Train without labeling data using Self-Supervised Learning by Relational Reasoning

Background and challenges 📋

In a modern deep learning algorithm, the dependence on manual annotation of unlabeled data is one of the major limitations. To train a good model, usually, we have to prepare a vast amount of labeled data. In the case of a small number of classes and data, we can use the pre-trained model from the labeled public dataset and fine-tune a few last layers with your data. However, in real life, it’s easily faced with the problem when your data is considerably large (the products in the store or the face of a human,…) and it will be difficult for the model to learn with just a few trainable layers. Furthermore, the amount of unlabeled data (e.g. document text, images on the Internet) is uncountable. Labeling all of them for the task is almost impossible but not utilizing them is definitely a waste.

In this case, training a deep model again from scratch with a new dataset will be an option but it takes a lot of time and effort for labeling data while using a pre-trained deep model seems no longer helpful. That is the reason why Self-supervised learning was born. The idea behind this is simple, which serves two main tasks:

  • **Surrogate task: **the deep model will learn generalizable representations from unlabeled data without annotation, and then will be able to self-generate a supervisory signal exploiting implicit information.
  • **Downstream task: **representations will be fine-tuned for supervised-learning taskse.g. classification and image retrieval with less number of labeled data (the number of labeled data depending on the performance of model based on your requirement)

There are much different training approaches proposed to learn such representations: **Relative position [1]: **themodel needs to understand the spatial context of objects to tell the relative position between parts; **Jigsaw puzzle [2]: **the model needs to place 9 shuffled patches back to the original locations; Colorization [3]: the model has trained to color a grayscale input image; precisely the task is to map this image to a distribution over quantized color value outputs; **Counting features [4]: **The model learns a feature encoder using feature counting relationship of input images transforming by _Scaling_and_Tiling; _**SimCLR [5]: **The model learns representations for visual inputs by maximizing agreement between differently augmented views of the same sample via a contrastive loss in the latent space.

However, I would like to introduce one interesting approach that is able to recognize things like a human. The key factor in human learning is the acquisition of new knowledge by comparing relating and different entities. So, it is a nontrivial solution if we can apply a similar mechanism in self-supervised machine learning via the Relational reasoning approach [6].

The relational reasoning paradigm is based on a key design principle: the use of a relation network as a learnable function on the unlabeled dataset to quantify the relationships between views of the same object (intra-reasoning) and relationships between different objects in different scenes (inter-reasoning). The possibility to exploit a similar mechanism in self-supervised machine learning via relational reasoning was evaluated by the performance on standard datasets (CIFAR-10, CIFAR-100, CIFAR-100–20, STL-10, tiny-ImageNet, SlimageNet), learning schedule, and backbones (both shallow and deep). The results show that the Relational reasoning approach largely outperforms the best competitor in all conditions by an average 14% accuracy and the most recent state-of-the-art method by 3% indicating in this paper [6].

#deep-learning #computer-vision #machine-learning #self-supervised-learning #data-science #machine learning

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Train without labeling data using Self-Supervised Learning by Relational Reasoning

Train without labeling data using Self-Supervised Learning by Relational Reasoning

Background and challenges 📋

In a modern deep learning algorithm, the dependence on manual annotation of unlabeled data is one of the major limitations. To train a good model, usually, we have to prepare a vast amount of labeled data. In the case of a small number of classes and data, we can use the pre-trained model from the labeled public dataset and fine-tune a few last layers with your data. However, in real life, it’s easily faced with the problem when your data is considerably large (the products in the store or the face of a human,…) and it will be difficult for the model to learn with just a few trainable layers. Furthermore, the amount of unlabeled data (e.g. document text, images on the Internet) is uncountable. Labeling all of them for the task is almost impossible but not utilizing them is definitely a waste.

