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Deep Learning & Machine Learning

Complete mastery of deep learning theory + PyTorch practice

This course teaches the “core concepts” of deep learning required to work in the deep learning field and the practical skills required to perform actual deep learning projects through hands-on training using PyTorch.

(4.9) 50 reviews

445 learners

  • peterbyun969574
이론 실습 모두
개념정리
딥러닝기초
딥러닝모델
자연어처리
Deep Learning(DL)
PyTorch
Computer Vision(CV)
NLP
transformer

Reviews from Early Learners

What you will learn!

  • How Deep Learning Works

  • Core concepts of deep learning (loss function, gradient descent, automatic differentiation, etc.)

  • Creating Custom Models with PyTorch

  • Major models of deep learning (CNN, RNN, Transformer)

  • Hands-on training in computer vision

  • Practical training in natural language processing

Artificial intelligence (AI) field
Attention job seekers and new employees!

DSdMkV_WsAAHEnZ

AlphaGo

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AlphaFold

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Images generated with DALLE

3603ae9b7929-shutterstock2237752713-1

ChatGPT

Deep learning is demonstrating remarkable results across a wide range of fields, including computer vision, natural language processing, and biology. Representative examples include AlphaGo, AlphaFold, DALLE, and ChatGPT. Consequently, market demand for deep learning (DL)/machine learning (ML) engineers and scientists is rapidly increasing.

However, I believe that deep learning education services that teach the level of theory and practice required in the field are still lacking .

This course has a curriculum designed to provide in-depth study of the “core concepts and practices” of deep learning required to work in the deep learning field .

From core principles to practice
Deep learning in one go

✅ Learn the “core concepts” of deep learning required in the field of deep learning and strengthen your practical deep learning project skills through hands-on practice using PyTorch.

✅ Theories and concepts are explained in depth and as easily as possible through various visualizations, rather than superficially.

✅ Each section includes practical exercises linked to the theory, allowing you to intuitively understand how the theory is implemented and integrated into code .

To make up for the shortcomings of boot camps and major classes
Contains only practical content

There are many bootcamps and courses on deep learning, but I don't see any that offer in-depth, industry-relevant instruction. They mostly focus on traditional machine learning techniques like random forests and support vector machines. Even when it comes to deep learning, I don't think there's a place that offers a comprehensive, hands-on approach that covers both theory and practice. Therefore, even if you take these courses, your fundamental skills will likely remain weak, making it difficult to pass technical interviews or become a successful ML engineer.

For example, many deep learning courses and bootcamps teach gradient descent, but fail to explain automatic differentiation, which makes it possible to perform gradient descent efficiently. Since all deep learning frameworks (PyTorch, TensorFlow, etc.) are built on automatic differentiation, understanding automatic differentiation is considered essential to claiming a "deep learning understanding."

In contrast, this course aims to cover all the core deep learning concepts that many bootcamps and lectures miss .

And in this course, we will not stop at simply introducing the concepts, but will delve deeper into “why they are used,” “what they mean,” “in what context were they proposed,” “what effects do they have,” etc., and through various actual Toy Projects and practical exercises, you will learn how the theory is implemented and applied to code in connection with the theory.

For example, we cover the following:

  • What is the meaning of Cross Entropy Loss used in classification learning and its relationship with KL Divergence?

  • Why Initialization is Important and What Effects Does It Have?

  • Why is Batch Normalization used and what effect does it have?

  • What is the structure of Attention and Transformer, which are the basis of LLM, and how do they work?

  • How is Gradient Descent performed and implemented in PyTorch and Tensorflow?

Therefore, after taking this class, students will be able to acquire the understanding of deep learning theory and practical skills necessary to work in the deep learning field .

For reference, I have compiled this into a condensed version of only the essential information you need to know to work in the deep learning field .

With solid theory and diverse practice
A comprehensive overview of core deep learning concepts and models.

The course is divided into 16 sections . This course covers all the fundamentals and core concepts of deep learning (from gradient descent to attention) and covers representative deep learning models (fully connected neural networks, CNNs, recurrent neural networks, and transformers) . While in-depth, it avoids overly mathematical details. Instead , it breaks down the meaning of formulas in simple terms and provides easy-to-understand explanations through a variety of visualizations .

