesson 1 - PyTorch Basics and Gradient Descent

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

Assignment 1 - All About torch.Tensor

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

Lesson 2 - Working with Images and Logistic Regression

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

Assignment 2 - Train Your First Model

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

Lesson 3 - Training Deep Neural Networks on a GPU

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

Assignment 3 - Feed Forward Neural Networks

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

Lesson 4 - Image Classification with Convolutional Neural Networks

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

Lesson 5 - Data Augmentation, Regularization & ResNets

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

Lesson 6: Generative Adversarial Networks and Transfer Learning

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

Project - Train a Deep Learning Model from Scratch

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