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.
By the end, you will be able to build a convolutional neural network, including recent variations such as residual networks; apply convolutional networks to visual detection and recognition tasks; and use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data.
The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.
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FOUNDATION OF CONVOLUTION NEURAL NETWORKS
0:00:00 Computer Vision
0:05:43 Edge Detection Example
0:17:14 More Edge Detection
0:25:11 Padding
0:35:01 Strided Convolutions
0:44:03 Convolutions Over Volume
0:54:48 One Layer of a Convolution Network
1:10:58 Simple Convolutional Network Example
1:19:30 Pooling Layers
1:29:55 CNN Example
1:42:32 Why Convolutions
1:52:12 Yann LeCun Interview
DEEP CONVOLUTION MODEL: CASE STUDIES
2:20:01 Why loot at case Studies
2:23:09 Classic Networks
2:41:28 ResNets
2:48:36 Why ResNets Work
2:57:48 Networks in Networks and 1X1 Convolutions
3:04:28 Inception Network Motivation
3:14:43 Inception Network
3:23:29 MobileNet
3:39:47 MobileNet Architecture
3:48:19 EfficientNet
3:51:59 Using Open-Source Implementation
3:56:56 Transfer Learning
4:05:44 Data Augmentation
4:15:15 State of Computer Vision
OBJECT DETECTION
4:27:53 Object Locallization
4:39:47 Landamark Detection
4:45:44 Object Detection
4:51:33 convolutional Implementation of Sliding Windows
5:02:41 Bounding Box Predictions
5:17:13 Intersection Over Union
5:21:32 Non-max Suppression
5:29:34 Anchor Boxes
5:39:17 YOLO Algorithm
5:46:18 Region Proposals (Optional)
5:52:45 Semantic Segmentation With U-Net
6:00:07 Transpose Convolutions
6:07:46 U-Net Architecture Intuition
6:11:08 U-Net Architecture
SPECIAL APPLICATIONS: FACE RECOGNITION & NEURAL STYLE TRANSFER
6:18:49 What is Face Recongnition
6:23:26 One shot Learning
6:28:11 Siamese Network
6:33:02 Triplet Loss
6:48:32 Face Verification and Binary Classification
6:54:38 What is Neural Style Transfer
6:56:40 What are deep ConNets Learning
7:04:38 Cost Function
7:08:37 content Cost function
7:12:15 Style Cost Function
7:25:32 ID and 3D Generalizations
⭐ Important Notes ⭐
⌨️ The creator of this course is Deeplearning.ai (Andrew Ng)
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