Python Tutorial: Introducing convolutional neural networks

Want to learn more? Take the full course at https://learn.datacamp.com/courses/image-processing-with-keras-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hello! My name is Ariel Rokem and I am a data scientist. In this course, you will learn about convolutional neural networks, or CNNs. Imagine that you work for a company that is building a self-driving car. One of the challenges you face is that your car needs to quickly be able to determine what is happening around it. For example, your self-driving car should be able to tell whether the sign at an intersection is a stop sign, , or a yield sign. CNNs are powerful algorithms for processing images. In fact, these algorithms are currently the best algorithms we have for automated processing of images, and they are used by many different companies to do things like identifying the objects in an image. After completing this course, you will be able to build an algorithm that processes images of different objects and can distinguish between them. We will use Keras, which is a Python-based library that implements the building blocks you need to build your own CNNs. I assume that you have taken DataCamp's Deep Learning course which introduces Keras. Because CNNs are a type of machine learning algorithm, I also assume that you have a working knowledge of basic principles of machine learning, such as overfitting, and model evaluation through cross-validation. Images contain data. Using Matplotlib, you can import an image into memory from a file and then display it using a plotting command, as shown here., but the computer doesn't see the image. All it sees is an array of numbers. Color images are stored in 3-dimensional arrays. The first two dimensions correspond to the height and width of the image (the number of pixels). The last dimension corresponds to the red, green and blue colors present in each pixel. To examine the red, green and blu