Hi, my name is Gilad Gressel and I’d like to tell you about my new course: Deep Learning with Python.
Deep learning is an old technology that has recently been sweeping through the field of machine learning and artificial intelligence. Deep learning powers many of the cutting edge technologies that appear to be “magic” in the world today. Voice recognition, image detection, facial recognition, AI game playing agents (Dota, Chess, and Go), all of these are examples of deep learning in action.
Why Would You Be Interested in Deep Learning?
You might be interested in studying deep learning simply because you want to know what the “magic” is, what the hype is all about. That’s a fine reason to take this course, but I urge you to dig a bit deeper. If you have ever stared at a bunch of data and known “there is a pattern here, but I can’t find it,” then this is the course for you!
About The Course
In this course you will learn what is going on in a deep learning program, what it can do and what it’s limitations are. If you are wondering if you can apply deep learning to your field of expertise – the chances are that yes you can. If you simply watch the introductory trailer you will know what is required to make this work (hint: data).
This course is an introduction to deep learning. It assumes zero prior knowledge and has no prerequisites aside from the ability to code comfortably in any language. That said, we will be coding in Python, so you will either need to learn it (it’s easy) or already know it. I won’t assume you know much of any math, except what you would have learned in high school.
In this course you will code your own image recognition model (for handwritten digits), predict housing prices and perform sentiment analysis on the IMDB movie database dataset. During all these exercises I will be helping you understand how your decisions will affect the performance of your deep learning models. I am going to help you understand what affects what when you build your deep learning models, and we’ll do this all in practical coding sessions.