L1 Apprentice

Level 1 - Apprentice: Hands-on Computer Vision with Deep Learning 🔥

Take your skills to the next level with deep learning-powered projects focusing on classification, object detection, and more.

1. MNIST Handwritten Digit Recognition

   • Description: Train a neural network to classify handwritten digits from the famous MNIST dataset.
   • Key Concepts: Neural networks, image classification, MNIST dataset.
   • Notebook: Open in Colab

2. CIFAR-10 Image Classification

   • Description: Implement a CNN to classify images into different categories, such as airplanes, cars, and animals, using the CIFAR-10 dataset.
   • Key Concepts: CNN, image classification, CIFAR-10 dataset.
   • Notebook: Open in Colab

3. Object Detection with YOLOv5

   • Description: Apply YOLOv5, a real-time object detection algorithm, to detect objects in images and videos.
   • Key Concepts: Object detection, YOLOv5, bounding boxes.
   • Notebook: Open in Colab

4. Semantic Segmentation with DeepLabv3+

   • Description: Use DeepLabv3+ to segment images into different semantic regions, identifying specific objects or areas.
   • Key Concepts: Semantic segmentation, DeepLabv3+, pixel-wise classification.
   • Notebook: Open in Colab

5. Facial Recognition with OpenFace

   • Description: Explore facial recognition technology using OpenFace, a library for facial identification and verification.
   • Key Concepts: Facial recognition, face embeddings, OpenFace.
   • Notebook: Open in Colab

6. Object Tracking

   • Description: Follow the movement of objects across video frames using object tracking algorithms.
   • Key Concepts: Object tracking, video processing, bounding boxes.
   • Notebook: Open in Colab

7. Human Pose Estimation

   • Description: Estimate human poses (joint positions) from images or videos using deep learning models.
   • Key Concepts: Human pose estimation, keypoints, OpenCV.
   • Notebook: Open in Colab