**Info:** 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.

Learn to implement the foundational layers of CNNs (pooling, convolutions) and to stack them properly in a deep network to solve multi-class image classification problems.

- Video: Computer Vision

- Video: Edge Detection Example

- Video: More Edge Detection

- Video: Padding

- Video: Strided Convolutions

- Video: Convolutions Over Volume

- Video: One Layer of a Convolutional Network

- Video: Simple Convolutional Network Example

- Video: Pooling Layers

- Video: CNN Example

- Video: Why Convolutions?

- Read: Convolutional Model: step by step

- Read: Convolutional Model: application

- Grading: The basics of ConvNets

- Grading: Convolutional Model: step by step

- Grading: Convolutional model: application

Learn about the practical tricks and methods used in deep CNNs straight from the research papers.

- Video: Why look at case studies?

- Video: Classic Networks

- Video: ResNets

- Video: Why ResNets Work

- Video: Networks in Networks and 1x1 Convolutions

- Video: Inception Network Motivation

- Video: Inception Network

- Video: Using Open-Source Implementation

- Video: Transfer Learning

- Video: Data Augmentation

- Video: State of Computer Vision

- Read: Keras Tutorial - The Happy House (not graded)

- Read: Residual Networks

- Grading: Deep convolutional models

- Grading: Residual Networks

Learn how to apply your knowledge of CNNs to one of the toughest but hottest field of computer vision: Object detection.

- Video: Object Localization

- Video: Landmark Detection

- Video: Object Detection

- Video: Convolutional Implementation of Sliding Windows

- Video: Bounding Box Predictions

- Video: Intersection Over Union

- Video: Non-max Suppression

- Video: Anchor Boxes

- Video: YOLO Algorithm

- Video: (Optional) Region Proposals

- Read: Car detection with YOLOv2

- Grading: Detection algorithms

- Grading: Car detection with YOLOv2

Discover how CNNs can be applied to multiple fields, including art generation and face recognition. Implement your own algorithm to generate art and recognize faces!

- Video: What is face recognition?

- Video: One Shot Learning

- Video: Siamese Network

- Video: Triplet Loss

- Video: Face Verification and Binary Classification

- Video: What is neural style transfer?

- Video: What are deep ConvNets learning?

- Video: Cost Function

- Video: Content Cost Function

- Video: Style Cost Function

- Video: 1D and 3D Generalizations

- Read: Art generation with Neural Style Transfer

- Read: Face Recognition for the Happy House

- Grading: Special applications: Face recognition & Neural style transfer

- Grading: Art generation with Neural Style Transfer

- Grading: Face Recognition for the Happy House