Data Science: Deep Learning and Neural Networks in Python

Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work. In this course, you will learn the foundations of these groundbreaking applications. This course will get you started in building your FIRST artificial neural network using deep learning techniques.

Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE. We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called "backpropagation" using first principles.

I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features. Next, we implement a neural network using Google's new TensorFlow library. You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested in machine learning and data science in general.

We go beyond basic models like logistic regression and linear regression and I show you something that automatically learns features. This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited.