February 2023

Top 15 ML Libraries to Use in 2023

Top 15 ML Libraries to Use in 2023

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Artificial intelligence is among the fastest-growing industries. The number of open-source ML libraries to which the best programmers contribute new features and functionalities is constantly increasing. With fast-paced advances in machine learning, some ML frameworks and libraries become outdated after a certain period of use. In contrast, others gain momentum thanks to the cutting-edge tools they offer to ML engineers. In this blog post, we present 15 ML libraries to pay attention to in 2023. What is a […]

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Modular Deep Learning

Modular Deep Learning

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This post gives a brief overview of modularity in deep learning. For a more in-depth review, refer to our survey. For modular fine-tuning for NLP, check out our EMNLP 2022 tutorial. For more resources, check out modulardeeplearning.com. Fuelled by scaling laws, state-of-the-art models in machine learning have been growing larger and larger. These models are monoliths. They are pre-trained from scratch in highly choreographed engineering endeavours. Due to their size, fine-tuning has become expensive while alternatives, such as […]

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Some Techniques To Make Your PyTorch Models Train (Much) Faster

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This blog post outlines techniques for improving the training performance of your PyTorch model without compromising its accuracy. To do so, we will wrap a PyTorch model in a LightningModule and use the Trainer class to enable various training optimizations. By changing only a few lines of code, we can reduce the training time on a single GPU from 22.53 minutes to 2.75 minutes while maintaining the model’s prediction accuracy. Yes, that’s a 8x performance boost!

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Optimizing Haskell Code for Runtime Verification

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In this blog post, we will be exploring, describing, and dissecting the first phase of the collaboration between Runtime Verification and Serokell. The collaboration involved work on making optimizations to K, a rewrite-based executable semantic framework in which programming languages, type systems, and formal analysis tools can be defined with the help of configurations and rewrite rules. The blog post will contain: the details and the results of the discovery phase; our recommendations (introduced in the Findings section); […]

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Rust vs. Haskell

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Rust vs. Haskell Rust and Haskell don’t shy away from powerful features. As a result, both languages have steep learning curves when compared with other languages. Trying to learn Rust or Haskell can be frustrating, especially in the first couple of months. But if you already know Rust, you have a head start with Haskell; and vice versa. In this article, we want to show how knowledge of one of these languages can help you get up to […]

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Understanding and Coding the Self-Attention Mechanism of Large Language Models From Scratch

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In this article, we are going to understand how self-attention works from scratch. This means we will code it ourselves one step at a time. Since its introduction via the original transformer paper, self-attention has become a cornerstone of many state-of-the-art deep learning models, particularly in the field of Natural Language Processing. Since self-attention is now everywhere, it’s important to understand how it works.

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Multimodal Learning in ML

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When I started writing about AI in 2019, my first article was dedicated to what machine learning is and how it works. It explained the difference between weak and strong (or general) AI and mentioned that we have no idea how to create the latter. Well, in 2023, maybe we actually (finally!) do. I’m talking about multimodal learning. Don’t get me wrong – multimodal learning wasn’t a new concept already in 2019. Back then, TechRepublic called it the […]

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