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 |
--DIVIDER--# Abstract
This publication focuses on the classification of fear emotions using EEG signals and machine learning techniques. The study explores how different levels of fear can be distinguished based on power variations across various EEG frequency bands (Alpha, Beta, Theta, Delta, Ga... | |
dLPDzlkDb51e | ready-tensor | cc-by-sa | From Thousands to Millions: A Flexible Tool for Generating Scalable TSP Datasets |
--DIVIDER--# TL;DR
We present an open-source tool for generating large-scale Traveling Salesman Problem (TSP) datasets in an efficient format, overcoming TSPLIB limitations. The tool is flexible and supports extensive training data generation, enabling modern ML approac... |
DM3Ao23CIocT | ready-tensor | cc-by-sa | Python Docstrings for Machine Learning Models |
--DIVIDER--tl;dr
In this tutorial, you will learn how to master the art of effectively documenting your machine learning code with Google, Numpy, and reStructuredText docstring styles for improved readability and maintainability.--DIVIDER--# Tutorial Overview
Welcome to our tutorial... |
EeNv3K1byb1V20OLZbBOd | ready-tensor | cc-by-sa | Ready Tensor Forecasting Benchmark |
--DIVIDER--# Ready Tensor Forecasting Benchmark
## Abstract
The purpose of this project is to provide a comprehensive and systematic evaluation of forecasting models across diverse time series datasets. This project aims to help researchers and practitioners identif... |
fUTy90FWorvg | 3rdson | none | Accelerate Your AI/ML Career with Open-Source Contributions | Whether you are a beginner eager to build your portfolio or a seasoned pro looking to collaborate on meaningful projects, open-source AI/ML offers endless opportunities to learn, grow, and make an impact.
In this article, we will introduce you to curated open-source projects; from emerging frameworks like Swarmauri to... |
HrJ0xWtLzLNt | ready-tensor | cc-by-nc | Program Guide: Agentic AI Developer Certification by Ready Tensor |
--DIVIDER--Welcome to the **Agentic AI Developer Certification Program** by Ready Tensor! This is a 12-week, hands-on learning journey where you'll design, build, and deploy intelligent, goal-driven AI systems.
This page provides all essential information about t... |
iERF3DYAwsD9 | ready-tensor | cc-by-sa | Decade of AI and ML Conferences: A Comprehensive Dataset for Advanced Research and Analysis |
--DIVIDER--# Abstract
The rapid growth of artificial intelligence (AI) and machine learning (ML) research has resulted in an overwhelming amount of academic literature, making efficient document retrieval crucial for researchers. In response to this challenge, we developed a Mini-... |
JNgtglsVpvrj | rahul.parajuli27 | none | Publish Like a Pro: Essential Steps for a Perfect Ready Tensor Publication |
.png)--DIVIDER--# Checklist for a High-Quality Ready Tensor Publication
--DIVIDER--*Publishing on Ready Tensor is an exciting opportunity to share your AI models, datasets, and research with a global com... |
kwFKTldV27nA | ready-tensor | cc-by-nc | Agentic AI Developer Certification Program: Welcome & Orientation |
--DIVIDER--# 👋 Welcome to the Program!
We’re so glad you’re here.
This is the first stop in your journey through the Agentic AI Developer Certification Program (AAIDC).
Think of this page as your orientation hub: quick intro to the people, pu... |
ljGAbBceZbpv | ready-tensor | cc-by-sa | Distance Profile for Time-Step Classification in Time Series Analysis |
--DIVIDER--TL;DR: Distance Profile is a versatile and powerful technique in time series analysis. In this work, we apply it to a task we define as Time-Step Classification, where the goal is to classify individual time steps within a time series. Our approach demo... |
LX9cbIx7mQs9 | ready-tensor | cc-by-sa | Markdown for Machine Learning Projects: A Comprehensive Guide |
--DIVIDER--# Overview
This comprehensive guide focuses on using Markdown for documentation in machine learning projects. Markdown is an invaluable tool that facilitates the creation of readable and easy-to-follow documentation. In the complex and collab... |
of4NZ9yVgKja | ready-tensor | cc-by-sa | Foundational LLMs in Timeseries Forecasting: A Benchmarking Study |
--DIVIDER--# Foundational Models in Timeseries Forecasting: A Benchmarking Study
## Abstract
This ongoing project at Ready Tensor features a comprehensive benchmarking analysis of foundational time series models, starting with the **Chronos** family from **Amazon** Science and **MOIRAI... |
pCgumBWFPD90 | ready-tensor | cc-by | PEP8 Style Guide for Data Scientists and AI/ML Engineers |
--DIVIDER--tl;dr
This tutorial will help you gain a solid understanding of the PEP8 style guide for writing clean, professional Python code.--DIVIDER--# Overview
Welcome to the tutorial on writing PEP8 compliant Python code. PEP8 is the official style guide for Python, outlining best practices ... |
