Deep learning practice using DNN, CNN, and RNN

Through this course, you will understand the operating principles of DNN, CNN, and RNN, which are the hottest technologies today, and you will be able to implement deep learning models through practice .

STEP 1. Understand the concepts and operating processes of DNN, CNN, and RNN.

Among the numerous deep learning algorithms, we will take a detailed look at and delve into the principles of DNN, CNN, and RNN , as they show different levels of performance depending on which algorithm is used in which situation !

STEP 2. Practice the Deep Learning Modeling Process

Representative deep learning algorithms , regression and classification !

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

You can use deep learning algorithm models that I designed myself and use them in various practical ways to apply them immediately .

STEP 3. Bringing insights discovered in the digital world to the real world

If we design and train models using various techniques of deep learning algorithms and then apply the obtained insights to actual work , we can bring about revolutionary advancements in data-based decision-making in a vast range of areas, including sales , development , and human resources .

After taking the Deep Learning Practical Course > , you will be able to acquire the following capabilities .

• Understanding the Deep Learning Development Process
• Understanding the components and model principles of DNN, CNN, and RNN
• Understanding the various frameworks of Tensorflow Keras
• Deep learning application skills through various deep learning exercises

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

What you'll learn 📚

1. Designing a multiple linear regression model!

2. Visualizing the MNIST handwriting model evaluation results!

3. Components of CNN!

4. Run the model with CNN!

5. Classifying news categories using LSTM!

Expected Questions Q&A 💬

Q. Is prior knowledge of Python programming required ?
A. This course and subsequent deep learning courses on Maso Campus require basic Python skills . If you're unfamiliar with Python, we recommend taking Maso Campus ' " Introduction to Python Data Analysis " and " Practical Python Data Analysis " courses first .

Q. Are there any requirements or prerequisites for taking the course ?
A. Since this is a practice-oriented lecture, it would be a good idea to prepare a dual monitor or extra device that can separate the lecture and practice screens .

Q. I heard that deep learning requires a high-spec PC . Is a high-spec PC necessary for practical training ?
A. It is recommended to run it in a high-spec environment , but since this lecture is conducted in a virtual environment using Anaconda and Jupyter Notebook , you will have no difficulty taking the lecture if you have a general work PC .

Introducing the Knowledge Sharer ✒️

Things to note before taking the course 📢

• Since this is a practice-oriented lecture, it would be a good idea to prepare a dual monitor or extra device that can separate the lecture and practice screens.
  Additionally, since the training is conducted based on Windows OS, we recommend taking the course in a Windows environment.
• Lecture notes and practice files are available in the <9. Textbook Download Center> section.