Many deep learning courses begin with teaching very high level neural network libraries at one extreme, or deep dive into linear algebra and calculus at the other. Both extremes have pitfalls, as on one hand, you don’t have the foundations to understand what is happening, or even worse, you never get chance to build anything due to the complexity of the maths.
The Neural Nets for Newbies course aims to give you the theoretical and practical foundations to start your deep learning journey without these obstacles. We start with the basics, including why artificial neural networks have become so popular. An introduction is then provided to Python libraries Pandas and NumPy, and the Jupyter Notebook IDE, which we will use throughout the course.
We will cover basic linear algebra and calculus functions which will allow you to understand what is actually happening in an artificial neural network. Through alternating between theory and practical, we will start by creating a very basic learning model, and progress through the course to create a deep supervised learning model, which you will use to train and make predictions from data.
This course is very hands on, and requires beginner to intermediate Python programming skills. As we are introducing NumPy for mathematical functions, and Pandas for data manipulation, if you are familiar with Python data types, functions and loops, then this level of Python knowledge will suffice.
While at the end of this course, you will be able to build a basic deep artificial neural network model in Python to train and predict using your own data, the actual aim is to give you enough understanding of deep learning to allow you to start your own deep learning journey.