课程四(Convolutional Neural Networks)，第三周（Object detection） —— 0.Learning Goals

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课程四(Convolutional Neural Networks)，第一周（Foundations of Convolutional Neural Networks） —— 3.Programming assignments：Convolutional Model: application

Convolutional Neural Networks: Application Welcome to Course 4's second assignment! In this notebook, you will: Implement helper functions that you will use when implementing a TensorFlow model Implement a fully functioning ConvNet using TensorFlow (…

课程四(Convolutional Neural Networks)，第一周（Foundations of Convolutional Neural Networks） —— 2.Programming assignments：Convolutional Model: step by step

Convolutional Neural Networks: Step by Step Welcome to Course 4's first assignment! In this assignment, you will implement convolutional (CONV) and pooling (POOL) layers in numpy, including both forward propagation and (optionally) backward propagati…

课程四(Convolutional Neural Networks)，第二周（Deep convolutional models: case studies） —— 0.Learning Goals

Learning Goals 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 Implem…

课程四(Convolutional Neural Networks)，第二周（Deep convolutional models: case studies） —— 2.Programming assignments : Keras Tutorial - The Happy House (not graded)

Keras tutorial - the Happy House Welcome to the first assignment of week 2. In this assignment, you will: Learn to use Keras, a high-level neural networks API (programming framework), written in Python and capable of running on top of several lower-l…

课程四(Convolutional Neural Networks)，第二周（Deep convolutional models: case studies） ——3.Programming assignments : Residual Networks

Residual Networks Welcome to the second assignment of this week! You will learn how to build very deep convolutional networks, using Residual Networks (ResNets). In theory, very deep networks can represent very complex functions; but in practice, the…

课程四(Convolutional Neural Networks)，第一周（Foundations of Convolutional Neural Networks） —— 0.Learning Goals

Learning Goals Understand the convolution operation Understand the pooling operation Remember the vocabulary used in convolutional neural network (padding, stride, filter, ...) Build a convolutional neural network for image multi-class classification…

课程四(Convolutional Neural Networks)，第二周（Deep convolutional models: case studies） —— 1.Practice questions

[解释] 应该是same padding 而不是 valid padding . [解释] 卷积操作用的应该是adding additional layers to the network ,而是应该添加跳跃连接(Skip connection). [解释] 这一题感觉四个选项都是对的,但是提交答案的时候,显示答案有错误.欢迎留言讨论. ---------------------------------------------------------- 参考链接: 1.https://www.c…

课程四(Convolutional Neural Networks)，第一周（Foundations of Convolutional Neural Networks） —— 1.Practice questions：The basics of ConvNets

[解释] 100*(300*300*3)+ 100=27000100 [解释] (5*5*3+1)*100=7600 [中文翻译] 您有一个输入是 63x63x16, 并将他与32个滤波器卷积, 每个滤波器的维度为 7x7x16, 使用的步幅为 2, 没有填充.输出是多少? [解释] nH=nW=(63+2*0-7)/2 +1=29 output=29*29*32 [解释] (63+2p-7)/1 + 1=63 ---------> p=3 [解释] max pooling 操作,paddi…

课程四(Convolutional Neural Networks)，第四周（Special applications: Face recognition & Neural style transfer） —— 2.Programming assignments：Art generation with Neural Style Transfer

Deep Learning & Art: Neural Style Transfer Welcome to the second assignment of this week. In this assignment, you will learn about Neural Style Transfer. This algorithm was created by Gatys et al. (2015) (https://arxiv.org/abs/1508.06576). In this as…

课程四(Convolutional Neural Networks)，第三周（Object detection） —— 2.Programming assignments：Car detection with YOLOv2

Autonomous driving - Car detection Welcome to your week 3 programming assignment. You will learn about object detection using the very powerful YOLO model. Many of the ideas in this notebook are described in the two YOLO papers: Redmon et al., 2016 (…

【课程四(Convolutional Neural Networks)，第三 周（Object detection） —— 0.Learning Goals】的更多相关文章

【课程四(Convolutional Neural Networks)，第三周（Object detection） —— 0.Learning Goals】的更多相关文章