DeepLearning With TensorFlowJS 1 - Train Data and Test Data

This tutorial is based on the book Deep Learning With JavaScript (TensorFlowJS).

The first script loads the TensorFlow package and defines the symbol tf, which provides a way to refer to names in TensorFlow.

The second script creates two constants, trainData and testData, each representing 20 samples of how long it took to download a file (timeSec) and the size of that file (sizeMB). The elements in sizeMB and those in timeSec have one-to-one correspondence. For example, the first element of sizeMB in trainData is 0.080 MB, and downloading that file took 0.135 seconds—that is, the first element of timeSec—and so forth.

The goal in this example will be to estimate timeSec, given just sizeMB.

<script src='https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest'></script> <script> const trainData = { sizeMB: [0.080, 9.000, 0.001, 0.100, 8.000, 5.000, 0.100, 6.000, 0.050, 0.500, 0.002, 2.000, 0.005, 10.00, 0.010, 7.000, 6.000, 5.000, 1.000, 1.000], timeSec: [0.135, 0.739, 0.067, 0.126, 0.646, 0.435, 0.069, 0.497, 0.068, 0.116, 0.070, 0.289, 0.076, 0.744, 0.083, 0.560, 0.480, 0.399, 0.153, 0.149] }; const testData = { sizeMB: [5.000, 0.200, 0.001, 9.000, 0.002, 0.020, 0.008, 4.000, 0.001, 1.000, 0.005, 0.080, 0.800, 0.200, 0.050, 7.000, 0.005, 0.002, 8.000, 0.008], timeSec: [0.425, 0.098, 0.052, 0.686, 0.066, 0.078, 0.070, 0.375, 0.058, 0.136, 0.052, 0.063, 0.183, 0.087, 0.066, 0.558, 0.066, 0.068, 0.610, 0.057] }; console.log(testData.timeSec); console.log(testData.sizeMB); </script>

https://codepen.io/tfjs-book/pen/VEVMbx

[ebook] Neural Networks and Deep Learning free online book.

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CHAPTER 1: Using neural nets to recognize handwritten digits

In this chapter we'll write a computer program implementing a neural network that learns to recognize handwritten digits. 

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CHAPTER 2: How the backpropagation algorithm works

In this chapter I'll explain a fast algorithm for computing such gradients, an algorithm known as backpropagation.

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CHAPTER 3:Improving the way neural networks learn

In this chapter I explain a suite of techniques which can be used to improve on our vanilla implementation of backpropagation, and so improve the way our networks learn.

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CHAPTER 4:A visual proof that neural nets can compute any function

In this chapter I give a simple and mostly visual explanation of the universality theorem. 

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CHAPTER 5:Why are deep neural networks hard to train?

In this chapter, we'll try training deep networks using our workhorse learning algorithm - stochastic gradient descent by backpropagation.

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CHAPTER 6:Deep learning

In this chapter, we'll develop techniques which can be used to train deep networks, and apply them in practice.

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