Об этом курсе: 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.
Для кого этот курс: - 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.
Преподаватели: Andrew Ng, Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain
Преподаватели: Head Teaching Assistant - Kian Katanforoosh, Adjunct Lecturer at Stanford University, deeplearning.ai, Ecole Centrale Paris
Преподаватели: Teaching Assistant - Younes Bensouda Mourri, Mathematical & Computational Sciences, Stanford University, deeplearning.ai
| Основные сведения | Course 4 of 5 in the Deep Learning Specialization |
| Уровень | Intermediate |
| Выполнение | 4 weeks of study, 4-5 hours/week |
| Язык | English |
| Требования к аппаратным средствам | None currently, unless you'd like to download Jupyter Notebooks locally for offline work. |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
- Видео: Computer Vision
- Видео: Edge Detection Example
- Видео: More Edge Detection
- Видео: Padding
- Видео: Strided Convolutions
- Видео: Convolutions Over Volume
- Видео: One Layer of a Convolutional Network
- Видео: Simple Convolutional Network Example
- Видео: Pooling Layers
- Видео: CNN Example
- Видео: Why Convolutions?
- Notebook: Convolutional Model: step by step
- Notebook: Convolutional Model: application
- Видео: Yann LeCun Interview
- Видео: Why look at case studies?
- Видео: Classic Networks
- Видео: ResNets
- Видео: Why ResNets Work
- Видео: Networks in Networks and 1x1 Convolutions
- Видео: Inception Network Motivation
- Видео: Inception Network
- Видео: Using Open-Source Implementation
- Видео: Transfer Learning
- Видео: Data Augmentation
- Видео: State of Computer Vision
- Notebook: Keras Tutorial - The Happy House (not graded)
- Notebook: Residual Networks
- Видео: Object Localization
- Видео: Landmark Detection
- Видео: Object Detection
- Видео: Convolutional Implementation of Sliding Windows
- Видео: Bounding Box Predictions
- Видео: Intersection Over Union
- Видео: Non-max Suppression
- Видео: Anchor Boxes
- Видео: YOLO Algorithm
- Видео: (Optional) Region Proposals
- Notebook: Car detection with YOLOv2
- Видео: What is face recognition?
- Видео: One Shot Learning
- Видео: Siamese Network
- Видео: Triplet Loss
- Видео: Face Verification and Binary Classification
- Видео: What is neural style transfer?
- Видео: What are deep ConvNets learning?
- Видео: Cost Function
- Видео: Content Cost Function
- Видео: Style Cost Function
- Видео: 1D and 3D Generalizations
- Notebook: Art generation with Neural Style Transfer
- Notebook: Face Recognition for the Happy House
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Easy and great explanations!
Interesting, challenging and promising!
great
I liked the course because is simply and straight forward.