Module 6: Neural Network Fundamentals
- How artificial neurons work
- Activation functions explained
- Backpropagation step-by-step Understanding gradient flow
Module 7: Optimizing Neural Networks
- Popular optimizers: Adam, RMSProp, Adagrad
- Regularization tactics (Dropout & early stopping)
- Tuning hyperparameters effectively
Module 8: Computer Vision Essentials
- Intro to computer vision concepts
- Reading & processing images
- Image transformations, color spaces, resizing & enhancement
- Feature extraction basics
Module 9: CNNs & Advanced Vision Models
- Convolutions, pooling, stride & padding
- Designing CNN architectures
- Training & evaluating CNN-based models
- Working with pre-trained networks like VGG, ResNet & EfficientNet
- Object detection using YOLO & RCNN series
- Image segmentation techniques (Mask-RCNN, U-Net)
