This course consists of ~20 tutorials to learn deep learning. You can think of this course as a "Free Online Nano Book". You'll learn core deep learning concepts and algorithms, including neural nets, convolutional nets, word embeddings, and even advanced concepts like generative adversarial networks. Tutorials have been written in a way to make learning productive and easy to the extent possible.

*This is an updated, more cohesive version of our earlier deep learning course.*

__Why deep learning?__: Deep Learning has caused the revival of artificial intelligence. It has become the dominant method for speech recognition (this allows us to talk to Amazon Alexa and Siri), computer vision (this allows us to search for "my pictures on the beach" on Google Photos), language translation and even game artificial intelligence (think AlphaGo and DeepMind). If you'd like to learn how these systems work and maybe make your own, deep learning is for you.

__Structure__: In the first section, you'll learn what **machine learning** is and the main concepts within machine learning. This background in machine learning is required in-order to be able to start learning about **neural networks** and **deep learning**, which is a sub-field of machine learning. In the second section, you'll learn what deep learning is, what **neural networks** look like, and how they are trained. In the following sections, we'll cover some popular neural networks architectures. This includes (a) **convolutional neural networks**, which have revolutionized computer vision, (b) **long short-term memory networks**, which have revolutionized natural language processing, and (c) **generative adversarial networks**, which are the first practical and successful algorithm for *generating* images.

__Expected time to completion__: Depends on how frequently you come back, but **non-stop reading time is about 4-5 hours**. Obviously, it is recommended that after every couple of tutorials you take time out and ask yourself what you learnt, or better yet, try to explain it to someone else.

__How to use this course__: Each section of this course depends on the previous one, apart from sections 3 and 4 (CNNs and LSTMs), which do not depend on each other and can be read in any order. Section 5 (GANs) depends on sections 1, 2 and 3, but not on section 4.

__Pre-requisites__: The course assumes some mathematical maturity, mostly basic fluency in algebra and calculus 1 (differentiation and chain rule).

*Pro-tip: If you already know machine learning, you can ***skip tutorials** by marking them completed.