About this Course

Welcome to Introduction to Deep Learning.

In this course, we will dive deep into the mystical world of deep learning. We will sequentially examine the most popular models from both a mathematical and a programming point of view.

This course is written with a lot of attention and respect for the reader. The aim of this course is to give you solid fundamentals in deep learning. Note that what we consider fundamental deep learning might very well be advanced knowledge for some people.

You will have a broad-ranging overview of all the family of methods and approaches that you need to solve your own problems.

At the end of the course, you will find an appendix with external resources to solidify your understanding of the concepts you have learned in the course.

After completion, you will have a solid understanding of different architectures, the intuition behind them, and their mathematical formulation. That’s not all, though.You will also get your hands dirty with Python and Pytorch as you will develop and train all these models from scratch.

Who is this course for?

You may be wondering if this course is for you. If you are an experienced deep learning researcher or practitioner, you will probably not benefit much from it. However, if you fall in one of the following categories, this course will prove to be extremely beneficial for you:

• Data scientists
• Computer scientists
• Software engineers
• University students and graduates with a mathematics and engineering background
• Big data engineers
• Solution architects

Why? Let me break it down to you.

In this course, you will find a solid mix of the following:

• The intuition behind the models and their training process
• Practical examples on how to build and train them with hands-on exercises and coding environments
• The mathematical formulation and principle behind the models and algorithms

So whether you have a programming background, a mathematical background, or a bit of both, you will be able to follow along just fine. The only prerequisite is to have substantial interest in deep learning.

Prerequisites

Ok, to be honest with you, having a sizable interest in deep learning is not the only prerequisite for the course. Deep learning is an advanced and quite complex field. Thus, there are a few prerequisites:

• A basic familiarity with the Python language and specific frameworks such as Tensorflow or Pytorch is needed
• Feeling comfortable with matrix operations such as matrix multiplication
• Basic understanding of probabilities
• Basic knowledge of calculus and derivatives

Throughout this course, you will become familiar with Pytorch as well as related mathematical principles. There will be times when some concepts will seem to be a bit advanced, but don’t be scared. This is completely natural.