This course introduces convolutional neural networks for visual recognition. It covers the fundamentals of deep learning and neural networks such as image classification, loss functions, optimization and training. Lectures on advanced topics such as CNN architectures, Recurrent Neural Networks, detection and segmentation, generative models, Deep Reinforcement Learning and efficient methods and hardware for deep learning are included. Additionally, the course covers adversarial examples and adversarial training for security and robustness in Deep Learning systems.
Overview
Syllabus
Lecture 1 | Introduction to Convolutional Neural Networks for Visual Recognition.
Lecture 2 | Image Classification.
Lecture 3 | Loss Functions and Optimization.
Lecture 4 | Introduction to Neural Networks.
Lecture 5 | Convolutional Neural Networks.
Lecture 6 | Training Neural Networks I.
Lecture 7 | Training Neural Networks II.
Lecture 8 | Deep Learning Software.
Lecture 9 | CNN Architectures.
Lecture 10 | Recurrent Neural Networks.
Lecture 11 | Detection and Segmentation.
Lecture 12 | Visualizing and Understanding.
Lecture 13 | Generative Models.
Lecture 14 | Deep Reinforcement Learning.
Lecture 15 | Efficient Methods and Hardware for Deep Learning.
Lecture 16 | Adversarial Examples and Adversarial Training.
Taught by
Anders Feder
