DCGANs: Key Takeaways

When we use labeled data to train a machine-learning algorithm (e.g. When we use unlabeled data to train a machine-learning algorithm and allow it to find patterns in the data(e.g. Using dimensionality reduction to transform raw data into numerical features that can be processed with machine learning that contain information about the original data (e.g. An unsupervised learning task where the algorithm learns patterns in input data to generate new examples (fake data) that would appear to have been drawn from the original dataset. The part of the GAN that generates fake data.