Advanced ChatGPT Usage - ChatGPT Secret Class to Use 100x Better Than Others
Masocampus
AI will not replace people. But 'you utilizing AI' will replace all who don't.
入門
ChatGPT, AI, prompt engineering
Deep learning practice using DNN, CNN, and RNN
DNN, CNN, RNN? This lecture perfectly organizes the core algorithms of deep learning and puts them into practice through practical deep learning training, even with difficult-looking technical terms!
What you will gain after the course
Understanding the Deep Learning Development Process
Understand the components and model principles of DNN, CNN, and RNN
Understanding the various frameworks of Tensorflow Keras
Ability to utilize deep learning algorithms through various deep learning practices
I want to start using deep learning in earnest!
In the era of the 4th Industrial Revolution, artificial intelligence technology is becoming more important than ever.
Therefore, many companies are actively recruiting AI experts, and there are predictions that many jobs will disappear in the future.
Of course, AI has not yet made a significant impact in areas that were once considered uniquely human, such as art and creative work, but it is only a matter of time before it is gradually replaced.
Deep learning technology is something everyone in the IT industry wants to try, but the barrier to entry is so high that it's difficult to take on the challenge.
Therefore, we designed the deep learning course at Maso Campus so that anyone can follow along and build up their knowledge of deep learning.
To do this, you need to learn and master new deep learning algorithms.
this time The course is a deep learning algorithm model development course based on the Python language and the Tensorflow Keras framework .
In the future, securing the technological capabilities to extract useful information from more data will become increasingly important.
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 DNNs, unsupervised learning CNNs, and reinforcement learning RNNs .
We'll take a look at the overall development process of deep learning, detailing the learning process of developing deep learning models using various algorithms, exploring the pros and cons of each model and practical application cases, inputting actual code to check the differences between models, and utilizing deep learning algorithms!
Course Target 🔑
- Practitioners who want to try utilizing artificial intelligence in their work
- Anyone who wants to build a career in the IT industry, such as starting a business, changing jobs, or joining a company.
- Managers and practitioners who want to introduce artificial intelligence into their businesses
- Anyone who wants to get off to a good start building deep learning capabilities.
Lecture Features ✨
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 .
Changes in students after attending the lecture 📜
- 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.
Recommended for
these people
Who is this course right for?
Practitioners who want to try out AI in their work
Anyone who wants to build a career in the IT industry, such as starting a business, changing jobs, or joining a company
Managers and practitioners who want to introduce artificial intelligence into their business
Anyone who wants to start by learning core techniques to build deep learning capabilities.
Need to know before starting?
This course requires basic Python skills.
I recommend that you take Maso Campus' [Introduction to Python] and [Python Practice] courses first.
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마소캠퍼스 팀은 우리의 모두의 성장을 위해 두 가지 원칙을 반드시 지킵니다.
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Curriculum
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26 lectures ∙ (4hr 33min)
Course Materials:
Lecture resources
Published:
Last updated:
Reviews
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3 reviews
3.7
3 reviews
jongsunlim4560Reviews 2
∙
Average Rating 5.0
5
100% enrolled- masocampusInstructor
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!
- bkkim29128
Reviews 1
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Average Rating 5.0
5
100% enrolledIt's good for introduction.
- masocampusInstructor
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!
- chamdevil2650
Reviews 1
∙
Average Rating 1.0
1
54% enrolledData preprocessing using tokenizer - isn't the embedding part included in the lecture? The key point is missing ;;;;;;;;
- masocampusInstructor
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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