Each section consists of [Theory] lectures and [Practice] lectures .

[Theory Part]

Screenshot 2024-01-08 at 10.14.59 PM

  1. Explained as easily as possible with visualizations

Screenshot 2024-01-08 at 10.14.02 PM

  1. Covers all core concepts of deep learning and representative deep learning models.

Screenshot 2024-01-08 at 10.15.29 PM

  1. In-depth explanations to help you understand the meaning of the concept

[Practical Part]

Screenshot 2024-01-08 at 10.19.39 PM

  1. Explains in detail how the theory is implemented and grafted into code.

Screenshot 2024-01-08 at 10.20.11 PM

  1. Includes several "real-world" deep learning projects required for practical skills.

Screenshot 2024-01-08 at 10.20.49 PM

  1. Each section begins with a theoretical explanation followed by practical training.

Screenshot 2024-01-08 at 10.21.24 PM

  1. Hands-on training on PyTorch's core components (Dataset, Dataloader, optimizer, etc.)

Additionally, the CNN section and the RNN, Attention & Transformer section perform Toy Projects for Computer Vision and NLP, respectively.

I recommend this to these people

✅ Those preparing for employment or career change as a machine learning/deep learning engineer

✅ Those who wish to advance to AI graduate school

✅ Those who want to study machine learning/deep learning properly

After class

  • You will gain a thorough and in-depth understanding of how deep learning works .


  • You will understand the main models of deep learning and be able to apply them to real-world problems .


  • You will acquire the understanding of deep learning theory and practical skills necessary to work in the deep learning field .

  • You will be able to understand and utilize the core components of PyTorch (Dataset, Dataloader, Optimizer, etc.) .

  • You will be able to create custom deep learning models with PyTorch.


Learn about these things

Section (1) Setting up your environment for PyTorch practice

  • Set up the environment required for deep learning practice.

  • We will explain how to install and use PyTorch, a deep learning framework, VS Code, a programming IDE, and Google Colab, which can utilize GPU resources.

Screenshot 2024-01-10 at 5.46.27 AM

Section (2) What is Deep Learning?

  • Learn what deep learning is and what problems it aims to solve.

  • Learn the components and operating principles of Neural Networks.

  • You will learn the basic concepts related to deep learning practice and the basics of PyTorch.

Screenshot 2024-01-10 at 5.47.28 AM

Section (3) Loss Function

  • You will learn about loss functions, one of the core components of deep learning.

  • You will learn about the definition of loss functions, Regression and Classification tasks, and the types of losses used in each task.

Screenshot 2024-01-10 at 5.48.43 AM

Section (4) Advanced theory on loss functions

  • You will learn advanced theory about loss functions.

  • We will look at Cross Entropy Loss and KL Divergence Loss in more detail, and also learn about one-hot encoding and entropy concepts.

Screenshot 2024-01-10 at 5.49.28 AM

Section (5) Gradient Descent

  • It is a means to optimize the weight of a neural network and is the core of deep learning.

    You will learn about Gradient Descent.

  • You will learn about the basic concepts of Gradient Descent, the meaning of Gradient, the effect and role of Learning Rate, and Mini-batch Gradient Descents.

Screenshot 2024-01-10 at 5.50.17 AM

Section (6) Advanced theory of gradient descent

  • You will learn about the advanced theory of gradient descent.

  • How Gradient Descent is performed for multi-variate inputs and multiple neurons.

  • What is automatic differentiation, which deep learning frameworks are based on, and how does it work?

  • You will learn various advanced theories such as what gradient means and so on.

Screenshot 2024-01-10 at 5.50.53 AM

Section (7) Activation Function

  • Activation is one of the core components of Neural Network.

    You will learn about ation functions.

  • Learn what an Activation Function is and why you need it.

  • And you will learn about different types of Activation Functions and their characteristics.

Screenshot 2024-01-10 at 5.52.41 AM

Section (8) Optimization

  • Learn about various optimization methods that further develop the Mini-batch Gradient Descent method.

  • You will learn about the characteristics of major optimization methods and understand their evolutionary history.

