Example: Building a cat picture startup
Say you’re building a startup that will provide an endless stream of cat pictures to cat lovers. You use a neural network to build a computer vision system for detecting cats in pictures.
But tragically, your learning algorithm’s accuracy is not yet good enough. You are under tremendous pressure to improve your cat detector. What do you do? Your team has a lot of ideas, such as:
• Get more data: Collect more pictures of cats.
• Collect a more diverse training set. For example, pictures of cats in unusual positions; cats with unusual coloration; pictures shot with a variety of camera settings; ….
• Train the algorithm longer, by running more gradient descent iterations.
• Try a bigger neural network, with more layers/hidden units/parameters. • Try a smaller neural network.
• Try adding regularization (such as L2 regularization).
• Change the neural network architecture (activation function, number of hidden units, etc.)
• …
If you choose well among these possible directions, you’ll build the leading cat picture platform, and lead your company to success. If you choose poorly, you might waste months. How do you proceed? This book will tell you how. Most machine learning problems leave clues that tell you what’s useful to try, and what’s not useful to try. Learning to read those clues will save you months or years of development time.
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