Cristian Vasta

Cristian Vasta

1606429380

A Playground for Learning Reactjs

React

Overview

This is a teaching repository for React created by Facebook.

The repo is sorted into single file react projects which can be run as standalone files ie they have React imported as a CDN into a single HTML file.

This repository is a combination of my own learning, also learning React from Udemy’s web developer course of 2018, and also from Egghead which has a course on React.

Also I have a much more complex set of standalone projects built with the create-react-app here.

Single File React HTML Pages

Single File React HTML Pages

Standalone Projects

React Projects Built With create-react-app

API Projects

Resources

reactforbeginners.com

Flavio Copes React Beginners Handbook

React Learning Path

Build This Game On React Docs Tutorial

FreeCodeCamp React Tutorial

Free React Course https://scrimba.com/learn/learnreact

Live Play Around With React And Learn Basics - All In Browser

Examples - https://www.jqwidgets.com

[Facebook React Tutorial] https://facebook.github.io/react/tutorial/tutorial.html

Terms

  • HOC Higher Order Component
  • Render props - used to share state between components
  • reducer - function which determines changes to state.
  • reducer - pure function, takes previous state and action and returns next state
  • Redux - manages state changes

Introduction And Getting Started

Introduction And Getting Started

JSX

We can read about JSX in detail here

https://reactjs.org/docs/jsx-in-depth.html#html-tags-vs.-react-components

Rendering

Rendering Components

Components

Components

Routing Between Components

Routing

Elements

Elements

API

API

API Google Calendar

API Google Calendar

API Google Maps

API Google Maps

Lifecycle

lifecycle methods in a class

// after component mounted
componentDidMount(){}
componentWillUnmount(){}
// this updates any time any part of the component updates
componentDidUpdate(){}

lifecycle methods in hooks

import React, {useState, useEffect} from 'react'
export default function MyComponent(props){
  const [name, setName] = useState('some name');
  // runs after initial render and after every update
  useEffect(()=>{
    // add javascript here to update whenever any part of the component updates
    // for example could asynchronously update an api 
    axios.POST(url,name)...
  })
  return(<></>)
}

React Native

This is probably a whole new repo in itself but I am going to make a start here!

React Native

React Hooks

React Hooks

React With JWT

React with JWT

Download Details:

Author: philanderson888

Source Code: https://github.com/philanderson888/react

#react #reactjs #javascript

What is GEEK

Buddha Community

A Playground for Learning Reactjs
Jerad  Bailey

Jerad Bailey

1598891580

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

sophia tondon

sophia tondon

1620898103

5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

#machinelearningapps #machinelearningdevelopers #machinelearningexpert #machinelearningexperts #expertmachinelearningservices #topmachinelearningcompanies #machinelearningdevelopmentcompany

Visit Blog- https://www.xplace.com/article/8743

#machine learning companies #top machine learning companies #machine learning development company #expert machine learning services #machine learning experts #machine learning expert

Jackson  Crist

Jackson Crist

1617331066

Intro to Reinforcement Learning: Temporal Difference Learning, SARSA Vs. Q-learning

Reinforcement learning (RL) is surely a rising field, with the huge influence from the performance of AlphaZero (the best chess engine as of now). RL is a subfield of machine learning that teaches agents to perform in an environment to maximize rewards overtime.

Among RL’s model-free methods is temporal difference (TD) learning, with SARSA and Q-learning (QL) being two of the most used algorithms. I chose to explore SARSA and QL to highlight a subtle difference between on-policy learning and off-learning, which we will discuss later in the post.

This post assumes you have basic knowledge of the agent, environment, action, and rewards within RL’s scope. A brief introduction can be found here.

The outline of this post include:

  • Temporal difference learning (TD learning)
  • Parameters
  • QL & SARSA
  • Comparison
  • Implementation
  • Conclusion

We will compare these two algorithms via the CartPole game implementation. This post’s code can be found here :QL code ,SARSA code , and the fully functioning code . (the fully-functioning code has both algorithms implemented and trained on cart pole game)

The TD learning will be a bit mathematical, but feel free to skim through and jump directly to QL and SARSA.