Screenshot 2024-01-10 at 5.53.17 AM

Section (9) Creating a Fully Connected NN with PyTorch

  • In Section 9, we'll build a fully connected neural network with PyTorch and summarize the exercises we've learned so far.

Screenshot 2024-01-10 at 5.54.21 AM

Section (10) Regularization

  • In Section 10, we will learn about what overfitting is and one way to address it: regularization.

Screenshot 2024-01-10 at 5.54.51 AM

Section (11) Learning Rate Scheduler

  • Learn about the learning rate scheduler, a method to adjust the learning rate based on the learning progress and time step.

Screenshot 2024-01-10 at 5.55.29 AM

Section (12) Initialization

  • Learn how to initialize.

  • Why initialization is important, criteria for desirable initialization,

    You will learn how initialization affects model learning and which initialization methods are appropriate for each activation function.


  • You will learn about initialization methods using transfer learning.

Screenshot 2024-01-10 at 5.55.54 AM

Section (13) Normalization

  • You will learn about Normalization, which has become an "almost" essential component of Neural Networks.

  • Learn about different types of Normalization Layers.

  • We cover the Internal Covariate Shift problem, a common question in technical interviews, and the practical benefits of Batch Normalization.

Screenshot 2024-01-10 at 5.56.17 AM

Section (14) Convolutional Neural Network (CNN)

  • Learn about how CNN works, the various variants of CNN layers, and representative CNN models.

  • We will implement a CNN model and perform a computer vision project using CNN.

Screenshot 2024-01-10 at 5.56.52 AM

Section (15) Recurrent Neural Network (RNN)

  • Learn how RNN works,

  • You will learn how backpropagation in RNNs is actually performed and why the vanishing gradient problem occurs.

  • Learn about LSTM and GRU, which are representative RNN models.

  • I am working on an NLP project using RNN, GRU, and LSTM.

Screenshot 2024-01-10 at 5.57.19 AM

Section (16) Attention and Transformer

  • Attention and Transformer, which are the foundation of LLM

  • Learn about the basic concepts of Attention and the evolutionary history of Attention (BERT, Transformer).

  • Learn about the structure and operating principles of a transformer.

  • We are working on an NLP project using the BERT model.

Screenshot 2024-01-10 at 5.58.09 AM

Things to note before taking the course

Practice environment

  • Operating System and Version (OS): Windows, macOS

  • Tools used: Visual Studio Code, Google Colab

  • PC specifications: CPU 2 cores or higher, memory 8GB or higher, disk 32GB or higher

Player knowledge

  • Basics of Python

  • Basics of Numpy

  • High school math (differentiation) and English (for understanding deep learning terminology)

    • (Although not required) it would be helpful to know Linear Algebra and Probability.

    • We provide supplementary explanations for concepts or content that go beyond the high school math and English level.


Frequently Asked Questions

Can I follow the lectures even if I am not a major?

  • Since the lecture content is quite in-depth, there may be some concepts that you may not immediately understand.

  • However, I created the lecture with non-majors in mind, and I think you will understand it if you go through it carefully several times because the lecture explains all concepts beyond high school mathematics.

  • However, there may be parts that you do not fully understand. Q&A on the lecture content is also provided, so please feel free to ask questions at any time about any parts you do not understand or are confused about!

Can I follow the exercises even if I don't have a personal laptop?

  • Yes, it is possible!

  • You can write code in Google Colab notebooks and run the code on top of the Colab notebooks.

  • Google Colab notebooks already have the deep learning environments we need (pytorch, numpy, matplotlib, etc.).

  • And because the resources required to run code on a Colab notebook use the CPU and GPU of a Google remote server, not the user's local resources, you can sufficiently follow along with the exercises on an iPad rather than a desktop or laptop.

  • Google Colab also has a free version, so you can practice without any burden!

Recommended for
these people

Who is this course right for?

  • Preparing for a job or career change as a machine learning/deep learning engineer

  • AI graduate school admission goal

  • Anyone who wants to learn machine learning/deep learning properly

  • Those who want to solidify their theoretical and practical skills in deep learning

  • Those who have taken many deep learning courses and boot camps but were disappointed

  • Anyone preparing for an ML engineer technical interview

  • Non-majors preparing for employment as ML engineers

Need to know before starting?

