Deep Learning with PyTorch: Zero to GANs
Offered By: Jovian
Course Description
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
- Explore the PyTorch documentation website
- Demonstrate usage of some tensor operations
- Publish your Jupyter notebook & share your work
- Training-validation split on the MNIST dataset
- Logistic regression, softmax & cross-entropy
- Model training, evaluation & sample predictions
- Download and explore a real-world dataset
- Create a linear regression model using PyTorch
- Train multiple models and make predictions
- Multilayer neural networks using nn.Module
- Activation functions, non-linearity & backprop
- Training models faster using cloud GPUs
- Explore the CIFAR10 image dataset
- Create a pipeline for training on GPUs
- Hyperparameter tuning & optimization
- Working with 3-channel RGB images
- Convolutions, kernels & features maps
- Training curve, underfitting & overfitting
- Adding residual layers with batchnorm to CNNs
- Learning rate annealing, weight decay & more
- Training a state-of-the-art model in 5 minutes
- Generating fake digits & anime faces with GANs
- Training generator and discriminator networks
- Transfer learning for image classification
- 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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