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# Introduction to Neural Networks

## Learn why neural networks are flexible tools for learning.

Artificial neural networks learn by detecting patterns in huge amounts of information. Much like your own brain, artificial neural nets are flexible, data-processing machines that make predictions and decisions. In fact, the best ones outperform humans at tasks like chess and cancer diagnoses.

In this course, you'll dissect the internal machinery of artificial neural nets through hands-on experimentation, not hairy mathematics. You'll develop intuition about the kinds of problems they are suited to solve, and by the end you’ll be ready to dive into the algorithms, or build one for yourself.

15

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1. 1

### Introduction

When traditional AI hit a dead end, artificial neural nets jumped in.

1. #### Neural Networks

Teaching machines to teach themselves

2. #### The Computer Vision Problem

Think image recognition is easy? Try seeing in pixels.

3. #### The Folly of Computer Programming

Why do we need neural networks? Some things just can't be programmed.

4. #### Can Computers Learn?

Do you have to be living to be learning?

2. 2

### Neurons

The power of neural networks emerges from these simple building blocks.

1. Included with

#### The Decision Box

Meet your first artificial neuron and learn how to encode simple logical operations.

2. Included with

#### Activation Arithmetic

You can count on simple artificial neurons — literally.

3. Included with

#### Decision Boundaries

Hone your intuition with this graphical model of a binary neuron.

4. Included with

#### Building an XOR Gate

Escape the limitations of single neurons by stacking them in layers.

3. 3

### Layers

Connecting neurons together in layers boosts a neural net's performance.

1. Included with

#### Hidden Layers

Got some complex data to classify? Try adding a hidden layer to your ANN.

2. Included with

#### Curve Fitting

Classifying isn't an ANN's only schtick. They are used to model lots of different data.

3. Included with