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Introduction to Neural Networks

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Introduction to Neural Networks provided by Brilliant is a comprehensive online course, which lasts for 4 hours worth of material. Upon completion of the course, you can receive an e-certificate from Brilliant. The course is taught in Englishand is Paid Course. Visit the course page at Brilliant for detailed price information.

via Brilliant
4 hours worth of material
Self Paced
Overview

  • 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.

Syllabus

    • Introduction: When traditional AI hit a dead end, artificial neural nets jumped in.
      • Can Computers Learn?: Do you have to be living to be learning?
      • The Computer Vision Problem: What's so hard about artificial intelligence? Try seeing in pixels.
      • The Folly of Computer Programming: Why do we need neural networks? Some things just can't be programmed.
      • Neural Networks: Teaching machines to teach themselves.
    • Neurons: The power of neural networks emerges from these simple building blocks.
      • The Decision Box: Meet your first artificial neuron and learn how to encode simple logical operations.
      • Binary Neurons: Take a look inside the building blocks of neural networks
      • Decision Boundaries: Hone your intuition with this graphical model of a binary neuron.
      • Building an XOR Gate: Escape the limitations of single neurons by stacking them in layers.
      • Classification: Sorting things into groups? The neuron knows best.
      • Sigmoid Neurons: Real data isn't black and white, this neuron sees in shades of gray.
      • Training a Single Neuron: Take a shot at building your first learning algorithm.
    • Layers: Connecting neurons together in layers boosts a neural net's performance.
      • Hidden Layers: Got some complex data to classify? Try adding a hidden layer to your ANN.
      • Curve Fitting: Classifying isn't an ANN's only schtick. Dangerous curves ahead...
      • Universal Approximator: Don't think an ANN can model it? Think again — they're universal!
      • A Shape-Recognizing Network: Learn how an ANN learns to see (and how you can trick it).
    • Learning:

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