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Deep Learning with PyTorch: Zero to GANs

via Jovian

Overview

"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

Syllabus

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

Taught by

Aakash N S

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