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Deep Learning for Computer Vision

Deep Learning for Computer Vision

advanced5 levels18 stages54 lessons

This is the path structure only. Once you sign up, MICHi builds your own version, measures real mastery through review gateways and spaced repetition, and ends with a verifiable certificate.

Level 1 — Foundations of Image Processing and Neural Networks

  1. 1.Image Data Representation and Manipulation3 lessons
  2. 2.Introduction to Artificial Neural Networks3 lessons
  3. 3.Project: Image Classifier with a Simple MLP3 lessons

Level 2 — Convolutional Neural Networks (CNNs) Fundamentals

  1. 1.Convolutional Layers and Feature Extraction3 lessons
  2. 2.Pooling Layers and Network Architectures3 lessons
  3. 3.Training & Evaluating CNNs for Image Classification3 lessons
  4. 4.Project: Classifying Real-World Images with CNN3 lessons

Level 3 — Advanced CNN Architectures and Transfer Learning

  1. 1.Deep CNN Architectures: VGG, ResNet, Inception3 lessons
  2. 2.Transfer Learning and Fine-tuning CNNs3 lessons
  3. 3.Object Localization and Detection Basics3 lessons
  4. 4.Project: Fine-tuned Image Classifier on a New Domain3 lessons

Level 4 — Object Detection, Segmentation, and Generation

  1. 1.Single-Shot Detectors (SSD, YOLO) Architectures3 lessons
  2. 2.Instance Segmentation with Mask R-CNN3 lessons
  3. 3.Introduction to Generative Adversarial Networks (GANs)3 lessons
  4. 4.Project: Real-time Object Detection on Video Stream3 lessons

Level 5 — Advanced Topics and Deployment

  1. 1.Semantic Segmentation Architectures (FCNs, U-Net)3 lessons
  2. 2.Deep Learning Model Optimization and Deployment3 lessons
  3. 3.Project: End-to-End Deep Learning CV Application3 lessons

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