This 24-part course consists of tutorials on deep learning concepts and neural networks, as well as quizzes and hands-on projects to practice implementing the algorithms and applying them to problems. You can think of this course as a "Free Online Nano Book".

**Why Deep Learning?: **Deep Learning has caused the revival of artificial intelligence. It has become the dominant method for speech recognition (allowing us to talk to the Google Assistant), computer vision (allowing 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.

The **primary objectives** of this course are as follows:

- Understand what
**machine learning**is, and learn the**gradient descent**algorithm. - Understand what
**deep learning**is, and how deep learning differs from and relates to machine learning. - Understand what
**neural networks**are and how they are trained using**back-propagation**. (and train your own neural network). - Understand the concept of
**computational graphs**, a core idea (often overlooked in DL courses) foundational to understanding and implementing all sorts of complex neural network architectures. - Understand how
**Convolutional Nets**work and how to solve computer vision tasks like**image classification**. - Understand how
**Recurrent Neural Nets**work and how to**generate text**and perform language translation. - Bonus: Understand
**Generative Adversarial Networks**, and how they can be used for**generating images**!

**Prerequisites**: Python, Linear Algebra, Statistics and NumPy and Calculus 1 (differentiation and chain rule).

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