This section contains a curated selection of learning resources! Whether you're a complete beginner or looking to brush up on your skills, this page has everything you need. Explore a range of resources, including beginner-friendly books, comprehensive cheatsheets for quick reference, and valuable online tutorials to enhance your Pythonic journey!

This section contains a curated collection of research papers to help you navigate the exciting world of Artificial Intelligence. These papers represent the latest advancements and key concepts in various AI subfields, providing a strong foundation for your senior project. Explore these papers to identify potential research areas, discover novel applications of AI, and gain insights from established researchers. This collection will empower you to develop a well-informed and impactful project proposal that contributes to the ever-evolving field of AI.

• Google Research Datasets
• Google Dataset Search
• Hugging Face Datasets
• Kaggle Datasets
• UC Irvine
• Papers with Code Datasets
• TensorFlow Datasets
• Microsoft Azure Datasets

• Paper: Architecturing Binarized Neural Networks for Traffic Sign Recognition
• Paper: Indian traffic sign detection and recognition using deep learning
• Paper: Traffic Sign Detection and Recognition Using YOLO Object Detection Algorithm: A Systematic Review
• Paper: Optimized Detection and Classification on GTRSB: Advancing Traffic Sign Recognition with Convolutional Neural Networks
• Tutorial: Traffic Signs Recognition using CNN and Keras in Python
• Tutorial: Traffic Signs Recognition using CNN Keras
• Dataset: GTSRB (German Traffic Sign Recognition Benchmark)
• Dataset: CURE Traffic Sign Recognition
• Dataset: Traffic-Sign Detection and Classification in the Wild

• Paper: Revising deep learning methods in parking lot occupancy detection
• Paper: Image-Based Parking Space Occupancy Classification: Dataset and Baseline
• Dataset: Action-Camera Parking
• Dataset: PKLot-A robust dataset for parking lot classification
• Dataset: SPKL (Seasonal Parking Lot Dataset)

• Paper: Detection in Crowded Scenes: One Proposal, Multiple Predictions
• Paper: Pedestrian Detection: Domain Generalization, CNNs, Transformers and Beyond
• Paper: F2DNet: Fast Focal Detection Network for Pedestrian Detection
• Paper*: Pedestrian Detection: An Evaluation of the State of the Art
• Paper: Cascade R-CNN: High Quality Object Detection and Instance Segmentation
• Dataset: EuroCity Persons
• Dataset: Caltech Pedestrian Dataset
• Dataset: ETH (ETH Pedestrian)

• Tutorial: How to Estimate Speed with Computer Vision
• Tutorial: Speed Estimation using Ultralytics YOLOv8
• Tutorial: Speed detection from CCTV with OpenCV and Deep Learning
• Tutorial: Real-Time Vehicle Tracking & Analytics using Traffic Videos
• Paper: Vision-based Vehicle Speed Estimation: A Survey
• Paper: Vehicle Speed Detection System Utilizing YOLOv8: Enhancing Road Safety and Traffic Management for Metropolitan Areas

• Paper: SIMPLE ONLINE AND REALTIME TRACKING WITH A DEEP ASSOCIATION METRIC
• Paper: MOT16: A Benchmark for Multi-Object Tracking
• Tutorial: Kaggle: Human tracking and counting
• Tutorial: Tracking and counting persons
• Tutorial: YoLo V8 - People Detection and Tracking in ROI
• Dataset: PersonPath22 is multi-person tracking dataset
• Dataset: People Tracking & Object Detection dataset

• Paper: FaceNet: A Unified Embedding for Face Recognition and Clustering
• Paper: VGGFace2: A dataset for recognising faces across pose and age
• Paper: A Light CNN for Deep Face Representation with Noisy Labels
• Tutorial: Learning Face Representation from Scratch
• Dataset: CACD (Cross-Age Celebrity Dataset)
• Dataset: LFW (Labeled Faces in the Wild)

• Paper: Predicting urban growth with machine learning
• Paper: Urban Growth Forecast Using Machine Learning Algorithms and GIS-Based Novel Techniques: A Case Study Focusing on Nasiriyah City, Southern Iraq
• Datasets: Kaggle Urban Planning Data
• Tutorial: A Deep Dive into Predicting Urban Growth using ArcGIS and R
• Github: https://github.com/EL-BID/Modelo-de-prediccion-de-crecimiento-urbano-

• Paper: Continuous Sign Language Recognition with Correlation Network
• Paper: CorrNet+: Sign Language Recognition and Translation via Spatial-Temporal Correlation
• Datasets: GSL (Greek Sign Language)
• Tutorial: BBC-Oxford British Sign Language Dataset

• Paper: Recipes for building an open-domain chatbot
• Paper: CAiRE: An Empathetic Neural Chatbot
• Paper: Towards highly adaptive Edu-Chatbot
• Paper: Chatbot in Arabic language using seq to seq model
• Project: Chatbot Using OpenAI API
• Tutorial: Building a Conversational Q&A Chatbot With A Gemini Pro Free API
• Tutorial: Get started with the Gemini API
• Tutorial: Getting Started with Chat with the Gemini Pro model
• Dataset: Customer Support on Twitter
• Dataset: ChatGPT Software Testing Study