Machine Learning Mastery
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Floating-Point Precision and Its Effects to Model Inference
Overview This post is divided into five parts; they are: • How Floating-Point Numbers Work • Common Precision Formats • Loading a Model at a Different Precision • Mixed Precision and Sensitive Operations • Measuring the Result How Floating-Point Numbers Work A floating-point number contains a sign…
DiffusionGemma: Hands-On with Google’s Text Model That Writes All at Once
In this article, you will learn what DiffusionGemma is, how its diffusion-based text generation differs from standard autoregressive language models, and how to run it...
Free AI and Data Courses with 365 Data Science: 100% Unlimited Access until Nov 20
Sponsored Content From November 5 to November 20, 2026 (starting at 8:00 a.m. UTC), 365 Data Science will grant free access to...
Decorators for Timing, Retries, Caching, and Validation in Python
In this article, you will learn how Python decorators work under the hood and how to build reliable ones for timing, retries, caching, and input...
How to Train Your own Decision Model like Jev using Unsloth
In this article, you will learn how to turn an open-weight language model into a decision model that classifies user intent, by fine-tuning Qwen3.5-4B on...
Choosing an LLM Inference Optimization Technique: A Decision-Tree Approach
In this article, you will learn how to match an LLM inference optimization technique to the actual bottleneck in your workload by working through a...
The AI Agent Memory Stack: Building Blocks, Patterns, and Frameworks
In this article, you will learn how agent memory works, what types of memory exist, and how different frameworks approach storage, retrieval, and memory management...
7 Ways Your AI Agent Is Wasting Tokens (And What to Do Instead)
In this article, you will learn seven common token-waste patterns in production AI agent systems, and the architectural fixes that address each one. Topics we...
Fine-Tuning vs. Frozen Embeddings: When to Adapt the LLM vs. the Sklearn Head
In this article, you will learn how to compare two strategies for adapting a large language model to a specific domain: fine-tuning with LoRA versus...
Context Managers Beyond Opening Files in Python
The stopwatch in the opening script is the smallest case of the pattern.
Robust Serialization with JSON and Custom Types
In this article, you will learn how to build a robust JSON serialization pipeline in Python that handles custom data types, graceful error recovery, and...
Speculative Decoding Explained: How to Get 3× Faster Inference from Any LLM
In this article, you will learn how speculative decoding works, why it produces real speed gains at the hardware level, and what modern implementations look...
How to Build a Multi-Modal RAG Pipeline for Images and Documents in Python
In this article, you will learn how to build a multi-modal RAG pipeline in Python that retrieves and reasons over both text and images extracted...
pgvector vs. Dedicated Vector Databases: A Production Benchmark
Run this on a typical laptop and both systems will report latencies in the low single-digit to tens of milliseconds range, with the exact numbers depending heavily on your CPU, available RAM, and whatever else is running on your machine at the time.
Descriptors and Controlled Attributes in Python
In this article, you will learn what Python descriptors are and how to use them to build three types of controlled attributes: validated, computed, and...
Iterators and Generators for Memory-Efficient Processing
Laziness is not a religion.
Choosing a Chunking Strategy for Your LLM Apps: A Decision-Tree Approach
In this article, you will learn how to choose the right chunking strategy for a retrieval-augmented generation system by working through a structured decision tree...
Building a Custom LLM Evaluation Harness in Python from Scratch
In this article, you will learn how to build a custom LLM evaluation harness in Python from scratch, structured around four modular components that give...
Building a RAG Pipeline with LLM Embeddings and Scikit-learn
In this article, you will learn how to build a lightweight, end-to-end RAG pipeline using scikit-learn for vector retrieval and open-source models for embeddings and...
AI Agent Benchmarks Explained: SWE-Bench, GAIA, WebArena, and More
In this article, you will learn what the major AI agent benchmarks measure, how they differ from traditional model benchmarks, and how to use them...
In the directory since 25 Sept 2026 · last checked 9 Nov 2026 · RSS