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Tests related to deep learning | Difficulty of G test and E qualification !

 E qualification

I think there are many people who are thinking of taking a deep learning test, but do not know which test to take, or do not know how to study. Therefore, in this article, I would like to introduce an overview and study methods for G-tests and E-qualifications.

Table of Contents

  • Advantages of taking a deep learning certification
  • Two tests for deep learning
    • G test
    • E-qualification
    • Difference between G test and E qualification
  • How to study for exams related to deep learning
    • Learn online
    • Learn from books
  • Recommended Courses for Examinations on Deep Learning
    • G test
    • E-qualification
  • Recommended books for certification on deep learning
    • G Test
    • E-qualification
  • Demand and Future of Deep Learning Tests
  • Summary

Advantages of taking a deep learning certification

First of all, obtaining E qualification makes it easier to prove your skills and knowledge. If you have a E qualification , you can prove your ability even if you don’t have experience, and you can advance your job or change jobs.

The second is that you can acquire systematic knowledge and skills for business utilization. As a result, not only those who work related to artificial intelligence, but also those who are in charge of sales and planning at IT companies can deepen their understanding of their businesses and products.

Finally, if you pass the G test, you have the advantage of being able to participate in a community called “CDLE”. If you join, you will be able to participate in study sessions and events held in the community, so you will have the opportunity to deepen your knowledge.

Two tests for deep learning

There are mainly G-tests and E-qualifications for deep learning-related tests.

G test

Basic information

It tests whether you have the basic knowledge of deep learning, determine appropriate utilization policies, and have the ability and knowledge to utilize it in your business. Anyone can get it if they study. The passing line is not officially published.

  • Purpose To learn how to use AI in business, such as machine learning and deep learning, their respective methodologies and examples.
  • Exam fee General: 13,200 yen (tax included), Student: 5,500 yen (tax included)
  • Exam time 120 minutes
  • About 200 questions
  • Format Multiple Choice, Online

Test scope/difficulty

The scope of the G-test is as follows.

  • Definition of artificial intelligence
  • Trends in artificial intelligence
  • Problems in the field of artificial intelligence
  • Concrete method of machine learning
  • Deep learning overview
  • Deep learning method
  • Deep learning research field
  • Toward the application of deep learning

Various questions will be asked, such as those that require knowledge of mathematics such as partial differentiation and determinants, and those that answer important phrases related to deep learning.

Application method and eligibility

Individuals can apply from the G Test Application Site ( G Test Office (jdla-exam.org) ). There is no eligibility to take the exam.

E-qualification

Basic information

It is a E qualification  that certifies whether you have the ability and knowledge to understand the theory of deep learning and to select and implement an appropriate method. In addition, in order to take the exam, it is necessary to take a course at a programming school designated by JDEL within two years of the exam and obtain a completion E qualification .

  • Objective To understand the theory and acquire the ability and knowledge of AI implementation or the ability for engineers
  • Exam fee General: 33,000 yen (tax included) Student: 22,000 yen (tax included) Member: 27,500 yen (tax included)
  • Exam time 120 minutes
  • About 100 questions
  • Format: multiple-choice test, taken at a designated test center in each region

Test scope/difficulty

The scope of the E qualification is as follows.

Applied mathematics

  • Linear algebra
  • Probability
  • Statistics
  • Information theory

Machine learning

  • Fundamentals of machine learning
  • Practical methodology

Deep learning

  • Forward propagating network
  • Normalization for deep models
  • Optimization for deep models
  • Convolutional network
  • Regressive coupled neural networks and recursive networks
  • Generative model
  • Reinforcement learning
  • Adaptation method of deep learning

Development/operation environment

  • Middleware
  • Weight saving
  • High-speed technology

As you can see, the questions are mainly based on deep learning, but the questions cover a wide range of topics such as machine learning, applied mathematics, and technology related to implementation.