  • High school level English and Math

  • Basic Python

  • Basic Numpy

Hello
This is

445

Learners

50

Reviews

34

Answers

4.9

Rating

1

Course

경력:

  • (현) ML Engineer @ MakinaRocks

  • (전) ML Engineer @ DearGen

  • (전) ML Engineer @ DeepBio

  • (전) Research Student @ UCL NLP Group, Streetbees

  • (전) Research Student @ ICL Photonics Lab

     

학력:

  • University College London (UCL): MSc in Machine Learning (머신러닝 석사) (학점: Distinction, GPA 4.0/4.0)

  • Imperial College London (ICL): BSc in Theoretical Physics (이론물리학 학사) (학점: First Class Honours, GPA 4.0/4.0)

소개:

5년차 Machine Learning Engineer입니다. (Google DeepMind가 출범하였고, Demis Hasabis가 박사과정을 한) University College London에서 머신러닝 석사를 전공하였습니다. 석사 때는 NLP에서 Knowledge Graph Embedding을 연구하였고, DeepBio에서는 Medical Diagnosis에 적용되는 Image Classification, Segmentation 딥러닝 모델들을 개발하였습니다. Deargen에서는 신약 개발의 Drug Target Interaction와 같은 문제 적용되는 GNN, RNN, Transformer 등등의 다양한 딥러닝 모델들을 적용한 경험이 있습니다. 현재 재직중인 MakinaRocks에서는 제조 현장의 로봇팔의 이상탐지에 적용되는 딥러닝 모델 및 머신러닝 시스템을 구축하고 있습니다.

Curriculum

All

143 lectures ∙ (13hr 48min)

Course Materials:

Lecture resources
Published: 
Last updated: 

Reviews

All

50 reviews

4.9

50 reviews

  • elizadukim9676님의 프로필 이미지
    elizadukim9676

    Reviews 1

    ∙

    Average Rating 5.0

    5

    31% enrolled

    Although this lecture is for beginners, it seems to be a good lecture for reviewing the main concepts for practitioners in their second or third year. Among the numerous theories and papers on deep learning, the important core concepts are organized in a well-organized manner, and the lectures are separated by subconcept, making it easy to find the content you need. It was also helpful for my work because it explained the concepts as well as the implementation in an easy-to-understand manner. I wish I had taken this lecture when I was a college student, but I regret why I only took this lecture now. I recommend it to those who want to grasp both concepts and implementation.

    • sguys996119님의 프로필 이미지
      sguys996119

      Reviews 3

      ∙

      Average Rating 5.0

      5

      24% enrolled

      It was a great help in reviewing the theory because you went into detail about concepts like loss function and optimizer that I didn't know much about and used. I'm looking forward to the intermediate course lecture!!

      • peterbyun969574
        Instructor

        Thank you for taking the class :) And I'm glad it was helpful! I'll prepare harder for the next class and open it!

    • seongwook님의 프로필 이미지
      seongwook

      Reviews 18

      ∙

      Average Rating 5.0

      5

      22% enrolled

      I am currently working as a backend developer. At first, I hesitated to take the course, but after taking it, I think I made a good choice. I have always been interested in ML engineers, so I took the course. Of course, my recent interest in artificial intelligence was also a decisive factor in taking the course. First of all, the vague concepts I learned in college were explained in an easy way, so I was able to understand them clearly. In particular, the formulas were explained easily during the theory lecture, so I was able to understand the formulas well. In particular, it seems to be the perfect course for people like me who only have a basic, superficial understanding of ML. For reference, I had a hard time with my thesis when I was in graduate school. If this course had been available at that time, it would have been very helpful. If you are interested in graduate school artificial intelligence or need conceptual content when writing a graduate school thesis, I think it would be very helpful to listen to it at least once.

      • peterbyun969574
        Instructor

        Hello! I was so frustrated with the fact that most deep learning bootcamps and lectures only cover the basics and only cover the surface of the watermelon, and I was also disappointed that many bootcamp graduates I interviewed only had a fragmentary understanding of deep learning. That's why I spent a lot of time and effort to create this lecture, and I'm so glad that it was helpful! Thank you for taking the course!

