Deep Reinforcement Learning for Trading: Strategy Development & AutoML
In this guide, we discuss the application of deep reinforcement learning to the field of algorithmic trading.
By
Peter Foy
Deep Learning
By
Peter Foy
Deep Reinforcement Learning for Trading with TensorFlow 2.0
In this article we look at how to build a reinforcement learning trading agent with deep Q-learning using TensorFlow 2.0.
By
Peter Foy
Deep Reinforcement Learning: Guide to Deep Q-Learning
In this article, we discuss two important topics in reinforcement learning: Q-learning and deep Q-learning.
By
Peter Foy
Generative Models
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Introduction to Wasserstein GANs with Gradient Penalty
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.
By
Peter Foy
Generative Models
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Introduction to Deep Convolutional GANs (DCGANs)
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.
By
Peter Foy
Generative Models
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Introduction to Generative Adversarial Networks (GANs): Intuition & Theory
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.
By
Peter Foy
An Overview of Deep Reinforcement Learning for Trading
In this article we provide an overview of deep reinforcement learning for trading. Reinforcement learning is the computational science of decision making.
By
Peter Foy
TensorFlow
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Recurrent Neural Networks (RNNs) and LSTMs for Time Series Forecasting
In this article, we review how to use sequence models such as recurrent neural networks (RNNs) and LSTMs for time series forecasting with TensorFlow.
By
Peter Foy
Deep Learning
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Introduction to Convolutional Neural Networks (CNNs) with TensorFlow
In this article, we'll review how to use TensorFlow for computer vision using convolutional neural networks (CNNs).
By
Peter Foy