id stringlengths 12 21 | username stringclasses 6
values | license stringclasses 6
values | title stringlengths 34 98 | publication_description stringlengths 4.41k 109k |
|---|---|---|---|---|
0CBAR8U8FakE | 3rdson | none | How to Add Memory to RAG Applications and AI Agents | 
Sometime in the last 5 months, I built a RAG application, and after building this RAG application, I realised there was a need to add memory to it before moving it to production. I went on YouTube and searched for videos, but I couldn’t find anything meaningful. I saw some video... |
0hkuicWh2tKk | regmi.prakriti24 | Hands on Computer Vision: Build Production-Grade Models in an Hour | :::youtube[Title]{#8em2GBD0H8g}
--DIVIDER--
---
--DIVIDER--# Learning Objectives
> *In this notebook, we will explore the practical implementations of some primal CV tasks like image classification, image segmentation, and object detection using modern computer vision techniques leveraging some popular pre-trained mode... | |
0llldKKtn8Xb | ready-tensor | cc-by | The Open Source Repository Guide: Best Practices for Sharing Your AI/ML and Data Science Projects | 
<p align="center"><em>Image credit: https://www.pexels.com</em></p>
--DIVIDER--
# Abstract
This article presents a comprehensive framework for creating and structuring AI/ML project repositories that maximize accessibility, reproducibility, and community benefit. We in... |
0z4EC8313LzS | ready-tensor | mit | Time Series Step Classification Benchmark |
--DIVIDER--# Introduction
In the field of time series analysis, step classification plays a critical role in interpreting sequential data by assigning class labels to each time step. This study presents a comprehensive benchmark of 25 machine learning models trained on five distinct datasets aime... |
1yiSfLXTffSF | aryan_patil | none | UV: The Next Generation Python Package Manager Built for Speed |
--DIVIDER--# TL;DR
UV is a Rust-built Python package manager that's 10-100x faster than pip/poetry/conda, combining virtual environment creation and dependency management in one tool while maintaining compatibility with existing Python standards.--DIVIDER--# Introduction
The evolution in Python has b... |
4SAKUg8ciBuV | ready-tensor | cc-by-sa | Image compression with Auto-Encoders |
--DIVIDER--# Introduction to Auto-Encoders
In the field of data compression, traditional methods have long dominated, ranging from lossless techniques such as ZIP file compression to lossy techniques like JPEG image compression and MPEG video compression. These methods are typically rule-based, ut... |
57Nhu0gMyonV | ready-tensor | mit | Building CLIP from Scratch: A Tutorial on Multi-Modal Learning |
--DIVIDER--# Abstract
This work provides a comprehensive implementation of Contrastive Language-Image Pretraining (CLIP) from the ground up. CLIP, introduced by OpenAI, jointly trains image and text encoders using contrastive learning to align visual and textual representations in a s... |
82lYI7TWVtvP | 3rdson | cc-by | Core concepts of Agentic AI and AI agents | 
Over the past year, there has been immense hype and discussion around AI, particularly **GenAI**, **Agentic AI**, and **RAG systems**. This buzz has sparked significant shifts across industries, with everyone scrambling to understand: *What exactly are agents? ... |
8eAX8A1gfdkJ | ready-tensor | cc-by-sa | Transformer Models for Automated PII Redaction: A Comprehensive Evaluation Across Diverse Datasets |
 --DIVIDER--# TL;DR
We automated PII redaction using transformer models like RoBERTa and DeBERTa, assessing their effectiveness on five datasets. RoBERTa was selected for its balance of performance and efficiency. The study introduced a redaction script combining ... |
AewIJAspNLZz | mo.abdelhamid | Ranking Fear Emotions Using EEG and Machine Learning | "\n--DIVIDER--# Abstract\n\nThis publication focuses on the classification of f(...TRUNCATED) |
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