Deep Learning for Computer Vision
Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars.
Core to many of these applications are visual recognition tasks such as image classification and object detection. Recent developments in neural network approaches have greatly advanced the performance of these state-of-the-art visual recognition systems.
This course is a deep dive into details of neural-network based deep learning methods for computer vision. During this course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.
We will cover learning algorithms, neural network architectures, and practical engineering tricks for training and fine-tuning networks for visual recognition tasks.
Topics Include
- End-to-end models
- Image classification, localization and detection
- Implementation, training and debugging
- Learning algorithms, such as backpropagation
- Long Short Term Memory (LSTM)
- Recurrent Neural Networks (RNN)
- Supervised and unsupervised learning
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