Please enable JavaScript.

Coggle requires JavaScript to display documents.

MIT BUSINESS STRATEGY and ML - Coggle Diagram

- - - - "Black box" machine learning models like deep neural networks can be opaque in how they arrive at predictions.
      - Improving the interpretability and explainability of these models is an active area of research.
      - Techniques like feature importance analysis and example-based explanations are emerging to make models more transparent.
- - - - Convolutional Neural Networks (CNNs) are a powerful deep learning architecture used for image analysis tasks like classification, object detection, and segmentation.
      - CNNs learn to recognize patterns in images during training on massive labeled datasets.
      - Transfer learning allows using pre-trained CNN models and fine-tuning them for specific image analysis tasks.
    - - Medical Image Analysis Example:
      - Machine learning on medical images helps automate diagnosis and detection of conditions like diabetic retinopathy.
      - Google scientists applied CNNs to retinal images to classify and detect signs of diabetic retinopathy.
      - Automated analysis of X-rays, MRIs, etc. can assist clinicians and improve healthcare delivery.
      - :
        
        SAMPLES IN mOBILITY
        
        Mobileye Autonomous Vehicle Example:
        
        Mobileye uses neural networks and machine learning for computer vision in self-driving cars.
        
        Their system analyzes real-world driving footage to recognize roads, vehicles, pedestrians, hazards.
        
        It combines supervised learning on labeled data with reinforcement learning in live conditions.
        
        Advanced AI vision is a key differentiator for autonomous vehicle and robotics companies like Mobileye