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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.

Interactive
quizzes

15

Concepts and
exercises

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.

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      The Decision Box

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

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      Binary Neurons

      Take a look inside the building blocks of neural networks

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      Decision Boundaries

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

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      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.

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      Hidden Layers

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

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      Curve Fitting

      Classifying isn't an ANN's only schtick. Dangerous curves ahead...

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      Universal Approximator

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

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      A Shape-Recognizing Network

      Learn how an ANN learns to see (and how you can trick it).

  4. 4

    Learning

    Coming Soon