    • acelhj1123님의 프로필 이미지
      acelhj1123

      Reviews 1

      ∙

      Average Rating 5.0

      5

      30% enrolled

      This course covers a wide range of topics from the basics to advanced topics of deep learning, and I liked the hands-on approach using PyTorch. It covered a variety of topics, including setting up the PyTorch environment, basic concepts of deep learning, loss functions, gradient descent, activation functions, optimization, regularization, learning rate schedulers, initialization, standardization, CNNs (Convolutional Neural Networks), RNNs (Recurrent Neural Networks), and the latest topics such as Attention and Transformers. The course is designed to be easily accessible to beginners, and each section consists of various exercises along with theoretical explanations, so I especially liked the fact that learners can experience the principles of deep learning by writing code themselves. In particular, it is recommended not only for those who are new to the field of deep learning, but also for those who want to refresh their basic knowledge, as it allows them to learn step-by-step from basic concepts that can be applied immediately in practice to advanced topics. Each topic is covered in depth with ample practice and examples, which I believe will allow learners to comprehensively understand the various aspects of deep learning and develop the ability to apply them to solving real-world problems. The systematic organization of the lectures and the practice-oriented approach provide learners with the practical experience necessary to actually utilize deep learning technology, and I highly recommend this course to anyone interested in the field of deep learning.

      • peterbyun969574
        Instructor

        Thank you for taking the class and writing such a detailed review ㅠㅠ I put a lot of thought into organizing the curriculum so that it covers a wide range of topics, but also explains them in depth and in an easy-to-understand manner, and allows you to gain practical experience through hands-on practice. I'm so glad that it was helpful! Thank you :)

    • kyuyeonpooh9631님의 프로필 이미지
      kyuyeonpooh9631

      Reviews 1

      ∙

      Average Rating 5.0

      5

      31% enrolled

      This lecture can be helpful for both those who are new to the field of deep learning and those who want to review important concepts. For those who are new to the field, the lecture is well organized from the bottom up so that they can follow the flow by following the table of contents, and for those who are already in the field, it seems that they can quickly review the concepts that I was weak in. The lecture table of contents and internal structure seem to have captured the essential elements without unnecessary details. The structure and content are very clean. In addition, the lecture is well organized with content that would be of interest to those in the field. For example, - So what kind of logic is used for the internal operation? - So how do you implement it? I felt that these two were well differentiated. In fact, it is well-incorporated with empirical content that can be learned from the perspective of work performance, not just from the perspective of an instructor.

      • peterbyun969574
        Instructor

        Thank you for taking the course~ Thank you for leaving such a detailed review! I put a lot of thought and effort into making the course so that students can understand all the key concepts they need to know, and explain them as easily as possible. I am so grateful and grateful that you found out about it. ㅠㅠ Thank you for the review!

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    AI is not just a simple technology, but was born from people's questions and the flow of the times. Understanding this flow allows you to connect with any new technology without fear. 👉 "The moment you learn AI's roots through stories, you'll see the context." Start now!

    입문

    입문

    Machine Learning(ML), Deep Learning(DL), Big Data

    From deep learning to the latest AI, see the flow of technology, data, and key figures at a glance!강의 썸네일

    From deep learning to the latest AI, see the flow of technology, data, and key figures at a glance!

    aidercollege

    [7-Day Complete] Pass MS AI-900 Certification in One Go강의 썸네일

    [7-Day Complete] Pass MS AI-900 Certification in One Go

    jobgreegi

    Essential certification for the AI era, MS AI-900! 🚀 Did you know? ChatGPT operates exclusively on Microsoft Azure! If you want to properly utilize Microsoft Azure OpenAI, which exclusively provides the latest GPT models, now is the perfect opportunity. Grow into an Azure AI services expert through the AI-900 course and master enterprise-grade AI solutions that companies trust. Secure global official certification and enhance your competitiveness as a true AI professional!

