forward pass
Beyond Market Intelligence keeps forward pass in one place: 3 stories so far. The section currently leads with “Explore how Usul turns complex neural networks into simple math”, “Gradient accumulation speed varies more than expected across GPU setups”, and “Explore how GPT-2 maps tokens with an interactive visual graph.”. If you're tired of treating neural networks like black boxes summoned by opaque frameworks, Usul-cpp offers a refreshing alternative. Conventional wisdom says a batch of four is a batch of four, but this test on LoRA with Qwen3-1.7B shows otherwise. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every forward pass story on Beyond Market Intelligence, newest first.
Explore how Usul turns complex neural networks into simple math
If you're tired of treating neural networks like black boxes summoned by opaque frameworks, Usul-cpp offers a refreshing alternative. This lightweight C++ library lets you express models as pure math, tensors, forward passes, and backpropagation written out in clear, readable code. The XOR example shown here is elegantly simple: define your weights, write the arithmetic, and let Usul handle gradient descent. It's a human-centered approach that demystifies deep learning without sacrificing precision or performance.
Gradient accumulation speed varies more than expected across GPU setups
Conventional wisdom says a batch of four is a batch of four, but this test on LoRA with Qwen3-1.7B shows otherwise. The user found that on a T4, running four micro-batches before one optimizer step was 17% slower than a single physical batch of four. On an L4, that gap stretched to 41%. The difference is execution shape, not just optimization math. Treating effective batch and physical batch as the same knob is a mistake.
Explore how GPT-2 maps tokens with an interactive visual graph.
There's a quiet thrill in tapping a single token and watching its nearest kin appear. This interactive map of GPT-2's token embedding space makes that possible, offering 32,070 alphabetic tokens to explore with a pinch or a tap. No forward pass, no context, just raw relationships laid bare. The t-SNE layout and minimum spanning tree ensure every line represents a genuine neighbourly bond. It's a hands-on way to grasp how the model organises language, and it works beautifully on mobile.