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 …
ChatGPT API Python Tutorial: Step-by-Step 2026 Beginner Guide
This tutorial walks you through using the ChatGPT API in Python from scratch. By the end you will know how to send prompts, stream responses, handle conversation history, control the …
First LangChain Project in Python: Complete Beginner Tutorial 2026
This tutorial walks you through your first LangChain project in Python. By the end you will have a working command-line assistant that uses an LLM, remembers the conversation, and shows …
Deploy an AI App to Production: Vercel vs AWS Lambda 2026 Guide
You have a working AI prototype. Now it needs to live on the internet. The two most common serverless deployment targets for AI applications in 2026 are Vercel and AWS …
OpenAI Function Calling vs Anthropic Tool Use: Complete 2026 Guide
Tool use is how LLMs stop being chat toys and start being useful parts of a real application. Both OpenAI and Anthropic ship mature tool-calling APIs in 2026, but they …
Prompt Engineering Patterns Every Developer Should Know (2026 Guide)
Prompt engineering stopped being folklore in 2026 and became a discipline with repeatable patterns. If you build with LLM APIs regularly, there are maybe fifteen patterns worth knowing, and this …
CrewAI Multi-Agent Tutorial: Build a 3-Agent Crew in Python (2026)
CrewAI is the fastest way to build multi-agent systems in Python in 2026. Instead of orchestrating LLM calls by hand, you define each agent’s role, goal, and tools, then let …
How to Build Your First AI Agent with LangGraph (2026 Tutorial)
This tutorial walks you through building your first AI agent with LangGraph. By the end, you will have a Python program that takes a user question, decides whether it needs …
LangChain Agents vs AutoGPT vs CrewAI: 2026 AI Agent Framework Comparison
AI agents moved from research toy to production feature in 2026. Three frameworks dominate the Python agent space: LangChain (via LangGraph), AutoGPT, and CrewAI. All three let you compose multiple …
Semantic Search with Embeddings: Complete 2026 Developer Guide
Semantic search is the capability behind every modern “find anything in your data” product in 2026, from Notion AI’s workspace search to GitHub Copilot’s code lookup to the internal support …
OpenAI Embeddings vs Cohere vs Voyage AI: 2026 Embedding Model Guide
Picking an embedding model is one of those decisions that feels small but compounds. A good embedding model gives your RAG system better recall, cheaper queries, and smaller indexes. A …
How to Build a RAG Application with LangChain + Pinecone (2026 Tutorial)
This is a hands-on tutorial for building a working retrieval-augmented generation application with LangChain and Pinecone. By the end, you will have a Python script that loads a folder of …
Pinecone vs Weaviate vs Qdrant: Vector Database Comparison 2026
Vector databases have crossed the chasm from research tool to production infrastructure in 2026. Pinecone, Weaviate, and Qdrant now serve billions of vectors in production for every category of AI …
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.