Learning Machine Learning

Learning Machine Learning

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Machine learning is one of the most interesting and advancing aspects of engineering and computer science. It shows you the potential of machines in our modern day world. Machine learning is essentially making a machine learn to perform different tasks by feeding enormous amounts of data. To accomplish this, there are multiple algorithms used for machines to learn and adapt to perform a task easier with new inputs. One of the algorithms is called neural networks. A Neural […]

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If AI is centralized today, it is not a law of nature

If AI is Centralized Today, It Is Not A Law of Nature

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Author(s): Jan Olsen Originally published on Towards AI. How the evolution of computing hardware is reopening the path toward decentralized intelligence — and why we must organize now Muir Woods National Monument is part of California’s Golden Gate National Recreation Area, north of San Francisco. It’s known for its towering old-growth redwood trees. Trails wind among the trees to Cathedral Grove and Bohemian Grove, and along Redwood Creek. The Ben Johnson and Dipsea trails climb a hillside for […]

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Interesting categories are big

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One of the things I found off-putting about category theory when I was first exposed to it was its reliance on the notion of “collections” that are not sets. That seemed to place the entire theory on dubious foundations with paradoxes looming around every corner. It turns out you can mostly ignore such issues in application. You can, for example, talk about the forgetful functor that maps a group to the set of its elements, ignoring the group […]

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Online Passive-Aggressive Algorithms on a Budget

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In this paper a kernel-based online learning algorithm, which has both constant space and update time, is proposed. The approach is based on the popular online Passive-Aggressive (PA) algorithm. When used in conjunction with kernel function, the number of support vectors in PA grows without bounds when learning from noisy data streams. This implies unlimited memory and ever increasing model update and prediction time. To address this issue, the proposed budgeted PA algorithm maintains only a fixed number […]

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Advanced Vulnerability Scanning for Open Source Software: Detection and Mitigation of Log4j Vulnerabilities

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arXiv:2601.00235v1 Announce Type: new Abstract: Automated detection of software vulnerabilities remains a critical challenge in software security. Log4j is an industrial-grade Java logging framework listed as one of the top 100 critical open source projects. On Dec. 10, 2021 a severe vulnerability Log4Shell was disclosed before being fully patched with Log4j2 version 2.17.0 on Dec. 18, 2021. However, to this day about 4.1 million, or 33 percent of all Log4j downloads in the last 7 days contain vulnerable […]

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Latent Flow Matching for Expressive Singing Voice Synthesis

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arXiv:2601.00217v1 Announce Type: new Abstract: Conditional variational autoencoder (cVAE)-based singing voice synthesis provides efficient inference and strong audio quality by learning a score-conditioned prior and a recording-conditioned posterior latent space. However, because synthesis relies on prior samples while training uses posterior latents inferred from real recordings, imperfect distribution matching can cause a prior-posterior mismatch that degrades fine-grained expressiveness such as vibrato and micro-prosody. We propose FM-Singer, which introduces conditional flow matching (CFM) in latent space to learn a […]

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New method improves the reliability of statistical estimations

New method improves the reliability of statistical estimations

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Let’s say an environmental scientist is studying whether exposure to air pollution is associated with lower birth weights in a particular county. They might train a machine-learning model to estimate the magnitude of this association, since machine-learning methods are especially good at learning complex relationships. Standard machine-learning methods excel at making predictions and sometimes provide uncertainties, like confidence intervals, for these predictions. However, they generally don’t provide estimates or confidence intervals when determining whether two variables are related. […]

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The Machine Learning “Advent Calendar” Bonus 2: Gradient Descent Variants in Excel

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Gradient Descent, Momentum, RMSProp, and Adam all aim for the same minimum. They do not change the destination, only the path. Each method adds a mechanism that fixes a limitation of the previous one, making the movement faster, more stable, or more adaptive. The goal stays the same. The update becomes smarter. The post The Machine Learning “Advent Calendar” Bonus 2: Gradient Descent Variants in Excel appeared first on Towards Data Science.

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SOLID Design Principles: Improve Object-Oriented Code in Python

SOLID Design Principles: Improve Object-Oriented Code in Python

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A great approach to writing high-quality object-oriented Python code is to consistently apply the SOLID design principles. SOLID is a set of five object-oriented design principles that can help you write maintainable, flexible, and scalable code based on well-designed, cleanly structured classes. These principles are foundational best practices in object-oriented design. In this tutorial, you’ll explore each of these principles with concrete examples and refactor your code so that it adheres to the principle at hand. By the […]

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