Why Use Generative Models?
Explore why generative models are crucial in AI by understanding their ability to create new data, improve datasets, and address challenges like bias and data scarcity. Discover their applications in image generation, classification, and enhancing machine learning performance.
We'll cover the following...
Why do we have a need for generative models in the first place? What value do they provide in practical applications? To answer this question, let’s take a brief discuss the topics that form the basis of generation using deep learning.
The promise of deep learning
Many of the models we’ll survey in the course are deep, multi-level neural networks. The last 15 years have seen a renaissance in the development of deep learning models for image classification, natural language processing and understanding, and reinforcement learning. These advances were enabled by breakthroughs in traditional challenges in tuning and optimizing very complex models, combined with access to larger datasets, distributed computational power in the cloud, and frameworks such as TensorFlow that make it easier to prototype and reproduce research.
Building a better digit classifier
A classic problem used to benchmark algorithms in machine learning and computer vision is the task of classifying which handwritten digit from