In this case, training a deep model again from scratch with a new dataset will be an option but it takes a lot of time and effort for labeling data while using a pre-trained deep model seems no longer helpful. That is the reason why Self-supervised learning was born. The idea behind this is simple, which serves two main tasks:

  • **Surrogate task: **the deep model will learn generalizable representations from unlabeled data without annotation, and then will be able to self-generate a supervisory signal exploiting implicit information.
  • **Downstream task: **representations will be fine-tuned for supervised-learning taskse.g. classification and image retrieval with less number of labeled data (the number of labeled data depending on the performance of model based on your requirement)

There are much different training approaches proposed to learn such representations: **Relative position [1]: **themodel needs to understand the spatial context of objects to tell the relative position between parts; **Jigsaw puzzle [2]: **the model needs to place 9 shuffled patches back to the original locations; Colorization [3]: the model has trained to color a grayscale input image; precisely the task is to map this image to a distribution over quantized color value outputs; **Counting features [4]: **The model learns a feature encoder using feature counting relationship of input images transforming by _Scaling_and_Tiling; _**SimCLR [5]: **The model learns representations for visual inputs by maximizing agreement between differently augmented views of the same sample via a contrastive loss in the latent space.

However, I would like to introduce one interesting approach that is able to recognize things like a human. The key factor in human learning is the acquisition of new knowledge by comparing relating and different entities. So, it is a nontrivial solution if we can apply a similar mechanism in self-supervised machine learning via the Relational reasoning approach [6].

The relational reasoning paradigm is based on a key design principle: the use of a relation network as a learnable function on the unlabeled dataset to quantify the relationships between views of the same object (intra-reasoning) and relationships between different objects in different scenes (inter-reasoning). The possibility to exploit a similar mechanism in self-supervised machine learning via relational reasoning was evaluated by the performance on standard datasets (CIFAR-10, CIFAR-100, CIFAR-100–20, STL-10, tiny-ImageNet, SlimageNet), learning schedule, and backbones (both shallow and deep). The results show that the Relational reasoning approach largely outperforms the best competitor in all conditions by an average 14% accuracy and the most recent state-of-the-art method by 3% indicating in this paper [6].

#deep-learning #computer-vision #machine-learning #self-supervised-learning #data-science #machine learning

Siphiwe  Nair

Siphiwe Nair

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

What is the cost of Hadoop Training in India?

Hadoop is an open-source setting that delivers exceptional data management provisions. It is a framework that assists the processing of vast data sets in a circulated computing habitat. It is built to enhance from single servers to thousands of machines, each delivering computation, and storage. Its distributed file system enables timely data transfer rates among nodes and permits the system to proceed to conduct unbroken in case of a node failure, which minimizes the risk of destructive system downfall, even if a crucial number of nodes become out of action. Hadoop is very helpful for massive scale businesses founding on its proven usefulness for enterprises given below:

Benefits for Enterprises:

● Hadoop delivers a cost-effective storage outcome for a business.
● It promotes businesses to handily access original data sources and tap into numerous categories of data to generate value from that data.
● It is a highly scalable storage setting.
● The distinctive storage procedure of Hadoop is established on a distributed file system that basically ‘maps’ data wherever it is discovered on a cluster. The tools for data processing are often on similar servers where the data is located, occurring in the much faster data processing.
● Hadoop is now widely operated across enterprises, including finance, media and entertainment, government, healthcare, information services, retail, and other commerce
● Hadoop is fault tolerance. When data is delivered to an individual node, that data is also reproduced to other nodes in the cluster, which implies that in the event of loss, there is another copy accessible for usage.
● Hadoop is more than just a rapid, affordable database and analytics device. It is composed of a scale-out architecture that can affordably reserve all of a company’s data for later usage.

Join Big Data Hadoop Training Course to get hands-on experience.

Demand for Hadoop:

Low expense enactment of the Hadoop forum is tempting the corporations to acquire this technology more conveniently. The data management enterprise has widened from software and web into retail, hospitals, government, etc. This builds an enormous need for scalable and cost-effective settings of data storage like Hadoop.
Are you looking for big data analytics training in Noida? KVCH is your go-to institute.

Big Data Hadoop Training Course at KVCH is administered by Experts who provide Online training for big data. KVCH offers Extensive Big Data Hadoop Online Training to learn Big data Hadoop architecture.
At KVCH with the assistance of Big Data Training, make your Big Data Developer Dream Job comes true. KVCH provides Advanced Big Data Hadoop Online Training. Don’t Just Dream to become a Certified Pro Big Data Hadoop Developer achieve it with India’s leading Best Big Data Hadoop Training in Noida.
KVCH’s Advanced Big Data Hadoop Online Training is packed with Best in Industry Certified Professionals who have More than 20+ Big Data Hadoop Industry Experience who Can Provide Real-time Experience As per The Current Industry Needs.

