Hybrid Search for RAG: BM25 + Vector Combined
Hybrid Search for RAG: BM25 + Vector Combined (2026 Guide) The short answer Hybrid search combines BM25 keyword retrieval with dense vector retrieval and merges the results with Reciprocal Rank …
LlamaIndex Tutorial 2026: Build Production RAG
LlamaIndex Tutorial 2026: Build a Production RAG System in 20 Minutes The short answer LlamaIndex abstracts the RAG stack into reusable components: data loaders, QueryEngine, routers, chat engines, and a …
Pinecone vs Weaviate vs Chroma vs Milvus 2026
Pinecone vs Weaviate vs Chroma vs Milvus 2026: Vector DB Comparison The short answer Four vector databases dominate production RAG in 2026. Chroma wins for local prototypes and small projects …
Embedding Models Compared 2026: OpenAI vs Cohere vs Voyage vs BGE
Embedding Models Compared 2026: OpenAI vs Cohere vs Voyage vs BGE The short answer Four embedding model families serve production RAG in 2026: OpenAI text-embedding-3 (default general-purpose), Cohere embed-v3 (strong …
Document Chunking Strategies for RAG: Fixed vs Semantic vs Agentic
Document Chunking Strategies for RAG: Fixed vs Semantic vs Agentic (2026) The short answer Three chunking families power modern RAG: fixed-size (predictable, cheap, works for prose), semantic (topic-coherent, better retrieval …
RAG Pipeline Tutorial 2026: Build Your First Retrieval-Augmented System
RAG Pipeline Tutorial 2026: Build Your First Retrieval-Augmented System The short answer A RAG pipeline retrieves relevant chunks from your own documents and pastes them into the prompt so a …
SWE-bench Benchmark Interpretation Guide
SWE-bench is the dominant coding-agent benchmark in 2026. Every major model release (Claude Opus 5.5, GPT-5, Gemini 2.5 Pro) announces a SWE-bench Verified score, and the top of the leaderboard …
Agent Handoff Patterns for Production AI
Agent handoffs sound simple: agent A finishes, agent B takes over. In practice, handoffs are where multi-agent systems break: lost context, duplicated work, infinite ping-pong, state desync. This article covers …
Build a Coding Agent with Claude Code SDK
Claude Code SDK is Anthropic’s officially supported interface for building coding agents on top of Claude. Released in 2026 alongside the Claude Code CLI, it packages the file-system access, shell …
Supervisor Agent Architecture Patterns
The supervisor pattern is the production workhorse for multi-agent systems that need variable routing with central accountability. One supervisor agent sits above a pool of worker agents, receives all incoming …
OpenAI Swarm vs CrewAI vs AutoGen
OpenAI’s Swarm framework (shipped experimental in late 2024, matured through 2026) added a fourth serious option to the multi-agent Python ecosystem. Where CrewAI uses roles, AutoGen uses conversations, and LangGraph …
LangChain Agents vs LangGraph: Compared
LangChain shipped two generations of agent primitives: the original AgentExecutor (now called “LangChain Agents”) and the state-machine framework LangGraph. In 2026, LangChain itself officially recommends LangGraph for all new agent …
AI Agent Planner-Executor Architecture
The planner-executor architecture is one of the most-cited patterns in AI agent design, and in 2026 it is the dominant pattern for complex multi-step tasks across production systems. Separating planning …
Frequently Asked Questions
Are these deep learning projects free for capstone and thesis use?
Yes. All deep learning projects on this hub are free to download, modify, and submit. No attribution required for academic use. Most are MIT-licensed or include source-code packs with sample datasets and pretrained model weights.
What deep learning frameworks do I need installed?
Most projects use OpenCV (cv2) for video capture and image preprocessing, plus one of: TensorFlow / Keras (Caffe model loading via cv2.dnn, custom CNN training), PyTorch (research-style models, YOLO v5+, transformers), or MediaPipe (Google's optimized face/hand/pose detectors). Install with pip install opencv-python tensorflow keras torch torchvision mediapipe numpy. Python 3.10, 3.11, or 3.12 recommended (avoid 3.13 until all wheels catch up).
Do I need a GPU to run these deep learning projects?
For inference (running a pretrained model on your webcam): no, CPU runs at 15-30 FPS for most computer-vision tasks. For training a custom model on your own dataset: GPU strongly recommended (CPU works but is slow). Free GPU options: Google Colab Free (12-hour sessions, sufficient for most BSIT capstones), Kaggle Notebooks Free (30-hour weekly quota), Paperspace Free tier. No need to buy a $1000+ GPU just for a capstone defense.
Deep learning vs classical machine learning, which should I pick for my capstone?
Pick deep learning when your inputs are unstructured (images, audio, video, text) and you have 10,000+ training samples. Pick classical ML (random forest, SVM, logistic regression) for tabular data, small datasets (under 1,000 rows), or when you need explainable predictions for the panel. Many capstones combine both: deep learning for feature extraction (face embedding via FaceNet) plus classical ML on top (SVM classifier for identity matching).
Why is my OpenCV deep learning model running at 2 FPS?
Three usual causes: (1) Resolution too high, resize frames to 640x480 or 320x240 before inference. (2) Wrong cv2.dnn backend, set net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV) and net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU). (3) Heavy model on weak hardware, swap YOLO v5 for MobileNet-SSD or use Haar cascades for simple face/eye detection. Also close other applications and disable laptop battery-save throttling.
Can I extend a single OpenCV demo into a full BSIT capstone?
Yes, and you should. A standalone webcam demo (face detection alone) is too narrow for capstone scope. Wrap it in a real system: face recognition becomes Real-Time Attendance System with PHP/MySQL dashboard, object detection becomes Smart CCTV Alert System with email notifications, drowsiness detection becomes Driver Monitoring System for fleet vehicles. Add user accounts, database logging, simple admin UI, and write Chapters 1-5 manuscript to satisfy panel requirements.
How often is this deep learning projects list updated?
New deep learning projects are added periodically as we receive student requests and new models become OpenCV-compatible. Last refreshed June 2026 with 19 vision-focused projects covering face recognition, object detection, traffic-sign classification, OCR, and more.