Learn PyTorch Deep Learning Basics in 6 Days

Even beginners can invest in just 6 days
I can understand the basics of deep learning. 🎓

This course is designed to help you easily learn the basic concepts and techniques of deep learning, focusing on PyTorch, an intuitive and powerful deep learning framework.

This course combines theory and practice to systematically learn the core principles of deep learning . Complex mathematical concepts are explained in a simple and easy-to-understand manner , and students can gain hands-on experience through practice. We structured the theory and practice to enable implementation of the model .

You'll gain practical skills in designing, training, and evaluating deep learning models on diverse datasets. If you want to build a solid foundation and develop skills that can be immediately applied to future learning and projects, this course is for you!

Take your first step into the world of deep learning with PyTorch! 🤖

✨ Features of this course ✨

📌 You can learn the basics of deep learning based on PyTorch by taking this course for 1 to 2 hours per day for 6 days .

📌 For days 1 to 6, the first lecture of each day is designed to help you familiarize yourself with the concepts by reviewing the previous content , and the last lecture is designed to summarize what you learned that day.

📌 From Days 1 to 5, you will learn essential concepts to master the fundamentals of deep learning, and on the final Day 6, you will reinforce what you have learned through comprehensive practice.

📌 50% theory, 50% examples and practice. You'll learn the theory through code, practice, and practice.

📌 This course is for beginners in deep learning .

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After class

• Easily build deep learning models with PyTorch : Design deep learning models and implement them directly using PyTorch. Even complex models can be handled with ease.

• Effective Hyperparameter Tuning : Learn how to tune hyperparameters to maximize the performance of your deep learning models, and learn what to adjust to improve performance.

• Analyze the training process and results of deep learning models : Learn how to monitor the model's training process, interpret the results, and improve the model.

Who created this course

• Career,

  • I have been a data engineer for 10 years since I was a newbie.

  • For the next 10 years, I worked in data analysis and artificial intelligence development.

• The lecture is,

  • I am giving lectures on SQL, data analysis, and modeling to current employees, job seekers, and college students.

  • Recently, I have been participating as a main instructor for KT AIVLE School from the 1st to the 6th semester.

    
    

Things to note before taking the course