93 stories
In the last article, CNNs showed us how a network can understand where things are in an image. But some data isn’t about space at all. It’s about order. Read this sentence backwards and it stops making sense. Shuffle a week of stock prices and the trend disappears.
AWS's Strands Agents team released Strands Decider 2B, an Apache-2.0 decision model built on Qwen3.5-2B-Base. It returns choices, yes/no probabilities and scores with calibrated confidence in one forward pass, never text.
In this comprehensive coding guide, we explore Google Research's Kauldron—a JAX training library optimized for research velocity and modularity. Learn how konfig turns experiments into plain dictionaries, kontext wires components via string paths, and ktyping enforces runtime shape checks.
What fractals, loss landscapes, and dynamical stability reveal about adaptation under constraint
From Token to Trajectory How Context Reshapes Representations — and What AI Systems Do with Them The suitcase felt surprisingly light. The color looked surprisingly light. The same token can begin …
The activation function was never a fixed biological commitment but a working hypothesis revised under empirical pressure. The post What the ReLU Revolution Revealed About Biological Plausibility appeared first on Towards Data Science.
How Does a Neural Network Learn? Neurons, Activation Functions and Loss A neural network starts from a surprisingly simple idea: Take some numbers, multiply them by weights, add a bias, and transform …
A small, strange discovery in machine learning called grokking The post The AI That Learned to Understand Long After It Stopped Trying appeared first on Towards Data Science.
Why I'd invest in post-training existing open-weight LLMs, with Fireworks' Ember-1 as an example of more token-efficient reasoning.
Autoregressive rollout and uncertainty propagation. Second in a series on probabilistic forecasting for physical signals. The post Your Model's MSE Is Lying to You: Part II appeared first on Towards Data Science.
Notes on MiMo-V2.6 Pro's GQA and sliding-window attention, agent training tasks, reward signals, and large RL batches.
A short note on Jev's generalization, possible encoder-style architecture and training, and Choice and Noul API examples.
A radiologist can mark a tumor on a CT scan by drawing a straight line across it. Creating a complete 3D segmentation requires outlining the tumor across the slices where it appears, which is more tim
My take on AI model pacing as a framework for release checks and the competitive pressure around model releases.
A 90-minute LinkedIn Learning course on how reasoning models relate to conventional LLMs and how they are developed.
You can finish three deep learning courses, build a CNN, and still find yourself stuck when the same model stops working on your own dataset. Maybe changing a few parameters does nothing, the validation loss starts behaving strangely, or the model simply refuses to learn.
A short note on OpenAI Astra, recurrent depth, looped transformers, Nanbeige 4.2, and the Mixture-of-Recursions paper.
A short video on how conventional LLMs relate to reasoning models and agents, along with Python and PyTorch setup using uv.
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