Application method and eligibility

To be eligible, you must have completed a JDLA certification program within the last two years prior to the exam date. JDLA certifies and recommends courses that develop human resources who understand the theory of deep learning and have the ability to select and implement appropriate methods as JDLA certification programs.

When reserving the exam, you will be required to enter the “Completion Number” and “Certification Program Completion Date” to prove that you have completed the certification program.

Difference between G test and E qualification

The G test is a E qualification for business that utilizes a wide range of knowledge about deep learning and its use in business. In addition, there are no particular restrictions on eligibility for taking the exam, so a wide variety of people can take the exam. Anyone can learn it relatively easily.

On the other hand, the E qualification is a E qualification for engineers that understands the theory of deep learning better and has the ability to select and implement appropriate methods. Therefore, in addition to knowledge of applied mathematics, machine learning, and deep learning, it is necessary to have the ability to implement it concretely, so completing the certification program recommended by JDLA is a condition for taking the exam. .

How to study for exams related to deep learning

There are two main ways to study: self-study from books or online courses.

Learn online

Currently, there are many online courses that specialize in deep learning certification. It is recommended for those who feel uneasy about studying on their own, or for those who want to study efficiently in a short period of time.

Also, if you are online, even busy people can choose the course that suits them. Therefore, even those who are too busy to find time to study on their own will be able to learn easily.

Learn from books

In recent years, with the development of AI, many people have taken exams related to AI. As a result, there are many books available for studying these exams.

If you buy books and study by yourself, you can study in spare time such as on the train, so even busy people can easily start studying.

Recommended Courses for Examinations on Deep Learning

G test

AVILEN

AVILEN has a wealth of exercises with over 360 questions. Therefore, it is recommended for those who want to practice and learn properly. It also has the added benefit of a full money-back guarantee if you are unsuccessful.

Zero to One

Zero to One has over 650 practice questions. However, this only provides practice problems, and there are no lecture videos. Therefore, it is recommended for those who have prior knowledge and want to solve a lot of exercises.

E-qualification

Aidemy

With Aidemy, you can easily start learning with just your usual browser. Also, if you register as a member, you can take introductory courses on mathematics and Python, which are necessary for AI, for free. In addition, there are various courses available, so you can study in a course that suits your level.

AVILEN

AVILEN’s E qualification course achieved the highest number of successful applicants for the second consecutive term with a pass rate of 85.7%. It is recommended for those who want to take proper measures and ensure that they pass.

Recommended books for certification on deep learning

G test

Deep Learning G Test Official Text

This is the official text written by the Japan Deep Learning Association, the governing body of the G test. We cater to a wide range of needs, including those who are considering taking the G-test, as well as those who want to learn about deep learning and those who want to use it in their businesses.

Shortest breakthrough deep learning G test (generalist) problem collection

This is a carefully explained explanation by AVILEN Co., Ltd., which has a track record of education related to G tests and E qualifications. At the end of each chapter, there is also a page summarizing key phrases. If you are going to take the G test, you should read it once.

E-qualification

Thorough capture deep learning E qualification engineer problem collection

This book is written by instructors of Skill Up AI, which is engaged in educational projects for AI human resource development. In addition, we have posted a mock test that thoroughly analyzes the tendency of questions in the E qualification test. Therefore, it is a good idea to read it when taking the E qualification.

Deep Learning from Scratch ―Theory and Implementation of Deep Learning in Python

In this book, you can understand not only the basics of deep learning and neural networks, but also the inverse error radio method and convolutional neural networks at the implementation level. Recommended for those who want to start serious study.

Demand and Future of Deep Learning Tests

Currently, AI is being used in various fields such as business, and human resources with AI technology and knowledge are widely needed. Therefore, taking the G test, E qualification, and other AI-related tests introduced here and having knowledge will be useful in various situations.

Summary

In this article, I introduced two tests related to deep learning. It is important to choose which test to take and how to study while thinking about what you are aiming for.

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