Deep Learning

From ‘Attention Is All You Need’ to modern mixture-of-experts and state-space hybrids: how the transformer family has evolved and what working engineers should know about the variants in production today.

A practical walkthrough of the 2017 transformer paper that replaced RNNs with self-attention — what the architecture actually does, why the design choices matter in production, and how every modern LLM inherits from it.

Introduction Training state‑of‑the‑art deep‑learning models now routinely requires hundreds of GPUs working in concert. While public cloud providers offer convenient, on‑demand clusters, many research labs and enterprises still prefer bare‑metal clusters for three core reasons: Predictable performance – no noisy neighbors, no hypervisor overhead. Cost efficiency at scale – amortized CAPEX and lower per‑GPU price. Full control over hardware and software – ability to fine‑tune network stacks, install custom drivers, and leverage specialized interconnects. When you combine bare‑metal hardware with RDMA (Remote Direct Memory Access) and low‑latency interconnects such as InfiniBand or RoCE (RDMA over Converged Ethernet), you can dramatically reduce the communication overhead that traditionally limits distributed training speed. This article walks through the entire optimization stack—from networking fundamentals to concrete PyTorch code—so you can extract the maximum throughput from your cluster. ...

Revolutionizing Portfolio Construction: How Deep Neural Networks Jointly Model Returns and Risk Imagine you’re a savvy investor staring at a screen full of stock charts, historical data, and volatility spikes. Traditional investing wisdom tells you to predict future returns based on past averages and estimate risks by crunching covariance matrices—fancy math for how assets move together. But markets aren’t static; they’re wild beasts that shift regimes overnight, from bull runs to crashes. What if an AI could learn both returns and risks simultaneously from the chaos of daily data, spitting out smarter portfolios that actually beat the benchmarks? ...

Introduction Large Language Models (LLMs) such as GPT‑3, LLaMA, and PaLM have demonstrated unprecedented capabilities in natural‑language understanding and generation. However, the sheer scale of these models—often hundreds of millions to billions of parameters—poses a serious challenge for real‑time inference. Latency, memory footprint, and energy consumption become bottlenecks in production environments ranging from interactive chatbots to on‑device assistants. One of the most effective strategies to alleviate these constraints is weight pruning—the systematic removal of redundant or less important parameters from a trained network. While naive pruning can degrade model quality, advanced weight pruning techniques—including structured sparsity, dynamic sparsity, and sensitivity‑aware methods—allow practitioners to dramatically shrink LLMs while preserving, or even improving, their performance. ...