deep learning

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A Coding Guide to Survey Bias Correction Using Facebook Research Balance with IPW CBPS Ranking and Post Stratification Methods

In this tutorial, we walk through a complete, end-to-end workflow for correcting bias in survey data using the balance library. We simulate a realistic population, deliberately introduce sampling bias, and then apply multiple re-weighting techniques to recover unbiased estimates. We focus on four widely used methods: Inverse Probability Weighting (IPW), Covariate Balancing Propensity Scores (CBPS), […]

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A Coding Implementation of End-to-End Brain Decoding from MEG Signals Using NeuralSet and Deep Learning for Predicting Linguistic Features

In this tutorial, we explore how we can decode linguistic features directly from brain signals using a modern neuroAI pipeline. We work with MEG data and build an end-to-end system that transforms raw neural activity into meaningful predictions, in this case, estimating word length from brain responses. We set up the environment, load and process

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Top 10 KV Cache Compression Techniques for LLM Inference: Reducing Memory Overhead Across Eviction, Quantization, and Low-Rank Methods

As large language models scale to longer context windows and serve more concurrent users, the key-value (KV) cache has emerged as a primary memory bottleneck in production inference systems. For a 30-billion-parameter model with a batch size of 128 and an input length of 1,024 tokens, the resulting KV cache can occupy up to 180

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Mend Releases AI Security Governance Framework: Covering Asset Inventory, Risk Tiering, AI Supply Chain Security, and Maturity Model

There’s a pattern playing out inside almost every engineering organization right now. A developer installs GitHub Copilot to ship code faster. A data analyst starts querying a new LLM tool for reporting. A product team quietly embeds a third-party model into a feature branch. By the time the security team hears about any of it,

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Mend.io Releases AI Security Governance Framework Covering Asset Inventory, Risk Tiering, AI Supply Chain Security, and Maturity Model

There’s a pattern playing out inside almost every engineering organization right now. A developer installs GitHub Copilot to ship code faster. A data analyst starts querying a new LLM tool for reporting. A product team quietly embeds a third-party model into a feature branch. By the time the security team hears about any of it,

Mend.io Releases AI Security Governance Framework Covering Asset Inventory, Risk Tiering, AI Supply Chain Security, and Maturity Model Read More »

A Detailed Implementation on Equinox with JAX Native Modules, Filtered Transforms, Stateful Layers, and End-to-End Training Workflows

In this tutorial, we explore Equinox, a lightweight and elegant neural network library built on JAX, and show how to use it. We begin by understanding how eqx.Module treats models as PyTrees, which makes parameter handling, transformation, and serialization feel simple and explicit. As we move forward, we work through static fields, filtered transformations such

A Detailed Implementation on Equinox with JAX Native Modules, Filtered Transforms, Stateful Layers, and End-to-End Training Workflows Read More »

A Coding Implementation to Build a Conditional Bayesian Hyperparameter Optimization Pipeline with Hyperopt, TPE, and Early Stopping

In this tutorial, we implement an advanced Bayesian hyperparameter optimization workflow using Hyperopt and the Tree-structured Parzen Estimator (TPE) algorithm. We construct a conditional search space that dynamically switches between different model families, demonstrating how Hyperopt handles hierarchical and structured parameter graphs. We build a production-grade objective function using cross-validation inside a scikit-learn pipeline, enabling

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A Coding Implementation on Microsoft’s Phi-4-Mini for Quantized Inference Reasoning Tool Use RAG and LoRA Fine-Tuning

In this tutorial, we build a pipeline on Phi-4-mini to explore how a compact yet highly capable language model can handle a full range of modern LLM workflows within a single notebook. We begin by setting up a stable environment, loading Microsoft’s Phi-4-mini-instruct in efficient 4-bit quantization, and then move step by step through streaming

A Coding Implementation on Microsoft’s Phi-4-Mini for Quantized Inference Reasoning Tool Use RAG and LoRA Fine-Tuning Read More »

OpenAI Scales Trusted Access for Cyber Defense With GPT-5.4-Cyber: a Fine-Tuned Model Built for Verified Security Defenders

Cybersecurity has always had a dual-use problem: the same technical knowledge that helps defenders find vulnerabilities can also help attackers exploit them. For AI systems, that tension is sharper than ever. Restrictions intended to prevent harm have historically created friction for good-faith security work, and it can be genuinely difficult to tell whether any particular

OpenAI Scales Trusted Access for Cyber Defense With GPT-5.4-Cyber: a Fine-Tuned Model Built for Verified Security Defenders Read More »

A Coding Guide to Build a Production-Grade Background Task Processing System Using Huey with SQLite, Scheduling, Retries, Pipelines, and Concurrency Control

In this tutorial, we explore how to build a fully functional background task processing system using Huey directly, without relying on Redis. We configure a SQLite-backed Huey instance, start a real consumer in the notebook, and implement advanced task patterns, including retries, priorities, scheduling, pipelines, locking, and monitoring via signals. As we move step by

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