The course is part of this learning path
Convolutional Neural Networks
In this course, discover convolutions and the convolutional neural networks involved in Data and Machine Learning. Introducing the concept of tensor, which is essential for everything that follows.
Learn to apply the right kind of data such as images. Images store their information in pixels, but you will discover that it is not the value of each pixel that matters.
- Understand how convolutional neural networks are essential to the fundamentals of Data and Machine Learning.
- It is recommended to complete the Introduction to Data and Machine Learning course before starting.
About the Author
I am a Data Science consultant and trainer. With Catalit I help companies acquire skills and knowledge in data science and harness machine learning and deep learning to reach their goals. With Data Weekends I train people in machine learning, deep learning and big data analytics. I served as lead instructor in Data Science at General Assembly and The Data Incubator and I was Chief Data Officer and co-founder at Spire, a Y-Combinator-backed startup that invented the first consumer wearable device capable of continuously tracking respiration and activity. I earned a joint PhD in biophysics at University of Padua and Université de Paris VI and graduated from Singularity University summer program of 2011.
Hello and welcome to section six on convolutional neural networks. In the last sections, we learned the basics of fully connected neural nets and we build our first models. We also dived deep into the internals of backpropagation and learned how networks are trained. Now it's time to apply neural networks to the type of data where they really provide an advantage, and we'll start with images. Images store their information in pixels, but it's not really the value of each pixel that matters. For example, if you're trying to detect an object in an image, it won't matter if the image is black and white, or if it's colored, or if the object is somewhere in different positions in the image.
All that matters is that the pixels representing the object are correlated with one another. So convolutional neural networks really exploit this nearby relationship and are able to treat images really, really well. Convolutional neural networks work well with images, but also work well with sequences, for example with sound. In this section, you will discover convolutions and then learn about convolutional neural networks. We will also introduce the concept of tensor which is really important for everything that follows. So let's get started with section six on convolutional neural networks.