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PyTorch Deep Learning Bootcamp: Implementing CNN and RNN Models from Scratch

This course is designed to help you easily understand and apply deep learning through both theory and practice using PyTorch. Based on their experience in machine learning projects, the instructor explains complex concepts (neural networks, optimization, model training) step-by-step so that anyone can understand them. Many learners know the theory but struggle with implementing actual models. This course is structured with a focus on practice to solve this problem. Through this course, you will understand the core concepts of deep learning and be able to build computer vision and sequence data processing models yourself.

1 learners are taking this course

Level Intermediate

Course period Unlimited

Python
Python
PyTorch
PyTorch
Deep Learning(DL)
Deep Learning(DL)
Computer Vision(CV)
Computer Vision(CV)
NLP
NLP
Python
Python
PyTorch
PyTorch
Deep Learning(DL)
Deep Learning(DL)
Computer Vision(CV)
Computer Vision(CV)
NLP
NLP

What you will gain after the course

  • You can directly implement and train artificial neural network models using PyTorch.

  • Image classification problems can be solved using CNN models.

  • You can process time-series and text data using RNN, LSTM, and GRU models.

  • I can understand and apply loss functions and optimization techniques.

  • You can learn how to process data using PyTorch Dataset and DataLoader.

  • You can learn how to solve overfitting and underfitting problems.

Practical PyTorch Deep Learning: Mastering Image and Sequence Data with CNN & RNN

👉 In this course, you will use PyTorch to directly implement deep learning models used in various industrial fields such as computer vision, natural language processing (NLP), and time series analysis.
👉 Through a practical project-based approach, you will develop the ability to build models applicable to real-world tasks beyond just theory.

💡 (Visual Example Suggestion)

  • CNN Architecture Diagram

  • RNN / LSTM Flowchart

  • Image Classification Result Samples (CIFAR-10)

What You’ll Learn

🧠 Section (1): Core Keywords

Artificial Neural Networks · PyTorch · Data Processing · Optimization

In this section, you will learn the core concepts of deep learning and the basic usage of PyTorch.

  • Understanding the structure and operating principles of Artificial Neural Networks (ANN)

  • The roles and applications of Loss functions and Optimizers

  • Understanding various activation functions (ReLU, Sigmoid, etc.)

  • Data processing using PyTorch Dataset & DataLoader

  • Model training process and performance evaluation methods

💡 (Visual Example Suggestion)

  • Neural Network structure image

  • Loss reduction graph

  • DataLoader Flowchart

👁️ Section (2): Core Keywords

CNN · RNN · LSTM · GRU · Hands-on Projects

In this section, you will develop practical skills by implementing real-world deep learning models.

  • Image classification using CNN (CIFAR-10 practice)

  • Understanding key CNN parameters such as Kernel and Stride

  • How to solve Overfitting / Underfitting problems

  • Understanding the structures and differences of RNN, LSTM, and GRU

  • Hands-on practice with time-series and text data processing

💡 (Visual Example Suggestion)

  • CNN Feature Map Visualization

  • RNN/LSTM structure diagrams

  • Model training result graphs

Before You Enroll

📦 Before You Enroll

📁 Learning Materials

In this course, the following learning materials are provided:

  • 📄 Lecture PPT Materials

  • 💻 PyTorch Practice Code (Full version provided)

  • ☁️ Google Colab Practice Environment

  • 📊 Example datasets (CIFAR-10, etc.)

👉 All materials are downloadable and structured with a focus on hands-on practice.
👉 The code and materials are approximately tens of MBs in size, making them easy to use without any burden.

⚠️ Prerequisites & Notices

✔️ Prerequisites

  • Basic Python Syntax

  • Basic concepts of machine learning (Recommended, not required)

🎥 Lecture Features

  • HD quality videos provided

  • Practice-oriented lectures (high proportion of coding)

  • Step-by-step explanations make it possible for even beginners to learn.

📢 Recommended Learning Method

  • Follow along with the lecture and practice coding yourself

  • Improve understanding through code modification and experimentation

  • Reinforcing concepts through repetitive learning

💬 Q&A and Updates

  • Questions can be asked at any time through the lecture Q&A.

  • Major content updates and additional materials will be provided.

Recommended for
these people

Who is this course right for?

  • Beginners who have a basic understanding of Python but find it difficult to implement deep learning models

  • Learners who know the theory but lack actual project experience

  • Those who want practice-oriented learning using PyTorch

  • Developers who want to start a Computer Vision or NLP project

Need to know before starting?

  • Basic Python Knowledge

  • Understanding of basic machine learning concepts (Recommended)

  • Willingness to follow the hands-on practice

Hello
This is kimw24072

CEO of Answernus - Instructor for 5 regular IT courses at Multicampus (RPA & ChatGPT & Crawling & AI & PE) - Instructor for 5 regular Generative AI courses at Korea Management Association (RPA & ChatGPT & Crawling & AI & Data Processing) - Author of [2022 Sejong Book Award Selection] "Money-Making Python Coding for Non-IT Majors" - Author of [2023 Sejong Book Award Selection] "Python Business Automation (RPA) for Non-IT Majors" - Operator of the "Bihyeonko Automation Lab" YouTube channel - Conducted lectures for numerous major corporations and public enterprises including Samsung, Hyundai, SK, KT, and LG - Cumulative 6,600+ learners in offline Generative AI education & 500+ hands-on project coaching cases [As of 2024.12] - IT Education Consultant & Instructor at Samsung Group Multicampus - AI Education Planning / Operations at Hyundai Steel HRD, Hyundai Motor Group - 12 years of professional experience as a non-developer at Hyundai Steel, Hyundai Motor Group (Sales, Planning, System Design, HRD, etc.)
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Curriculum

All

20 lectures ∙ (11hr 36min)

Course Materials:

Lecture resources
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