Neural Networks that ResNets

Convolutional Neural Networks
About this course: 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. You will: – 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. This is the fourth course of the Deep Learning Specialization.

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.

Deep convolutional models: case studies

Learn about the practical tricks and methods used in deep CNNs straight from the research papers.
Learning Objectives
• Understand multiple foundational papers of convolutional neural networks
• Analyze the dimensionality reduction of a volume in a very deep network
• Understand and Implement a Residual network
• Build a deep neural network using Keras
• Implement a skip-connection in your network
• Clone a repository from github and use transfer learning

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6 Antworten auf „Neural Networks that ResNets“

  1. That was a very useful video..many thanks to you.. I just have a small doubt, if we are skipping connections, why do we have to make these connections when we create the neural network, we could simply not add the layers we intend to skip.. i am sorry if my doubt is very primitive, i am just a beginner..will be very kind of you, if you could clarify my doubt.. Thanks!

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