关于此课程： 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.
此课程适用人群： - 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.
教学方： Andrew Ng, Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain
教学方： Head Teaching Assistant - Kian Katanforoosh, Adjunct Lecturer at Stanford University, deeplearning.ai, Ecole Centrale Paris
教学方： Teaching Assistant - Younes Bensouda Mourri, Mathematical & Computational Sciences, Stanford University, deeplearning.ai
| 基本信息 | 课程 4（共 5 门，Deep Learning Specialization ） |
| 级别 | 中级 |
| 承诺学习时间 | 4 weeks of study, 4-5 hours/week |
| 语言 | English, 字幕： Chinese (Traditional), Korean |
| 硬件要求 | None currently, unless you'd like to download Jupyter Notebooks locally for offline work. |
| 如何通过 | 通过所有计分作业以完成课程。 |
| 用户评分 |
- 视频: Edge Detection Example
- 视频: More Edge Detection
- 视频: Padding
- 视频: Strided Convolutions
- 视频: Convolutions Over Volume
- 视频: One Layer of a Convolutional Network
- 视频: Simple Convolutional Network Example
- 视频: Pooling Layers
- 视频: CNN Example
- 视频: Why Convolutions?
- Notebook: Convolutional Model: step by step
- Notebook: Convolutional Model: application
- 视频: Yann LeCun Interview
- 视频: Classic Networks
- 视频: ResNets
- 视频: Why ResNets Work
- 视频: Networks in Networks and 1x1 Convolutions
- 视频: Inception Network Motivation
- 视频: Inception Network
- 视频: Using Open-Source Implementation
- 视频: Transfer Learning
- 视频: Data Augmentation
- 视频: State of Computer Vision
- Notebook: Keras Tutorial - The Happy House (not graded)
- Notebook: Residual Networks
- 视频: Landmark Detection
- 视频: Object Detection
- 视频: Convolutional Implementation of Sliding Windows
- 视频: Bounding Box Predictions
- 视频: Intersection Over Union
- 视频: Non-max Suppression
- 视频: Anchor Boxes
- 视频: YOLO Algorithm
- 视频: (Optional) Region Proposals
- Notebook: Car detection with YOLOv2
- 视频: One Shot Learning
- 视频: Siamese Network
- 视频: Triplet Loss
- 视频: Face Verification and Binary Classification
- 视频: What is neural style transfer?
- 视频: What are deep ConvNets learning?
- 视频: Cost Function
- 视频: Content Cost Function
- 视频: Style Cost Function
- 视频: 1D and 3D Generalizations
- Notebook: Art generation with Neural Style Transfer
- Notebook: Face Recognition for the Happy House
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Great course! Interesting Assignments!
Very interesting and well structured course. Great lectures and content.
Only critics: The actual tasks to be computed/coded in the assignments did not include the "interesting"-ML issues. A lot of linear algebra coding was asked for (i.e. compute loss-function several-times), but which did not help me to understand the underlying ML / CNN principles. A little bit more effort could be done there!
Thx for the course!
This course will be your guide into CNNs.
HP
Very attractive course. It gives me deeper insights of processing image recognition by convolution nearual networks.