Share Description
In this course, you will learn:
- Deep Learning with PyTorch: Zero to GANs" is a beginner-friendly online course delivering a practical and coding-focused introduction to deep learning using the PyTorch framework. Enroll now to start learning.
Syllabus:
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
2. All About torch.Tensor
- Explore the PyTorch documentation website
- Demonstrate usage of some tensor operations
- Publish your Jupyter notebook & share your work
3. Working with Images and Logistic Regression
- Training-validation split on the MNIST dataset
- Logistic regression, softmax & cross-entropy
- Model training, evaluation & sample predictions
4. Train Your First Model
- Download and explore a real-world dataset
- Create a linear regression model using PyTorch
- Train multiple models and make predictions
5. Training Deep Neural Networks on a GPU
- Multilayer neural networks using nn.Module
- Activation functions, non-linearity & backprop
- Training models faster using cloud GPUs
6. Feed Forward Neural Networks
- Explore the CIFAR10 image dataset
- Create a pipeline for training on GPUs
- Hyperparameter tuning & optimization
7. Image Classification with Convolutional Neural Networks
- Working with 3-channel RGB images
- Convolutions, kernels & features maps
- Training curve, underfitting & overfitting
8. 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
9. Generative Adversarial Networks and Transfer Learning
- Generating fake digits & anime faces with GANs
- Training generator and discriminator networks
- Transfer learning for image classification
10. 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