Are you the one who is very passionate to learn Big Data Hadoop Technology from scratch? The one who is eager to understand how this technology functions? Then you’re landed in the right place where you can enhance your skills in this field with KVCH’s Advanced Big Data Hadoop Online Training.
Enroll in Big Data Hadoop Certification Training and receive a Global Certification.
Improve your career progress by discovering the most strenuous technology i.e. Big Data Hadoop Course from the industry-certified experts of Best Big Data Hadoop Online Training. So, choose KVCH the best coaching center and get advanced course complete certification with 100% Job Assistance.

**Why KVCH’s Big Data Hadoop Course should be your choice? **
● Get trained by the finest qualified professionals
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**Upgrade Your Self with KVCH’s Big Data Hadoop Training Course!
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Extensively narrating the IT world presently gets upgraded with ever-renewing technologies every minute. If one lacks much familiarity in coding and doesn’t have an adequate hands-on scripting understanding but still wishes to make an impression in the technical business that too in the IT sector, Big Data Hadoop Online Training is perhaps the niche one requires to begin at. Taking up professional Big Data Training is thus the best option to get to the depth of this language. If one doesn’t have much acquaintance in coding and doesn’t have a good hands-on scripting experience but still wants to make a mark in the technical career that too in the IT sector, Hadoop Corporate Training is probably the place one needs to start at. Adopting skilled Big Data Hadoop Online Training is therefore the promising possibility to get to the center of this language.

#best big data hadoop training in noida #big data analytics training in noida #learn big data hadoop #big data hadoop training course #big data hadoop training and certification #big data hadoop course

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Ananya Gupta

1611381728

What Are The Advantages and Disadvantages of Data Science?

Data Science becomes an important part of today industry. It use for transforming business data into assets that help organizations improve revenue, seize business opportunities, improve customer experience, reduce costs, and more. Data science became the trending course to learn in the industries these days.

Its popularity has grown over the years, and companies have started implementing data science techniques to grow their business and increase customer satisfaction. In online Data science course you learn how Data Science deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions.

Advantages of Data Science:- In today’s world, data is being generated at an alarming rate in all time lots of data is generated; from the users of social networking site, or from the calls that one makes, or the data which is being generated from different business. Because of that reason the huge amount of data the value of the field of Data Science has many advantages.

Some Of The Advantages Are Mentioned Below:-

Multiple Job Options :- Because of its high demand it provides large number of career opportunities in its various fields like Data Scientist, Data Analyst, Research Analyst, Business Analyst, Analytics Manager, Big Data Engineer, etc.

Business benefits: - By Data Science Online Course you learn how data science helps organizations knowing how and when their products sell well and that’s why the products are delivered always to the right place and right time. Faster and better decisions are taken by the organization to improve efficiency and earn higher profits.

Highly Paid jobs and career opportunities: - As Data Scientist continues working in that profile and the salaries of different position are grand. According to a Dice Salary Survey, the annual average salary of a Data Scientist $106,000 per year as we consider data.

Hiring Benefits:- If you have skills then don’t worry this comparatively easier to sort data and look for best of candidates for an organization. Big Data and data mining have made processing and selection of CVs, aptitude tests and games easier for the recruitment group.

Also Read: How Data Science Programs Become The Reason Of Your Success

Disadvantages of Data Science: - If there are pros then cons also so here we discuss both pros and cons which make you easy to choose Data Science Course without any doubts. Let’s check some of the disadvantages of Data Science:-

Data Privacy: - As we know Data is used to increase the productivity and the revenue of industry by making game-changing business decisions. But the information or the insights obtained from the data may be misused against any organization.

Cost:- The tools used for data science and analytics can cost tons to a corporation as a number of the tools are complex and need the people to undergo a knowledge Science training to use them. Also, it’s very difficult to pick the right tools consistent with the circumstances because their selection is predicated on the proper knowledge of the tools also as their accuracy in analyzing the info and extracting information.

#data science training in noida #data science training in delhi #data science online training #data science online course #data science course #data science training