Learn what is a buffer on how to work with it - Node for Beginners

In this video we will Learn what is a buffer on how to work with it in Node

Subscribe : https://www.youtube.com/channel/UCrG2Z0usOCCdUTAr4D1A8mw

#node #nodejs

What is GEEK

Buddha Community

Learn what is a buffer on how to work with it - Node for Beginners

Learn About Util and Inheritance in Node.js

Hello everyone, today we are going to learn about the util module, a frequently used node.js core module. We will learn about what it is, why it is useful, and how to use it in node.js application development. We will also learn about inheritance in the Node.js JavaScript ecosystem. Let’s start.

Util Module

This lesson and the coming one will cover some of the modules that we generally use in development. One of the modules is util and it comes very handy to ease the developer work while debugging. Let’s talk more about it by answering the following questions.

#beginners #node.js #node.js lessons #programming #node

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

Samanta  Moore

Samanta Moore

1620508020

10 Ways Stand Out as a Java Developer and Land that Dream Job

Java is has been one of the most popular programming languages for decades. The number of specialists who want to become proficient in Java is rapidly growing. Because the competition is fierce, it’s no longer enough to just be a good Java developer — you need to acquire deep knowledge and get familiar with many concepts to be ahead of the competition.

If you’re the one who’s stuck asking yourself “What should I learn to stand out as a Java developer?”, this blog post can help you figure things out.

1. Get a Solid Base and Clear Idea of OOP Principles

2. Read Books That Cover Those Principles

3. Get Familiar with the Spring Framework

4. Learn the Most Essential APIs and Libraries

5. Get Deep Knowledge of Java 11

6. Focus on JVM and its Internals

7. Have Multiple Methodologies at Hand

8. Get Used to Automated Testing

9. Polish Up Your Coding Skills

#java #learn-java #java-development-resources #learning-to-code #learn-to-code #beginners #beginners-guide #learn-to-code-java

Samanta  Moore

Samanta Moore

1624955940

12 Common Java Mistakes Made by Newcomers

Everyone makes mistakes, not just beginners, but even professionals. This article goes over a dozen common mistakes that Java newbies and newcomers make and how to avoid them. Have you or your colleagues made any of these common Java mistakes early in your career?

Everyone makes mistakes, not only learners or beginners but professionals. As a programming course, the CodeGym team often collects mistakes of newbies to improve our auto validator. This time we decided to interview experienced programmers about mistakes in Java they made closer to their careers start or noticed them among their young colleagues.

We collected their answers and compiled this list of dozen popular mistakes Java beginners make. The order of errors is random and does not carry any special meaning.

#java #learn-java #java-programming #beginners #beginners-to-coding #learning-to-code #learn-to-code #learn-to-code-java

Tia  Gottlieb

Tia Gottlieb

1596336480

Beginners Guide to Machine Learning on GCP

Introduction to Machine Learning

  • Machine Learning is a way to use some set of algorithms to derive predictive analytics from data. It is different than Business Intelligence and Data Analytics in a sense that In BI and Data analytics Businesses make decision based on historical data, but In case of Machine Learning , Businesses predict the future based on the historical data. Example, It’s a difference between what happened to the business vs what will happen to the business.Its like making BI much smarter and scalable so that it can predict future rather than just showing the state of the business.
  • **ML is based on Standard algorithms which are used to create use case specific model based on the data **. For example we can build the model to predict delivery time of the food, or we can build the model to predict the Delinquency rate in Finance business , but to build these model algorithm might be similar but the training would be different.Model training requires tones of examples (data).
  • Basically you train your standard algorithm with your Input data. So algorithms are always same but trained models are different based on use cases. Your trained model will be as good as your data.

ML, AI , Deep learning ? What is the difference?

Image for post

ML is type of AI

AI is a discipline , Machine Learning is tool set to achieve AI. DL is type of ML when data is unstructured like image, speech , video etc.

Barrier to Entry Has Fallen

AI & ML was daunting and with high barrier to entry until cloud become more robust and natural AI platform. Entry barrier to AI & ML has fallen significantly due to

  • Increasing availability in data (big data).
  • Increase in sophistication in algorithm.
  • And availability of hardware and software due to cloud computing.

GCP Machine Learning Spectrum

Image for post

  • For Data scientist and ML experts , TensorFlow on AI platform is more natural choice since they will build their own custom ML models.
  • But for the users who are not experts will potentially use Cloud AutoML or Pre-trained ready to go model.
  • In case of AutoML we can trained our custom model with Google taking care of much of the operational tasks.
  • Pre-trained models are the one which are already trained with tones of data and ready to be used by users to predict on their test data.

Prebuilt ML Models (No ML Expertise Needed)

  • As discuss earlier , GCP has lot of Prebuilt models that are ready to use to solve common ML task . Such as image classification, Sentiment analysis.
  • Most of the businesses are having many unstructured data sources such as e-mail, logs, web pages, ppt, documents, chat, comments etc.( 90% or more as per various studies)
  • Now to process these unstructured data in the form of text, we should use Cloud Natural Language API.
  • Similarly For common ML problems in the form of speech, video, vision we should use respective Prebuilt models.

#ml-guide-on-gcp #ml-for-beginners-on-gcp #beginner-ml-guide-on-gcp #machine-learning #machine-learning-gcp #deep learning