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Articles about computational science and data science, neuroscience, and open source solutions. Personal stories are filed under Weekend Stories. Browse all topics here. All posts are CC BY-NC-SA licensed unless otherwise stated. Feel free to share, remix, and adapt the content as long as you give appropriate credit and distribute your contributions under the same license.
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PyTorch on Apple Silicon
Already some time ago, PyTorch became fully available for Apple Silicon. It’s no longer necessary to install the nightly builds to run PyTorch on the GPU of your Apple Silicon machine as I described in one of my earlier posts.
Rössler attractor
Unlike the Lorenz attractor which emerges from the dynamics of convection rolls, the Rössler attractor does not describe a physical system found in nature. Instead, it is a mathematical construction designed to illustrate and study the behavior of chaotic systems in a simpler, more accessible manner. In this post, we explore how we can quickly simulate this strange attractor using simple Python code.
Understanding Hebbian learning in Hopfield networks
Hopfield networks, a form of recurrent neural network (RNN), serve as a fundamental model for understanding associative memory and pattern recognition in computational neuroscience. Central to the operation of Hopfield networks is the Hebbian learning rule, an idea encapsulated by the maxim ‘neurons that fire together, wire together’. In this post, we explore the mathematical underpinnings of Hebbian learning within Hopfield networks, emphasizing its role in pattern recognition.
Building a neural network from scratch using NumPy
Ever thought about building you own neural network from scratch by simply using NumPy? In this post, we will do exactly that. We will build, from scratch, a simple feedforward neural network and train it on the MNIST dataset.
Python’s version logos
Have you ever noticed that Python has introduced individual version logos starting with version 3.10? I couldn’t find any official announcement, but luckily, the Python community on Mastodon was able to help out.
Conditional GANs
I was wondering whether it would be possible to let GANs generate samples conditioned on a specific input type. I wanted the GAN to generate samples of a specific digit, resembling a personal poor man’s mini DALL•E. And indeed, I found a GAN architecture, that allows what I was looking for: Conditional GANs.
Eliminating the middleman: Direct Wasserstein distance computation in WGANs without discriminator
We explore an alternative approach to implementing WGANs. Contrasting from the standard implementation that requires both a generator and discriminator, the method discussed here employs the optimal transport to compute the Wasserstein distance directly between the real and generated data distributions, eliminating the need for a discriminator.
Wasserstein GANs
We apply the Wasserstein distance to Generative Adversarial Networks (GANs) to train them more effectively. We compare a default GAN with a Wasserstein GAN (WGAN) trained on the MNIST dataset and discuss the advantages and disadvantages of both approaches.
Probability distance metrics in machine learning
Probabilistic distance metrics play a crucial role in a broad range of machine learning tasks, including clustering, classification, and information retrieval. The choice of metric is often determined by the specific requirements of the task at hand, with each having unique strengths and characteristics. In this post, we discuss five commonly used metrics: the Wasserstein Distance, the Kullback-Leibler Divergence (KL Divergence), the Jensen-Shannon Divergence (JS Divergence), the Total Variation Distance (TV Distance), and the Bhattacharyya Distance.