Top 100 Coding Interview Questions 2026 (FAANG-Tested)
These are the 100 coding interview questions that FAANG and top-tier tech companies actually ask in 2026, grouped by topic with LeetCode links. Cover all 100 and you can walk …
LeetCode vs HackerRank vs Codewars 2026 (Which One)
Every BSIT student preparing for coding interviews eventually asks: should I grind LeetCode, HackerRank, or Codewars? All three offer thousands of practice problems. But they target different audiences, have different …
Graph Algorithms Complete 2026 Guide (BFS, DFS, Dijkstra)
Graphs are the model behind almost every real-world network: social networks, dependency graphs, road maps, package installers. Master the 6 core graph algorithms and you can solve LeetCode Hard problems, …
Dynamic Programming Beginners Guide 2026 (Real Examples)
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Binary Tree Complete Tutorial with Python 2026
Binary trees are the data structure that unlocks about 40 percent of LeetCode Medium problems. Master traversal patterns and BFS/DFS becomes automatic. This 2026 tutorial walks you through building a …
HashMap Deep Dive 2026 (Python + Java + JavaScript)
HashMap is the data structure that turns O(n) into O(1). Master it and half of interview problems become one-liners. This 2026 deep dive covers how hash tables actually work under …
Sorting Algorithms Cheat Sheet 2026 (Every Comparison)
Sorting is one of the most-asked algorithm topics in coding interviews and the topic every developer uses daily (whether they realize it or not). This 2026 cheat sheet compares the …
Recursion Explained with Real Examples 2026
Recursion is a technique where a function calls itself to solve a smaller version of the same problem. It feels magical the first time it clicks and impossible before that. …
DSA Roadmap for BSIT Students 2026 (Complete Path)
Data Structures and Algorithms is the single most-asked topic in developer job interviews and the topic BSIT students find hardest to self-study. I have watched dozens of interns join PIES …
Big-O Notation Complete Guide for Beginners 2026
Big-O notation is the language developers use to describe how fast (or slow) an algorithm runs as its input grows. If you have ever wondered why one solution feels instant …
I Built a Free UML Diagram Generator for Your Capstone (2026)
It is 11 in the evening, your defense is in two days, and Chapter 3 still has no diagrams in it. You open a drawing tool, you drag one box, …
Kubernetes on DigitalOcean vs Linode vs Vultr 2026
AWS EKS, Google GKE, and Azure AKS run most enterprise Kubernetes. For solo developers, small teams, and side projects, the “big three” cloud pricing hurts. The alternative in 2026 is …
Docker Compose Multi-Container Setup 2026 (Web+DB+Cache)
Real applications need more than one container. A typical web stack has a web app, a database, a cache, and sometimes a background worker + a reverse proxy. Running each …
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.