    입문

    입문

    Machine Learning(ML), Deep Learning(DL), AI

    [7-Day Complete] Pass MS AI-900 Certification in One Go강의 썸네일

    [7-Day Complete] Pass MS AI-900 Certification in One Go

    jobgreegi

    Understanding the Fundamental Principles of Large Language Models (LLMs)강의 썸네일

    Understanding the Fundamental Principles of Large Language Models (LLMs)

    arigaram

    Explains the basic principles of large language models like ChatGPT, focusing on theory.

    중급이상

    중급이상

    NLP, gpt, AI

    Understanding the Fundamental Principles of Large Language Models (LLMs)강의 썸네일

    Understanding the Fundamental Principles of Large Language Models (LLMs)

    arigaram

    Learn LLM and GPT Basics in 1 Hour강의 썸네일

    Learn LLM and GPT Basics in 1 Hour

    Essential

    This course explains the basic concepts of LLM and GPT in an easy-to-understand way for everyone. Students can directly use the GPT API to create chatbots and run them on the web using Streamlit. By experiencing everything from basics to hands-on practice, you can solidly establish your first steps in AI utilization.

    초급

    초급

    Python, AI, ChatGPT

    Learn LLM and GPT Basics in 1 Hour강의 썸네일

    Learn LLM and GPT Basics in 1 Hour

    Essential

    Practical Docker: Creating your own deep learning cloud computer with Docker강의 썸네일

    Practical Docker: Creating your own deep learning cloud computer with Docker

    danielyouk

    You can create a deep learning analysis environment in the cloud using Docker. When Docker images managed by Google, MS, etc. are combined with the cloud, you can work with the latest deep learning analysis methods on your computer.

    초급

    초급

    Docker, Virtualization, Python

    Practical Docker: Creating your own deep learning cloud computer with Docker강의 썸네일

    Practical Docker: Creating your own deep learning cloud computer with Docker

    danielyouk

    Looking at the birth and development process of artificial intelligence reveals the principles of AI.강의 썸네일

    Looking at the birth and development process of artificial intelligence reveals the principles of AI.

    aidercollege

    AI is not a technology that suddenly appeared. To properly see AI's present and future, you must understand 70 years of challenges and failures. 👉 "The moment you understand AI history, you can see both its principles and future together." Start right now!

    입문

    입문

    Machine Learning(ML), Deep Learning(DL), Reinforcement Learning(RL)

    Looking at the birth and development process of artificial intelligence reveals the principles of AI.강의 썸네일

    Looking at the birth and development process of artificial intelligence reveals the principles of AI.

    aidercollege

    Artificial Intelligence (AI) - Learning Runway AI that Creates New Videos and Moving Images강의 썸네일

    Artificial Intelligence (AI) - Learning Runway AI that Creates New Videos and Moving Images

    usefulit

    This course covers everything from the basics to hands-on practice of AI platforms, and is a program where you can learn AI image generation technology. Learners can understand the fundamental concepts of AI and develop the ability to generate AI images using various tools and technologies. Through this course, you can enhance your understanding of the AI field and learn how to directly generate AI images in real-world applications.

    입문

    입문

    AI, Generative AI, Deep Learning(DL)

    Artificial Intelligence (AI) - Learning Runway AI that Creates New Videos and Moving Images강의 썸네일

    Artificial Intelligence (AI) - Learning Runway AI that Creates New Videos and Moving Images

    usefulit

    Artificial Intelligence (AI) - Learning Tensor Art, Which Creates Free AI Images강의 썸네일

    Artificial Intelligence (AI) - Learning Tensor Art, Which Creates Free AI Images

    usefulit

    This course is an introductory program designed so that learners who are new to AI technology can easily understand and follow along. Through the lectures, you will learn the basic concepts and usage methods of AI platforms, and conduct hands-on practice directly generating images using various AI tools. Upon completing this course, you will become much closer to AI and acquire the capability to create AI images that can visually implement creative ideas.

    입문

    입문

    AI, Generative AI, Machine Learning(ML)

    Artificial Intelligence (AI) - Learning Tensor Art, Which Creates Free AI Images강의 썸네일

    Artificial Intelligence (AI) - Learning Tensor Art, Which Creates Free AI Images

    usefulit