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Learn WebAssembly With WebAssembly Studio

Introduction

As web technologies have advanced, there’s been a push to move more and more applications to the web. This has presented developers with another challenge because web browsers support only one programming language: JavaScript. As browser makers looked for ways to improve JavaScript’s performance, they came up with a great new invention: WebAssembly.

WebAssembly was first introduced in 2015, and, alongside HTML, CSS, and JS, it became the fourth language for the Web that runs natively in browsers (W3C announced on  5 December 2019 ).

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Learn TensorFlow: Linear Regression

Introduction to Linear Regression

An important algorithm of supervised learning is linear regression. In this article, I am going to re-use the following notations that I have referred from [1] (in the References section).

  • xi denotes the “input” variables, also called input features
  • yi denotes the “ouput” or target variable that we are trying to predict
  • A pair (xi, yi) is called a training example
  • A list of m training examples {xi, yi; i = 1,…,m} is called a training set
  • The superscript “i” in the notation is an index into the training set
  • X denotes the space of input values and Y denotes the space of output values. In this article, I am going to assume that X = Y = R
  • A function h: X -> Y, where h(x) is a good predictor for the corresponding value of y, is called a hypothesis or a model

When the target variable that we are trying to predict is continuous, we call the learning problem a regression problem. When y takes on only a small number of discrete values, we call it a classification problem.

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Learn TensorFlow: Vectors

TensorFlow

TensorFlow is an open-source library that was developed by the Google Brain team, and it was released in November 2015. Before working with TensorFlow, we need to understand the following basic concepts:

  • Graph: Layout of the learning process. It does not include data.
  • Data: Examples that are used to train. It has two kinds, which are inputs and targets.
  • Session: Where we feed the graph with data or Session = Graph + Data. We can do this by using placeholders — gates to introduce examples.

We can install Anaconda to use TensorFlow.