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In this guide, we discuss variational autoencoders, which combine techniques from deep learning and Bayesian machine learning, specifically variational inference.
In this article, we discuss various applications of classification-based machine learning in finance, including logistic regression for predicting asset returns.
A recurrent neural network (RNN) attempts to model time-based or sequence-based data. An LSTM network is a type of RNN that uses special units as well as standard units.
In this guide, we discuss the application of deep reinforcement learning to the field of algorithmic trading.
In this article we look at how to build a reinforcement learning trading agent with deep Q-learning using TensorFlow 2.0.
In this article we cover an important topic in reinforcement learning: Q-learning and deep Q-learning.
In this article, we discuss the Wasserstein loss function for Generative Adversarial Networks (GANs), which solves a common issue that arises during the training process.
In this article, we discuss the key components of building a DCGAN for the purpose of image generation. This includes activation functions, batch normalization, convolutions, pooling and upsampling, and transposed convolutions.
Generative Adversarial Networks, or GANs, are an emergent class of deep learning that have been used for everything from creating deep fakes, synthetic data, creating NFT art, and more.
