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# Introduction to Neural Networks

## Learn why neural networks are such 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

195+
1. 1

### Introduction

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

1. #### Can Computers Learn?

Do you have to be living to be learning?

2. #### The Computer Vision Problem

What's so hard about artificial intelligence? Try seeing in pixels.

3. #### The Folly of Computer Programming

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

4. #### Neural Networks

Teaching machines to teach themselves.

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

#### Binary Neurons

Take a look inside the building blocks of neural networks

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. Dangerous curves ahead...

3. Included with

#### Universal Approximator

Don't think an ANN can model it? Think again — they're universal!

4. Included with