PyTorch

In this capstone, learners will apply their deep learning knowledge and expertise to a real world challenge. They will use a library of their choice to develop and test a deep learning model. They will load and pre-process data for a real problem, build the model and validate [...]

In this course, you will: explore the applications of GANs and examine them wrt data augmentation, privacy, and anonymity; leverage the image-to-image translation framework and identify applications to modalities beyond images; implement Pix2Pix, a paired image-to-image translation GAN, to adapt satellite images into map routes (and vice versa); compare [...]

In this course, you will: Assess the challenges of evaluating GANs and compare different generative models; Use the Fréchet Inception Distance (FID) method to evaluate the fidelity and diversity of GANs; Identify sources of bias and the ways to detect it in GANs; Learn and implement the techniques associated [...]

In this course, you will: Learn about GANs and their applications; Understand the intuition behind the fundamental components of GANs; Explore and implement multiple GAN architectures; Build conditional GANs capable of generating examples from determined categories.

The course will teach you how to develop deep learning models using Pytorch. The course will start with Pytorch's tensors and Automatic differentiation package. Then each section will cover different models starting off with fundamentals such as Linear Regression, and logistic/softmax regression.

This course is the second part of a two-part course on how to develop Deep Learning models using Pytorch. In the first course, you learned the basics of PyTorch; in this course, you will learn how to build deep neural networks in PyTorch. Also, you will learn how [...]