Taking the Coursera Deep Learning Specialization, **Neural Networks and Deep Learning** course. Will post condensed notes every week as part of the review process. All material originates from the free Coursera course, taught by Andrew Ng. See deeplearning.ai for more details.

# Table of Contents

# Introduction to Deep Learning

There are five courses in the Coursera Deep Learning Specialization. (This is course 1.)

- Neural Networks and Deep Learning
- Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
- Structuring your Machine Learning Project
- Convolutional Neural Networks
- Natural Language Processing: Building Sequence Models.

## What is a Neural Network

A neural network is a learning algorithm comprised of many stacked neurons. THey are chained together to create representations of functions based on input data.

## Supervised Learning with Neural Networks

Supervised learning is when you are given specific inputs and outputs. For instance, in the housing pricing example you can have `x: number of bedrooms, size of house, ..., house features`

map to `y: price of house`

. These types of algorithms can calculate a given `y`

with input `x`

. Typically the machine is trained on data that has both `x`

and `y`

values.

## Why is Deep Learning Taking Off?

Primarily 3 reasons:

- Massive increase in collected data
- Increase in computational power
- Algorithm modifications (usually increases computation speed)

## About this Course

This course is broken out into four weeks.

- Week 1: Introduction
- Week 2: Basics of Neural Network Programming
- Week 3: One Hidden Layer Neural Networks
- Week 4: Deep Neural Networks

## Optional: Heroes of Deep Learning (Geoffrey Hinton)

Interview between Andrew Ng and Geoffrey Hinton.

Move on to Week 2.