LangGraph vs CrewAI: Production Patterns
LangGraph and CrewAI both ship multi-agent systems to production in 2026, but at scale the differences between them matter more than the quick-start API comparisons suggest. This article covers the …
LangGraph State Machine Tutorial
LangGraph is the state-machine framework from the LangChain team for building production-grade AI agents. Where CrewAI uses roles and AutoGen uses conversations, LangGraph uses nodes, edges, and shared state. This …
CrewAI Tutorial: Build Your First Crew
CrewAI is the simplest multi-agent framework to pick up in 2026. Where AutoGen requires understanding group chats and LangGraph requires understanding state machines, CrewAI gives you three primitives (Agent, Task, …
Build AutoGen Multi-Agent System Tutorial
AutoGen is Microsoft Research’s framework for building multi-agent systems where AI agents coordinate through conversation. In 2026 it is one of the three dominant Python agent frameworks (alongside CrewAI and …
AutoGen vs CrewAI vs LangGraph: Compared
Multi-agent frameworks moved from research curiosity to production baseline in 2026. AutoGen, CrewAI, and LangGraph dominate the Python ecosystem for building systems where multiple AI agents coordinate on a task. …
AI Model Serving: SageMaker vs Vertex vs Bedrock 2026
The big three cloud providers all ship managed LLM services in 2026: AWS has SageMaker and Bedrock, Google has Vertex AI, and the pattern-match for enterprises moving to production is …
Hugging Face Inference Endpoints: Production Guide
Hugging Face Inference Endpoints turn any model on the Hub into a production API in about three clicks. Pick a model, pick a GPU, click Deploy. In exchange for giving …
Replicate vs Modal vs Baseten: LLM Hosting
Self-hosting open-source LLMs for production in 2026 has moved past the “spin up your own EC2 and fight with CUDA” phase. Three serverless GPU platforms dominate the market for turnkey …
Prompt Compression with LLMLingua Tutorial
Long prompts are expensive. A 50,000-token RAG prompt sent 100,000 times a month costs $12,500 at Claude Sonnet 5.5 input pricing. Microsoft Research shipped LLMLingua (and the sequel LongLLMLingua + …
LLM Router: Portkey vs LiteLLM vs OpenRouter
LLM routing is the production pattern where you point your app at one gateway and the gateway picks the right model for each request. The three tools dominating this space …
Semantic Caching: Cut LLM Costs 50-80%
Semantic caching cuts LLM API costs by 40 to 80 percent on workloads where users ask similar questions in different wording. Instead of caching exact strings (which almost never match …
PII Redaction in LLM Pipelines with Presidio
Users paste things they should not. Credit card numbers in support messages. Social security numbers in form-filling requests. API keys in debugging screenshots. If your LLM pipeline sends these raw …
Prompt Injection Detection Tools Compared
Prompt injection is the single most-discussed LLM security vulnerability of the past three years, and in 2026 it is still an open research problem. Attackers find novel injection patterns faster …
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.