Support Vector Machine (SVM) algorithm with a focus on classification tasks, using a simple 2D dataset for illustration. It explains key concepts like hard and soft margins, support vectors, kernel tricks, and optimization probles.
Hallux.ai provides open-source solutions leveraging Large Language Models (LLMs) to streamline operations and enhance productivity for Production Engineers, SRE, and DevOps. Offering cutting-edge CLI tools for Linux and MacOS, they automate workflows, accelerate root cause analysis, empower self-sufficiency, and optimize daily tasks.
Improving the memory and computational efficiency of Large Language Models (LLMs) for handling long input sequences, including retrieval augmented questions answering, summarization, and chat tasks. It covers various techniques, such as lower precision computing, Flash Attention algorithm, positional embedding methods, and key-value caching strategies. These methods help reduce memory consumption and increase inference speeds while maintaining high accuracy levels in LLM applications. Furthermore, it highlights some advanced approaches like Multi-Query-Attention (MQA) and Grouped-Query-Attention (GQA), which further enhance computational and memory efficiency without compromising performance.
How computationally optimized prompts make language models excel, and how this all affects prompt engineering
Elbow curve and Silhouette plots both are very useful techniques for finding the optimal K for K-means clustering
