In this step-by-step tutorial, you'll learn all about one of the most exciting areas of research in the field of machine learning: generative adversarial networks.
Generative adversarial networks (GANs) are neural networks that generate material, such as images, music, speech, or text, that is similar to what humans produce.
GANs have been an active topic of research in recent years. Facebook’s AI research director Yann LeCun called adversarial training “the most interesting idea in the last 10 years” in the field of machine learning. Below, you’ll learn how GANs work before implementing two generative models of your own.
In this tutorial, you’ll learn:
Let’s get started!
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Generative adversarial networks are machine learning systems that can learn to mimic a given distribution of data. They were first proposed in a 2014 NeurIPS paper by deep learning expert Ian Goodfellow and his colleagues.
GANs consist of two neural networks, one trained to generate data and the other trained to distinguish fake data from real data (hence the “adversarial” nature of the model). Although the idea of a structure to generate data isn’t new, when it comes to image and video generation, GANs have provided impressive results such as:
Neural networks, as their name implies, are computer algorithms modeled after networks of neurons in the human brain. Learn more about neural networks from Algorithmia.
Recurrent neural networks, also known as RNNs, are a class of neural networks that allow previous outputs to be used as inputs while having hidden states.
The purpose of this project is to build and evaluate Recurrent Neural Networks(RNNs) for sentence-level classification tasks. Let's understand about recurrent neural networks for multilabel text classification tasks.
Artificial neural networks (ANNs), usually simply called neural networks (NNs), are computing systems vaguely inspired by the biological neural networks that constitute animal brains.
Convolutional Neural Network: How is it different from the other networks? What’s so unique about CNNs and what does convolution really do? This is a math-free introduction to the wonders of CNNs.