**Info:** If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago.

In this course, you will learn the foundations of deep learning. When you finish this class, you will:

- Understand the major technology trends driving Deep Learning

- Be able to build, train and apply fully connected deep neural networks

- Know how to implement efficient (vectorized) neural networks

- Understand the key parameters in a neural network's architecture

This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. So after completing it, you will be able to apply deep learning to a your own applications. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions.

This is the first course of the Deep Learning Specialization.

Be able to explain the major trends driving the rise of deep learning, and understand where and how it is applied today.

- Video: Welcome

- Video: What is a neural network?

- Video: Supervised Learning with Neural Networks

- Video: Why is Deep Learning taking off?

- Video: About this Course

- Read: Frequently Asked Questions

- Video: Course Resources

- Read: How to use Discussion Forums

- Video: Geoffrey Hinton interview

Learn to set up a machine learning problem with a neural network mindset. Learn to use vectorization to speed up your models.

- Video: Binary Classification

- Video: Logistic Regression

- Video: Logistic Regression Cost Function

- Video: Gradient Descent

- Video: Derivatives

- Video: More Derivative Examples

- Video: Computation graph

- Video: Derivatives with a Computation Graph

- Video: Logistic Regression Gradient Descent

- Video: Gradient Descent on m Examples

- Video: Vectorization

- Video: More Vectorization Examples

- Video: Vectorizing Logistic Regression

- Video: Vectorizing Logistic Regression's Gradient Output

- Video: Broadcasting in Python

- Video: A note on python/numpy vectors

- Video: Quick tour of Jupyter/iPython Notebooks

- Video: Explanation of logistic regression cost function (optional)

- Read: Deep Learning Honor Code

- Read: Programming Assignment FAQ

- Bloco de Notas: Python Basics with numpy (optional)

- Programming: Python Basics with numpy (optional)

- Notepad: Logistic Regression with a Neural Network mindset

- Video: Pieter Abbeel interview

Learn to build a neural network with one hidden layer, using forward propagation and backpropagation.

- Video: Neural Networks Overview

- Video: Neural Network Representation

- Video: Computing a Neural Network's Output

- Video: Vectorizing across multiple examples

- Video: Explanation for Vectorized Implementation

- Video: Activation functions

- Video: Why do you need non-linear activation functions?

- Video: Derivatives of activation functions

- Video: Gradient descent for Neural Networks

- Video: Backpropagation intuition (optional)

- Video: Random Initialization

- Notepad: Planar data classification with a hidden layer

- Video: Ian Goodfellow interview

Understand the key computations underlying deep learning, use them to build and train deep neural networks, and apply it to computer vision.

- Video: Deep L-layer neural network

- Video: Forward Propagation in a Deep Network

- Video: Getting your matrix dimensions right

- Video: Why deep representations?

- Video: Building blocks of deep neural networks

- Video: Forward and Backward Propagation

- Video: Parameters vs Hyperparameters

- Video: What does this have to do with the brain?

- Notepad: Building your Deep Neural Network: Step by Step

- Notepad: Deep Neural Network - Application