Appeared in the 1959, however, recently, Deeplearning has shown its true strength thanks to the strong development of computer hardware. nitially inspired by applications in the field of computer vision, however, it later showed its strength in many other areas.To imagine what deep learning is, consider the problem of face detection. Previously to detect the position of a face in a photo, the programmer had to define the "rough" attributes of the face like the eyes, the "black" than the middle, the nose is usually thick color than the two sides, ...These attributes are observed by the programmer from the face and want to train the computer to acquire these characteristics of the face to detect it. however, because it is a subjective opinion or these attributes do not describe the characteristics of the face, the findings are usually not high. With deeplearning the above attributes are defined based on data. Programmers just need to input data and label where the faces of the deeplearning models will learn the attributes themselves, giving more accurate results if the data is good enough.
In order to introduce and explain why deeplearning can be done so we introduce Deeplearning for Computer Vision course.Not only are there dry theories, when taking a course, students can also build their own popular deeplearning network architecture, and apply it to specific problems.
Part 1: Neural networks
Part 2: Convolutional Neural Networks
Part 3: Introduction of architectures: LeNet, VGG, ResNet, Inception
Part 4: Application