Deep Learning with PyTorch: Zero to GANs

 
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"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

Lesson 1 - PyTorch Basics and Gradient Descent
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  • 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
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  • 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
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  • 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
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  • Discover & explore a large real-world dataset
  • Train a convolutional neural network from scratch
  • Document, present, and publish your work online

Certificate of Accomplishment

Earn a verified certificate of accomplishment (sample) by completing all weekly assignments and the course project. The certificate can be added to your LinkedIn profile, linked from your Resume, and downloaded as a PDF.

Course Prerequisites

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

Instructor - Aakash N S

Aakash N S is the co-founder and CEO of Jovian. Previously, Aakash has worked as a software engineer (APIs & Data Platforms) at Twitter in Ireland & San Francisco and graduated from the Indian Institute of Technology, Bombay. He’s also an avid blogger, open-source contributor, and online educator.