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Practical Deep Learning Using DNN, CNN, and RNN

DNN, CNN, RNN? A deep learning practical course where you can perfectly organize core deep learning algorithms and actually put them to use, even with professional terminology that may seem difficult!

(4.0) 4 reviews

38 learners

Level Basic

Course period Unlimited

CNN
CNN
RNN
RNN
dnn
dnn
Deep Learning(DL)
Deep Learning(DL)
Keras
Keras
CNN
CNN
RNN
RNN
dnn
dnn
Deep Learning(DL)
Deep Learning(DL)
Keras
Keras

What you will gain after the course

  • Understanding the Deep Learning Development Process

  • Understanding the components and model principles of DNN, CNN, and RNN

  • Understanding various frameworks of TensorFlow Keras

  • The ability to utilize deep learning algorithms through various hands-on deep learning practices

I want to start using deep learning in earnest!

 
In the era of the Fourth Industrial Revolution, artificial intelligence technology is becoming more important than anything else.
That is why many companies are actively recruiting AI experts, and there are predictions that many jobs will disappear in the future.
Of course, while AI has not yet stood out in areas like art and creativity, which were once considered uniquely human domains, it is likely only a matter of time before they are gradually replaced.
Deep learning technology is something that everyone in the IT industry wants to try, but the reality is that the barrier to entry is so high that it is not easy to take on the challenge.
 
Therefore, Miso Campus has designed this deep learning course so that anyone can follow along and build their knowledge in the field.
To achieve this, you must learn and master new deep learning algorithms.
This <Deep Learning Practice using DNN, CNN, and RNN> course is a deep learning algorithm model development course based on the Python language and the Tensorflow Keras framework.
 
In the future, it will become increasingly important to secure the technical capability to extract useful information from more data.
To solve these problems, Artificial Intelligence (AI)-based machine learning/deep learning technologies have emerged, and the main models used to implement them include supervised learning-based DNN, unsupervised learning-based CNN, and reinforcement learning-based RNN.
Take a detailed look at the overall development process of deep learning and explore a curriculum for developing deep learning models using various algorithms. Learn about the pros and cons of each model along with practical application cases, and experience the differences between models firsthand by writing actual code to utilize deep learning algorithms!

Target Audience 🔑

  • Practitioners who want to try utilizing AI in their work
  • Anyone who wants to build a career in the IT industry through entrepreneurship, career change, or employment.
  • Executives and practitioners who want to introduce AI into their business
  • Anyone who wants to start from the very beginning to properly build their deep learning capabilities.

Lecture Features ✨

Through this course, you can understand the operating principles of DNN, CNN, and RNN, which are the HOTtest deep learning algorithms today, and actually implement deep learning models through hands-on practice.

STEP 1. Understanding the concepts and operational processes of DNN, CNN, and RNN

Among the numerous algorithms in deep learning, we will examine and uncover the principles of DNN, CNN, and RNN in detail, as they deliver a different dimension of performance depending on which algorithm is used in which situation!

STEP 2. Practicing the Deep Learning Modeling Process

Representative deep learning algorithms, regression and classification!

DNN, CNN, and RNN, which can perform regression and classification much more sophisticatedly and accurately than machine learning!

You can use deep learning that can be applied immediately through various hands-on exercises with deep learning algorithm models you design yourself.

STEP 3. Bringing Insights Discovered in the Digital World to the Real World

By designing and training models using various deep learning algorithm techniques and applying the resulting insights to actual tasks, you can bring revolutionary advancements to data-driven decision-making across vast areas, from sales, development, to human resources.


Changes in students after taking the course 📜

<Deep Learning Practice using DNN, CNN, and RNN> After taking this course, you will be able to acquire the following capabilities.

  • Understanding the deep learning development process
  • DNN, CNN, RNN components and understanding of model principles
  • Tensorflow Keras understanding of various Frameworks
  • Deep learning application skills through various deep learning hands-on exercises

Deep learning, which brings overwhelming productivity improvements regardless of the field!
A course to simultaneously learn in-depth theory and practice through detailed explanations of core deep learning algorithms!


