Convolutional Neural Networks Lecture
Nowadays, Artificial Intelligence drives scientific and economic growth worldwide. This is largely due to advances in Machine Learning (ML), notably in Deep Neural Networks (DNNs), which are essentially massive ‘learning by experience/examples’ systems. Their applications span and revolutionize almost every human activity:
-Autonomous Systems (cars, drones, vessels),
-Media Content and Art Creation (including fake data creation/detection), Social Media Analytics,
-Medical Imaging and Diagnosis,
-Financial Engineering (forecasting and analytics), Big Data Analytics,
-Broadcasting, Internet and Communications,
-Robotics/Control
-Intelligent Human-Machine Interaction, Anthropocentric (human-centered)Computing,
-Smart Cities/Buildings and Assisted living.
-Scientific Modeling and Analytics.
Several DNN advances and challenges hit the news almost every day, arising discussions on AI ethics, privacy protection and its societal impact.
Convolutional Neural Networks form the backbone of current AI revolution and are used in a multitude of classification and regression problems. This lecture overviews the transition from multilayer perceptrons to deep architectures. The following topics are resented in detail: Tensors and mathematical formulations. Convolutional layers. Fully connected layers. Pooling. Neural Image Features and their relation to human vision are discussed. Various types of convolutions are presented: Atrous (Dilated) Convolution, 1×1 convolution, separable convolutions. Training convolutional NNs is detailed, including. Initialization, Data augmentation, Batch Normalization, Dropout, Regularization. Various CNN architectures are presented: Siamese Networks, FRACTALNET, DenseNet, Inception, ResNets, Squeeze and Excitation, Network-In-Network, AlexNet / ZFNet, Deployment on embedded systems. Lightweight deep learning.