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
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
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- 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
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- Multilayer neural networks using nn.Module
- Activation functions, non-linearity & backprop
- Training models faster using cloud GPUs
Assignment 3 - Feed Forward Neural Networks
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- Explore the CIFAR10 image dataset
- Create a pipeline for training on GPUs
- Hyperparameter tuning & optimization
Lesson 4 - Image Classification with Convolutional Neural Networks
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- Working with 3-channel RGB images
- Convolutions, kernels & features maps
- Training curve, underfitting & overfitting
Lesson 5 - Data Augmentation, Regularization & ResNets
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- 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
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- 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.