Loss functions aren’t magic. They aren’t some arbitrary design choice that researchers pulled out of thin air. Every loss function you’ve ever seen has a story, a logical, step-by-step derivation rooted in probability theory. By the end of this piece, …
Mastering high-performance vector search in 2026.Vector search is the backbone of modern AI. It enables Semantic Search, Retrieval-Augmented Generation (RAG), and recommendation systems. Relying on managed services is common. However, building your own…
The Core Concepts Behind Data Analysis, Machine Learning, and Scientific DiscoveryContinue reading on Towards AI »
Photo by authorHow I built a molecular similarity search system using ChemBERTa, RDKit, and a vector database, and what I learned along the way.Why I Wanted to Do ThisI have been spending a lot of time lately experimenting with vector databases and emb…
During the weekend, I scrolled through Twitter to see what was happening in the AI community. Google has announced its first native…Continue reading on Towards AI »
Learn when LangGraph plans, Temporal executes, and how checkpoints plus webhooks keep 10,000-item LLM pipelines durable.Continue reading on Towards AI »
An end-to-end computer vision project using LBPH, Tkinter, and classical techniquesContinue reading on Towards AI »
Read my experiences from using OpenClaw.Continue reading on Towards AI »
In this tutorial, we explore how to solve differential equations and build neural differential equation models using the Diffrax library. We begin by setting up a clean computational environment and installing the required scientific computing libraries such as JAX, Diffrax, Equinox, and Optax. We then demonstrate how to solve ordinary differential equations using adaptive solvers […]
The post A Coding Guide to Implement Advanced Differential Equation Solvers, Stochastic Simulations, and Neural Ordinary Differential Equations Using Diffrax and JAX appeared first on MarkTechPost.
The scaling of inference-time compute has become a primary driver for Large Language Model (LLM) performance, shifting architectural focus toward inference efficiency alongside model quality. While Transformer-based architectures remain the standard, their quadratic computational complexity and linear memory requirements create significant deployment bottlenecks. A team of researchers from Carnegie Mellon University (CMU), Princeton University, Together […]
The post Meet Mamba-3: A New State Space Model Frontier with 2x Smaller States and Enhanced MIMO Decoding Hardware Efficiency appeared first on MarkTechPost.