CS361 (Software Engineering Program) Artificial Intelligence II - Applied Machine Learning Lecture 9 A Basic Introduction to Deep Learning & Convolutional Neural Networks [CNNs] Amr S. Ghoneim (Assistant Professor, Computer Science Dept.) Helwan University Fall 2019 Lecture is based on its counterparts in the following courses (& the following resources): o CS231n: Convolutional Neural Networks for Visual Recognition, Stanford University (California USA), Stanford School of Engineering. o CS 898: Deep Learning and Its Applications, University of Waterloo (Waterloo - Ontario, Canada), David R. Cheriton School of Computer Science o Introduction to Deep Learning, UIUC University of Illinois at Urbana - Champaign (Illinois USA), Computer Science Department. o CS231A: Computer Vision, From 3D Reconstruction to Recognition, Stanford University (California USA), Computer Science Dept. - The Computational Vision & Geometry Lab (CVGL) Today’s Key Concepts Machine Learning Deep Learning CNNs: A Bit of History First Strong Results Hierarchical Organization of early Visual Pathways ImageNet Classification with Deep CNNs AlexNet LeNet-5 Basic Concepts of CNNs Overall CNN Architecture Convolution Layer & Convolution Filters Activation Maps & Activation Functions (ReLU) ConvNet: a Sequence of Convolution Layers Spatial Dimensions Pooling Layer & Padding the Borders Fully Connected Layer (FC layer) & Flattening Machine Learning? {Artificial Intelligence} Machine Learning Map 3 Deep Learning Recap: Machine Learning Basics Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed Labeled Data Machine Learning Algorithm Training Prediction Labeled Data Learned Model Prediction Methods that can learn from and make predictions on data. Recap: Types of Learning Supervised: Learning with a labeled training set. Example: email classification with already labeled emails. U