This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images.
– Understand how to build a convolutional neural network, including recent variations such as residual networks.
– Know how to apply convolutional networks to visual detection and recognition tasks.
– Know to use neural style transfer to generate art.
– Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data.
Who is this class for: – Learners that took the first two courses of the specialization. The third course is recommended. – Anyone that already has a solid understanding of densely connected neural networks, and wants to learn convolutional neural networks or work with image data.
Course 4 of 5 in the Deep Learning Specialization.
Foundations of Convolutional Neural Networks
Learn to implement the foundational layers of CNNs (pooling, convolutions) and to stack them properly in a deep network to solve multi-class image classification problems.
Graded: The basics of ConvNets
Graded: Convolutional Model: step by step
Graded: Convolutional model: application
Deep convolutional models: case studies
Learn about the practical tricks and methods used in deep CNNs straight from the research papers.
Graded: Deep convolutional models
Graded: Residual Networks
Learn how to apply your knowledge of CNNs to one of the toughest but hottest field of computer vision: Object detection.
Graded: Detection algorithms
Graded: Car detection with YOLOv2
Special applications: Face recognition & Neural style transfer
Discover how CNNs can be applied to multiple fields, including art generation and face recognition. Implement your own algorithm to generate art and recognize faces!
Graded: Special applications: Face recognition & Neural style transfer
Graded: Art generation with Neural Style Transfer
Graded: Face Recognition for the Happy House
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