https://thecodersblog.com/tag/neural-networks/Recent content in Neural Networks on The Coders BlogHugoen-usWed, 06 May 2026 22:07:47 +0000https://thecodersblog.com/a-theory-of-deep-learning-2026/Wed, 06 May 2026 22:07:47 +0000https://thecodersblog.com/a-theory-of-deep-learning-2026/<p>The practice of deep learning has long outpaced its theoretical underpinnings, leaving us with a powerful toolset that often feels more like sophisticated alchemy than rigorous science. We can train models that achieve superhuman performance, yet the fundamental reasons for their generalization, especially in the face of extreme overparameterization, remain elusive, forcing us to rely on empirical risk minimization and the hope that it won&rsquo;t spectacularly fail. This gap is precisely what Elon Litman&rsquo;s recent work seeks to bridge, proposing a radical shift in how we analyze and understand neural networks.</p>https://thecodersblog.com/vector-space-reasoning-for-llms-2026/Wed, 29 Apr 2026 11:24:51 +0000https://thecodersblog.com/vector-space-reasoning-for-llms-2026/<p>We&rsquo;ve pushed natural language to its absolute limits with LLMs, but a nagging question persists: Is language itself the bottleneck to true, robust AI reasoning? I argue, emphatically, yes. The continuous, multi-dimensional world of <strong>vector space</strong> is not just an augmentation for Large Language Models; it is the fundamental arena where advanced AI reasoning must occur. Ignoring this imperative ensures we will perpetually chase diminishing returns in textual processing.</p> <h2 id="the-language-trap-why-textual-reasoning-is-fundamentally-suboptimal">The Language Trap: Why Textual Reasoning is Fundamentally Suboptimal</h2> <p>Natural language, for all its expressive power, is a system built on inherent <strong>ambiguity</strong> and <strong>polysemy</strong>. When we ask an LLM to reason purely in tokens, we force it to navigate a minefield of potential misinterpretations. This fundamental noisiness isn&rsquo;t a bug in current LLMs; it&rsquo;s an inherent feature of language itself, contributing directly to phenomena like &lsquo;hallucinations&rsquo; not as system failures, but as artifacts of an imprecise medium.</p>