qWBpwY20fqSz | ready-tensor | cc-by-sa | Licenses for ML Projects: A Primer |
--DIVIDER--TL;DR: This article explains the importance of licensing in ML projects, explores common license types, guides you in choosing the right license, and provides best practices for licensing your work. Understanding licensing is crucial for protecting your work and fostering collab... |
r95vGYcr1shK | ready-tensor | mit | Exploring Parameter-Efficient Fine-Tuning (PEFT) |
--DIVIDER--# TL;DR
In this article, we explore Parameter-Efficient Fine-Tuning (PEFT) methods, including Full Fine-Tuning, LoRA (Low-Rank Adaptation), DoRA (Weight-Decomposed Low-Rank Adaptation), and QLoRA (Quantized LoRA). By training and testing models on the SST-2 (Stanford Sentiment Treebank)... |
sBFzhbX4GpeQ | 3rdson | none | How to Build RAG Apps with Pinecone, OpenAI, Langchain and Python | 
## Pre-requisites :
> 1. Before jumping into the discussion, it’s important to have a foundational understa... |
SBgkOyUsP8qQ | ready-tensor | mit | Engage and Inspire: Best Practices for Publishing on Ready Tensor |
<p align="center">Image Credit: <a href="https://www.freepik.com/">Freepik</a></p>--DIVIDER--
# TL;DR
This guide outlines best practices for creating compelling AI and data science publications on Ready Tensor. It covers selecting appropriate pu... |
SHMk0UbaMlcq | ready-tensor | Introduction to knowledge Graphs with Neo4j | --DIVIDER--# Introduction
Behind every Google search, LinkedIn connection, or Amazon recommendation lies a powerful concept: the knowledge graph. At its core, a knowledge graph represents information as interconnected entities and relationships, mirroring how humans naturally think about and conne... | |
SQpaze1akU6g | ready-tensor | cc-by-sa | CPUs, GPUs, and TPUs: The Hardware Engines Driving AI |
--DIVIDER--
# What We Will Cover
Welcome to this article on the hardware that powers Artificial Intelligence (AI) and machine learning. As AI continues to evolve, understanding the relationship between algorithms and their associated hardware becomes crucial. This article will p... |
SW9KU4DapFrs | mo.abdelhamid | cc-by-sa | Hyper-Parameter Tuning (HPT) Using Optuna |
--DIVIDER--# Overview
Welcome to our publication on integrating hyperparameter tuning into our reusable machine learning models. In this publication, we'll leverage Optuna to optimize the performance of our existing Random Forest time step classifier model.
Here's a brief outline of what we'l... |
TLqRdPFx8Bjt | ready-tensor | cc-by-sa | A Comprehensive Comparison of AutoML Libraries for Binary Classification | 
# Introduction
In the rapidly evolving world of machine learning, AutoML libraries have become essential tools for data scientists and machine learning engineers. These libraries automate the time-consuming process of model selection, hyperparameter tuning, and feature engineering, making it... |
tum5RnE4A5W8 | ready-tensor | cc-by-sa | Balancing the Scales: A Comprehensive Study on Tackling Class Imbalance in Binary Classification |
--DIVIDER--TL;DR
This study evaluates three strategies for handling imbalanced datasets in binary classification—SMOTE, class weights, and decision threshold calibration—across 15 classifiers and 30 datasets. Results from 9,000 experi... |
v2pswk4Vf2Bq | ready-tensor | cc-by | Repeatability Is Not Reproducibility: Why AI Research Needs a Higher Bar |
--DIVIDER--# TL;DR
Many AI/ML papers claim "reproducibility" by offering a GitHub repo that regenerates their results - but that’s just repeatability, not true validation. In our AI Magazine paper, we explain why reproducibility requires indep... |
wm6Jm52Y5pBu | ready-tensor | cc-by-sa | Beyond tracemalloc: A Comprehensive Resource Tracker for Python |
--DIVIDER--# Introduction
In the world of Python programming, especially in data science and machine learning, efficient memory management is crucial. As projects grow in complexity and scale, understanding and optimizing memory usage becomes increasingly important. However, tracking memory consumpt... |
WsaE5uxLBqnH | ready-tensor | cc-by-sa | Technical Excellence in AI/ML Publications: An Evaluation Rubric by Ready Tensor |
<div align="center">
<a href="https://www.freepik.com/free-vector/data-extraction-concept-illustration_12079896.htm#fromView=search&page=1&position=3&uuid=11dae826-208d-4ed7-82ff-a57bc0a5505d&query=AI+report">Image by storyset on Freepik</a>
</div>
--DIVIDER-... |
yzN0OCQT7hUS | ready-tensor | cc-by-sa | One Model, Five Superpowers: The Versatility of Variational Auto-Encoders |
--DIVIDER--# TL;DR
Variational Auto-Encoders (VAEs) are versatile deep learning models with applications in data compression, noise reduction, synthetic data generation, anomaly detection, and missing data imputation. This publication demonstrates these capabilities using the MNIST dat... |
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