4 Steps to Start Machine Learning with Computer Vision

In 2012, AlexNet took first place at the ImageNet Large Scale Visual Recognition Challenge, marking the first time a convolutional neural network had won the image classification competition. One more factor that made this achievement much more significant is that AlexNet showed twice the accuracy than the second-place participant.

In the years following, convolutional neural networks were rapidly integrated into computer vision projects to solve image classification, localization, object detection, segmentation, and other problems with state-of-art accuracy. CNNs became the most widely-used algorithm for different problems of computer vision. CNNs are applicable to everything connected with images and video streamings. Self-driving cars, security systems, anomaly detectors, medical assistants, and smart traffic regulation systems are just a few examples of where neural networks can be applied. Machine learning with computer vision is an exciting field: Computer-vision engineers are in high-demand, and top mass-media resources even predict that this field will continue to grow for at least 20 years.

This is the roadmap of where to start learning computer visualization and which topics deserve the most attention.

1. Create your own classification model with Keras