#reinforcement-learning #artificial-intelligence #machine-learning #deep-learning #learning

E-learning Software Services - SISGAIN

SISGAIN is one of the top e-Learning software companies in New York, USA. Develop Education Technology based, mobile application for e-learning from SISGAIN. We Develop User Friendly Education App and Provide e-learning web portals development Service. Get Free Quote, Instant Support & End to End Solution. SISGAIN has been developing educational software and provides e-learning application development services for US & UK clients. For more information call us at +18444455767 or email us at hello@sisgain.com

#learning development companies #development of software for e-learning #top e-learning software companies #e-learning web portals #mobile applications for e-learning #e-learning product development

Few Shot Learning — A Case Study (2)

In the previous blog, we looked into the fact why Few Shot Learning is essential and what are the applications of it. In this article, I will be explaining the Relation Network for Few-Shot Classification (especially for image classification) in the simplest way possible. Moreover, I will be analyzing the Relation Network in terms of:

  1. Effectiveness of different architectures such as Residual and Inception Networks
  2. Effects of transfer learning via using pre-trained classifier on ImageNet dataset

Moreover, effectiveness will be evaluated on the accuracy, time required for training, and the number of required training parameters.

Please watch the GitHub repository to check out the implementations and keep updated with further experiments.

Introduction to Few-Shot Classification

In few shot classification, our objective is to design a method which can identify any object images by analyzing few sample images of the same class. Let’s the take one example to understand this. Suppose Bob has a client project to design a 5 class classifier, where 5 classes can be anything and these 5 classes can even change with time. As discussed in previous blog, collecting the huge amount of data is very tedious task. Hence, in such cases, Bob will rely upon few shot classification methods where his client can give few set of example images for each classes and after that his system can perform classification young these examples with or without the need of additional training.

In general, in few shot classification four terminologies (N way, K shot, support set, and query set) are used.

  1. N way: It means that there will be total N classes which we will be using for training/testing, like 5 classes in above example.
  2. K shot: Here, K means we have only K example images available for each classes during training/testing.
  3. Support set: It represents a collection of all available K examples images from each classes. Therefore, in support set we have total N*K images.
  4. Query set: This set will have all the images for which we want to predict the respective classes.

At this point, someone new to this concept will have doubt regarding the need of support and query set. So, let’s understand it intuitively. Whenever humans sees any object for the first time, we get the rough idea about that object. Now, in future if we see the same object second time then we will compare it with the image stored in memory from the when we see it for the first time. This applied to all of our surroundings things whether we see, read, or hear. Similarly, to recognise new images from query set, we will provide our model a set of examples i.e., support set to compare.

And this is the basic concept behind Relation Network as well. In next sections, I will be giving the rough idea behind Relation Network and I will be performing different experiments on 102-flower dataset.

About Relation Network

The Core idea behind Relation Network is to learn the generalized image representations for each classes using support set such that we can compare lower dimensional representation of query images with each of the class representations. And based on this comparison decide the class of each query images. Relation Network has two modules which allows us to perform above two tasks:

  1. Embedding module: This module will extract the required underlying representations from each input images irrespective of the their classes.
  2. Relation Module: This module will score the relation of embedding of query image with each class embedding.

Training/Testing procedure:

We can define the whole procedure in just 5 steps.

  1. Use the support set and get underlying representations of each images using embedding module.
  2. Take the average of between each class images and get the single underlying representation for each class.
  3. Then get the embedding for each query images and concatenate them with each class’ embedding.
  4. Use the relation module to get the scores. And class with highest score will be the label of respective query image.
  5. [Only during training] Use MSE loss functions to train both (embedding + relation) modules.

Few things to know during the training is that we will use only images from the set of selective class, and during the testing, we will be using images from unseen classes. For example, from the 102-flower dataset, we will use 50% classes for training, and rest will be used for validation and testing. Moreover, in each episode, we will randomly select 5 classes to create the support and query set and follow the above 5 steps.

That is all need to know about the implementation point of view. Although the whole process is simple and easy to understand, I’ll recommend reading the published research paper, Learning to Compare: Relation Network for Few-Shot Learning, for better understanding.

#deep-learning #few-shot-learning #computer-vision #machine-learning #deep learning #deep learning