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 …
Lakera Guard vs NeMo Guardrails: Compared
Lakera Guard and NVIDIA NeMo Guardrails are the two serious AI safety frameworks that production teams reach for when “add some guardrails” becomes a shipping requirement in 2026. They solve …
LLM Regression Testing: Production Guide
Regression testing an LLM pipeline is different from regression testing a REST API or a database query. LLM outputs are non-deterministic, judgment-sensitive, and often correct in multiple distinct ways. Yet …
Giskard AI Testing Framework Tutorial
Giskard is the AI testing framework that approaches LLM quality from an adversarial-first angle. Instead of only measuring accuracy on your golden dataset, Giskard generates attack scenarios automatically to find …
DeepEval vs Promptfoo vs RAGAs: Compared
LLM evaluation moved from “nice to have” to “ship-blocker” in 2026 for any team running production AI. Three frameworks dominate the Python ecosystem for this: DeepEval, Promptfoo, and RAGAs. Each …
OpenLLMetry + OpenTelemetry for LLM Tracing
OpenTelemetry is the industry-standard observability framework for traditional backend systems. In 2026 it extends cleanly to LLM applications through two projects: OpenLLMetry (by Traceloop) and OpenInference (by Arize). Both ship …
Phoenix Arize AI Observability Tutorial
Arize Phoenix is the open-source LLM observability tool that stands out for one reason: it is built on OpenTelemetry from day one. If your organization already runs OTel for traditional …
Langfuse vs LangSmith vs Helicone: Compared
LLM observability is the production category that quietly becomes essential around the time your pipeline grows past 1,000 requests per day. You can no longer eyeball what the model is …
AI Pricing Comparison 2026: GPT-5, Claude, Gemini
LLM pricing changed dramatically between 2024 and 2026. Models that cost $30 per million tokens in 2023 are $3 in 2026; models that would have been inaccessible are now cheap …
Build a Research Assistant with Web Search Tools
A research assistant that can search the web, read pages, and synthesize an answer is one of the most useful AI applications you can build. In 2026 it is also …
Build a Code Review AI Agent with Claude
Claude Opus 5.5 and Claude Sonnet 5.5 are the strongest code models in production in 2026. Building a code review agent on top of them is one of the highest-leverage …
AI Guardrails and Safety for Production LLMs
Shipping an LLM to production without guardrails is like shipping a web form without input validation. The model will eventually generate something you did not expect: profanity to a user, …
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.