In the fourth course of the Deep Learning Specialization, you will understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more.
Ce cours fait partie de la Spécialisation Deep Learning
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À propos de ce cours
Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures
A basic grasp of linear algebra & ML
Compétences que vous acquerrez
- Deep Learning
- Facial Recognition System
- Convolutional Neural Network
- Tensorflow
- Object Detection and Segmentation
Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures
A basic grasp of linear algebra & ML
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Programme de cours : ce que vous apprendrez dans ce cours
Foundations of Convolutional Neural Networks
Deep Convolutional Models: Case Studies
Object Detection
Special Applications: Face recognition & Neural Style Transfer
Avis
- 5 stars87,74 %
- 4 stars10,36 %
- 3 stars1,43 %
- 2 stars0,28 %
- 1 star0,17 %
Meilleurs avis pour CONVOLUTIONAL NEURAL NETWORKS
A big thank you to Professor Andrew and his team for structuring this course and introducing the world of ConvNets to me. I found the video lectures easy to understand and the exercises intriguing.
Wonderful course. Covers a wide array of immediately appealing subjects: from object detection to face recognition to neural style transfer, intuitively motivate relevant models like YOLO and ResNet.
Too much hand-holding during assignments, although still very good directions. Obviously the issue with the final programming assignment needs to be addressed. Fantastic lecture material, as always.
Excellent, solid insights into working of models as well as providing references to the original work. THe assignments give practical examples of models one might want to implement for their own use.
À propos du Spécialisation Deep Learning

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