In this course, you will :
- Get comfortable with most commonly used PyTorch concepts, modules and API including Tensor operations, data representations, and manipulation.
- Work with Deep Learning models and architectures including layers, activations, loss functions, gradients, chain rule, forward and backward passes, and optimizers.
- Apply Deep Learning architectures to solve Machine Learning problems for Structured Datasets, Computer Vision, and Natural Language Processing.
- Utilize the concept of Transfer Learning by using pre-trained Deep Learning models to your own problems.
- Implement state of the art in Natural Language Processing to solve real-world problems such as sentiment analysis.
- Implement a simple Generative Adversarial Network to generate fancy images after training on a large image dataset.