Build Vuejs from scratch to learn how it works internally

Build your own Vuejs

This project is working in progress, stay tuned.

This repository includes Build your own Vuejs book and the code for it.

In the book Build your own Vuejs, we will build a Vuejs from scratch. And you will learn how Vuejs works internally, which helps a lot for your daily development with vue.

Inspired by the amazing book Build your own Angularjs, the code of Build your own Vuejs will be developed in a test-driven way.

We’ll focus on Vuejs 2.0. And we assume our reader have played around with Vuejs once and know basics about Vuejs APIs.

Table of Contents

Chapter1: Vuejs Overview
Chapter2: Reactivity system
Chapter3: Virtual DOM
Chapter4: Built-in modules: directives, attributes, class and style
Chapter5: Instance methods and global API
Chapter6: Advanced features

Code

develop

npm run watch

test

npm run test

build

npm run build

Lisence

Well, do whatever your like.

Download Details:

Author: jsrebuild

Source Code: https://github.com/jsrebuild/build-your-own-vuejs

#vuejs #vue #javascript

Hire Dedicated VueJS Developers

Want to Hire VueJS Developer to develop an amazing app?

Hire Dedicated VueJS Developers on the contract (time/project) basis providing regular reporting about your app. We, at HourlyDeveloper.io, implement the right strategic approach to offer a wide spectrum of vue.js development services to suit your requirements at most competitive prices.

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#hire dedicated vuejs developers #vuejs developer #vuejs development company #vuejs development services #vuejs development #vuejs developer

Jerad Bailey

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Google Reveals "What is being Transferred” in Transfer Learning

Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.

The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.

#developers corner #learn transfer learning #machine learning #transfer learning #transfer learning methods #transfer learning resources

Hollie Ratke

1603753200

ML Optimization pt.1 - Gradient Descent with Python

So far in our journey through the Machine Learning universe, we covered several big topics. We investigated some regression algorithms, classification algorithms and algorithms that can be used for both types of problems (SVM**, **Decision Trees and Random Forest). Apart from that, we dipped our toes in unsupervised learning, saw how we can use this type of learning for clustering and learned about several clustering techniques.

We also talked about how to quantify machine learning model performance and how to improve it with regularization. In all these articles, we used Python for “from the scratch” implementations and libraries like TensorFlow, Pytorch and SciKit Learn. The word optimization popped out more than once in these articles, so in this and next article, we focus on optimization techniques which are an important part of the machine learning process.

In general, every machine learning algorithm is composed of three integral parts:

A loss function.
Optimization criteria based on the loss function, like a cost function.
Optimization technique – this process leverages training data to find a solution for optimization criteria (cost function).

As you were able to see in previous articles, some algorithms were created intuitively and didn’t have optimization criteria in mind. In fact, mathematical explanations of why and how these algorithms work were done later. Some of these algorithms are Decision Trees and kNN. Other algorithms, which were developed later had this thing in mind beforehand. SVMis one example.

During the training, we change the parameters of our machine learning model to try and minimize the loss function. However, the question of how do you change those parameters arises. Also, by how much should we change them during training and when. To answer all these questions we use optimizers. They put all different parts of the machine learning algorithm together. So far we mentioned Gradient Decent as an optimization technique, but we haven’t explored it in more detail. In this article, we focus on that and we cover the grandfather of all optimization techniques and its variation. Note that these techniques are not machine learning algorithms. They are solvers of minimization problems in which the function to minimize has a gradient in most points of its domain.

Dataset & Prerequisites

Data that we use in this article is the famous Boston Housing Dataset . This dataset is composed 14 features and contains information collected by the U.S Census Service concerning housing in the area of Boston Mass. It is a small dataset with only 506 samples.

For the purpose of this article, make sure that you have installed the following _Python _libraries:

**NumPy **– Follow this guide if you need help with installation.
**SciKit Learn **– Follow this guide if you need help with installation.
Pandas – Follow this guide if you need help with installation.

Once installed make sure that you have imported all the necessary modules that are used in this tutorial.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import SGDRegressor

Apart from that, it would be good to be at least familiar with the basics of linear algebra, calculus and probability.

Why do we use Optimizers?

Note that we also use simple Linear Regression in all examples. Due to the fact that we explore optimizationtechniques, we picked the easiest machine learning algorithm. You can see more details about Linear regression here. As a quick reminder the formula for linear regression goes like this:

where w and b are parameters of the machine learning algorithm. The entire point of the training process is to set the correct values to the w and b, so we get the desired output from the machine learning model. This means that we are trying to make the value of our error vector as small as possible, i.e. to find a global minimum of the cost function.

One way of solving this problem is to use calculus. We could compute derivatives and then use them to find places where is an extrema of the cost function. However, the cost function is not a function of one or a few variables; it is a function of all parameters of a machine learning algorithm, so these calculations will quickly grow into a monster. That is why we use these optimizers.

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Shana Towne

1591326529

How Deep Learning Works with Different Neuron Layers

How Deep Learning Works with Different Neuron Layers
Artificial Intelligence, Machine Learning, and Deep Learning come under Data Science. These terms are small but have changed technology. They have given a new direction to technology. The first step to understanding how deep learning works is to grasp the differences between AI, ML, and Deep Learning

#deep learning working #how deep learning works #machine learning

Jack Downson

1598707020