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, …
Structured Outputs: JSON Mode vs Function Calling
Getting an LLM to return structured data your code can parse is one of the most common production needs. In 2026 there are three distinct mechanisms for this: JSON mode, …
Prompt Caching Deep Dive: OpenAI, Anthropic, Google
Prompt caching cuts inference cost by 50% to 90% on workloads with repeated prefixes. In 2026 it is a baseline production feature across OpenAI, Anthropic, and Google. If your app …
OpenAI Batch API vs Real-time: Cut Costs 50%
If your application calls OpenAI for background processing (classification, embedding generation, data extraction from documents), you are probably paying 2x more than you need to. The OpenAI Batch API cuts …
Agent Memory Patterns: Short, Semantic, Episodic
An AI agent without memory is a chatbot that forgets you between turns. An agent with memory can carry context across conversations, learn from past interactions, and build user-specific behavior. …
RAG Evaluation with RAGAs: Catch Regressions Early
RAG applications fail quietly. The system returns an answer; it looks reasonable; nobody notices it is wrong until a user complains or revenue drops. In 2026, the teams that ship …
LangSmith Deep Dive: LLM Observability Done Right
LangSmith is the observability platform most teams reach for when their LangChain or LangGraph application leaves prototype. It traces every LLM call, tool invocation, and chain step, lets you evaluate …
Self-Host LLMs on GPU: RunPod vs Lambda Labs
You have an open-source LLM you want to serve, but your laptop GPU is not enough and you do not want to buy a $10,000 workstation. The practical 2026 solution …
vLLM vs TGI vs llama.cpp: Serve LLMs in Production
Running an LLM locally on your laptop is one problem. Serving an LLM to real users at production load is a different one, and the right tool depends on your …
Mistral vs Llama 3 vs Qwen 3: 2026 Open-Source LLM Comparison
Three open-source model families dominate the serious self-hosted LLM space in 2026: Meta’s Llama 3, Mistral’s family (Mistral, Mixtral, Codestral), and Alibaba’s Qwen 3. All three ship permissive licenses, strong …
How to Run Local LLMs with Ollama (2026 First-Setup Tutorial)
Running an LLM locally used to mean wrangling CUDA, downloading 50GB of weights, and praying your Python environment did not break overnight. In 2026 Ollama has made this a two-command …
How to Fine-Tune OpenAI Models in 2026 (Complete Developer Guide)
Fine-tuning OpenAI models used to be exotic. In 2026 it has become a standard part of the production AI stack when prompt engineering runs out of runway. This guide walks …
Build Your First AI Chatbot with the Anthropic Claude SDK (2026)
This tutorial walks you through building your first AI chatbot with the Anthropic Claude SDK. By the end you will have a command-line chatbot that uses Claude Sonnet 5.5, remembers …
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.