Deep Learning

19 posts
Generative Models
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Introduction to Wasserstein GANs with Gradient Penalty

Peter Foy
Peter Foy
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.…
Generative Models
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Introduction to Deep Convolutional GANs (DCGANs)

Peter Foy
Peter Foy
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 Models
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Introduction to Generative Adversarial Networks (GANs): Intuition & Theory

Peter Foy
Peter Foy
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.…
TensorFlow
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Recurrent Neural Networks (RNNs) and LSTMs for Time Series Forecasting

Peter Foy
Peter Foy
In this article, we review how to use sequence models such as recurrent neural networks (RNNs) and LSTMs for time series forecasting with TensorFlow.…
Deep Learning
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Introduction to Convolutional Neural Networks (CNNs) with TensorFlow

Peter Foy
Peter Foy
In this article, we'll review how to use TensorFlow for computer vision using convolutional neural networks (CNNs).…
Reinforcement Learning
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Deep Reinforcement Learning for Trading: Strategy Development & AutoML

Peter Foy
Peter Foy
Deep reinforcement learning has shown promise in many other fields, and it's likely that it will have a significant impact on the financial industry in the coming years.…
Reinforcement Learning
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Implementing Deep Reinforcement Learning with PyTorch: Deep Q-Learning

Peter Foy
Peter Foy
In this article we will look at several implementations of deep reinforcement learning with PyTorch.…
Deep Learning
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An Introduction to DeepDream with TensorFlow 2.0

Peter Foy
Peter Foy
DeepDream is a powerful computer vision algorithm that uses a convolutional neural network to find and enhance certain patterns in images.…
Machine Learning
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Introduction to Transfer Learning with TensorFlow 2.0

Peter Foy
Peter Foy
Transfer learning is a machine learning technique in which a pre-trained network is repurposed as a starting point for another similar task.…
Reinforcement Learning
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Deep Reinforcement Learning for Trading with TensorFlow 2.0

Peter Foy
Peter Foy
In this article we look at how to build a reinforcement learning trading agent with deep Q-learning using TensorFlow 2.0.…