Learning Content 📚

1. Designing a multiple linear regression model!

 

2. Visualizing MNIST handwritten digit model evaluation results!

 

3. Components of CNN!

 

4. Running a model with CNN!

 

5. Classifying news categories using LSTM!


Expected Q&A 💬

Q. Do I need prior knowledge of Python programming?
A.
Basic Python skills are required for this course and the subsequent deep learning courses from IT-CAMPUS. For those who are not familiar with Python, we recommend taking IT-CAMPUS's 'Introduction to Python Data Analysis' and 'Practical Python Data Analysis' courses first.

Q. Are there any requirements or prerequisites for taking this course?
A. Since this is a practice-oriented course, it is recommended to have a dual monitor or an extra device to separate the lecture screen from the practice screen.

Q. I heard that deep learning requires a high-spec PC, so do I need a high-spec PC for the practice sessions?
A. While it is better to run it in a high-spec environment, this course conducts practice sessions in a virtual environment using Anaconda and Jupyter Notebook, so a standard business-grade PC will be sufficient to follow along.


Instructor Introduction ✒️


Notes before taking the course 📢

  • Since this is a practice-oriented course, it is recommended to prepare a dual monitor or an extra device so you can separate the lecture screen from the practice screen.
    Additionally, as the practice sessions are based on Windows OS, we recommend taking the course in a Windows environment.
  • The lecture notes and practice files are located in the <9. Textbook Download Center> section.

Recommended for
these people

Who is this course right for?

  • Practitioners who want to try utilizing artificial intelligence in their work

  • Anyone who wants to build a career in the IT industry, including starting a business, changing jobs, or entering the field.

  • Executives and practitioners who want to implement artificial intelligence into their business

  • Anyone who wants to start by properly mastering core techniques to build deep learning capabilities.

Need to know before starting?

  • This course requires basic Python proficiency.

  • We recommend that you first take the [Introduction to Python] and [Python for Business] courses from Maso Campus.

Hello
This is Masocampus

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4.7

Rating

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Courses

"I will grow more than yesterday. And, I will help those who strive to grow more than yesterday."

With Actionable Content that embodies the sincerity and aspirations of Maso Campus,

100 million cumulative hours of lectures shared online and offline since 2013!

This precious experience and time are always the source of growth for both Maso Campus and our students.

The Miso Campus team strictly adheres to two principles for the growth of us all. 1. Actionable Content that can be put into practice immediately after learning. 2. Respecting the time and effort of participants.

The Miso Campus team strictly adheres to two principles for the growth of us all.

1. Actionable Content that you can actually use after learning 2. Time-Saving Curriculum that respects the participant's time and effort Grow with Miso Campus's Actionable and Time-Saving Curriculum

1. Actionable Content that you can surely use once you learn it

2. Time-Saving Curriculum that respects the time and effort of participants

We hope you will walk the path of growth together with Masocampus's Actionable and Time-Saving Curriculum.

We hope you will walk the path of growth together with Maso Campus's Actionable and Time-Saving Curriculum.

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Curriculum

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26 lectures ∙ (4hr 33min)

Course Materials:

Lecture resources
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Reviews

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4 reviews

4.0

4 reviews

  • jongsunlim4560님의 프로필 이미지
    jongsunlim4560

    Reviews 2

    Average Rating 5.0

    5

    100% enrolled

    • masocampus
      Instructor

      Thank you for leaving a review! 😊 It's rewarding to see that the hard work we put in has paid off. Moso Campus will always do its best!

  • bkkim29128님의 프로필 이미지
    bkkim29128

    Reviews 1

    Average Rating 5.0

    5

    100% enrolled

    It's good for introduction.

    • masocampus
      Instructor

      I'm glad that it was a good introductory lecture😊 I will prepare the next lecture well to match the level of the target students!

  • chamdevil2650님의 프로필 이미지
    chamdevil2650

    Reviews 1

    Average Rating 1.0

    1

    54% enrolled

    Data preprocessing using tokenizer - isn't the embedding part included in the lecture? The key point is missing ;;;;;;;;

    • masocampus
      Instructor

      Thank you for taking the course and leaving a constructive review. We will actively consider the curriculum to reflect the points you pointed out when planning future courses.

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