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A Complete Guide to Deep Learning CNN - TensorFlow Keras Version

Name: A Complete Guide to Deep Learning CNN - TensorFlow Keras Version
Price: 110000 KRW
Rating: 4.9 (118 reviews)

From core theories of Deep Learning and CNN to implementation methods of various CNN models, and practical Deep Learning development know-how through real-world problems, If you want to become a Deep Learning CNN technology expert, join us in this lecture :)

(4.9) 118 reviews

2,107 learners

dooleyz3525

Deep Learning(DL)

CNN

Tensorflow

Keras

Kaggle

Reviews from Early Learners

What you will gain after the course

Core technological components of deep learning and CNNs
Important foundational frameworks comprising TensorFlow and Keras
Tuning Know-How to Improve the Performance of CNN Classification Models
Implementing Image Classification Using CNNs
Various image augmentation techniques and methods to improve model performance using them.
Detailed mechanisms of Keras ImageDataGenerator and Sequence
Image Data Preprocessing Techniques for Deep Learning CNNs
Key CNN models such as AlexNet, VGGNet, Inception, and ResNet.
Applied cutting-edge models such as Xception and EfficientNet.
Understanding and Applying Fine-Tuning of Pretrained Models
Ways to Improve Model Performance Using Various Learning Rate Scheduler Techniques
Practical Deep Learning Development Methods: Image Preprocessing, Data Processing, Model Creation, Optimal Performance Improvement, Performance Evaluation, etc.

Why I created this course 😚

The fastest-growing field of deep learning, CNN

Among the fields of deep learning utilization, the computer vision field based on deep learning CNN is the one that is growing the most rapidly and also changing the fastest. Therefore, in order to grow as an expert in the field of deep learning-based computer vision, it has become essential to have practical implementation skills and core competencies for CNN. To this end, we have released the course ' Deep Learning CNN Complete Guide - Fundamental Edition' . And we plan to release ' Deep Learning CNN Complete Guide - Advance Edition' with more advanced topics in the future.

What you will learn in this lecture

This time, the 'Deep Learning CNN Complete Guide - Fundamental' edition provides in-depth theory and practice of the core technology elements of deep learning and CNN, as well as various implementation techniques for building CNN image classification models and model performance optimization methods. In addition, by following many practical examples, you will be able to acquire image preprocessing, data loading, understanding the tf.keras framework, the internal architecture of the latest CNN model, and model performance tuning methods that can be used in real-world situations, and will help you grow into a deep learning CNN technology expert.

Deep Learning CNN Lecture, Ends with This Lecture.

Through 130 lectures and 30 hours of lectures, we cover in depth everything you need to understand CNN.
Check out some of the content and lecture materials below.

권 철민, 딥러닝 CNN 완벽 가이드

Check out the lecture materials in advance 🙂

Features of this course

1. In-depth theory and practice on the core technology elements that make up deep learning and CNN.

We will install the core fundamental knowledge of deep learning and CNN in your head through in-depth theory and practice.

2. Understanding the core framework that constitutes Tensorflow.Keras

We will help you implement more flexible and scalable powerful Keras-based CNN applications through detailed explanations and hands-on practice of the core framework that makes up Tensorflow.Keras.

3. From image preprocessing to optimal CNN model performance tuning!
Maximize your practical skills by learning the AZ of implementing image classification models through various practical examples.

In order to grow into a deep learning-based computer vision expert, you must also have basic technology for image processing. We will explain in detail the image processing-based technology for implementing deep learning image discrimination models, such as image preprocessing methods, image arrangement and characteristics, image library utilization, and augmentation techniques using dedicated tools such as Albumentations.

You will learn the ability to freely implement CNN image classification models through various data sets and difficult practical problems, as well as optimal performance tuning techniques for image classification models using the latest models such as Augmentation, Learning Rate Optimization, and EfficientNet .

4. We provide detailed explanations of the core CNN models that have become an important foundation for the development of modern CNNs at the source code level.

In order to use CNN for applications that extend beyond image classification models, it is important to understand how modern CNN models have evolved and what their core technologies are. To this end , we will explain in detail the architecture and characteristics of key CNN models such as AlexNet, VGGNet, GoogLeNet (Inception), and ResNet, as well as the implementation of these models at the source code level.

Guide to the Practice Environment

The practice environment is performed using the notebook kernel provided by Kaggle. After signing up for Kaggle, you can use the Jupyter Notebook environment similar to Colab by selecting the Code menu.

Kaggle Notebook kernel provides GPU P-100 VM for free. It also has a beautiful UI environment and can be easily linked with various data of Kaggle, so you can practice very conveniently. The practice code is written based on tf.keras of Tensorflow 2.4. For more detailed description of the practice environment, please refer to the introduction video of the practice environment in Session 0.

Lecture materials and practice code can be downloaded from 'Section 0: Lecture Introduction' and 'Download Lecture Materials and Practice Code' in Introduction to Practice Environment.

Because I know how valuable your efforts are.

You can't become an expert in any field without effort. No, if you become an expert without effort, you are not an expert. Because I know that you want to become an expert in the field of deep learning, and I know the value of the effort you put into it, I have put my heart and soul into creating a complete guide to deep learning CNN so that even a little time you invest in studying deep learning will not be wasted.

This course will serve as a valuable stepping stone for you to grow into a deep learning expert.

A lecture that will be helpful if you learn it in advance ✨

Kwon Chul-min, a knowledge sharer, lecture series on 'Machine Learning'

1 Complete Guide to Python Machine Learning

People met by Inflearn

Check out Kwon Chul-min's interview! | Go see

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Who is this course right for?

For those looking to significantly enhance their basic competencies in deep learning and CNNs
For those who need a solid understanding of CNNs
For those who want to use deep learning image classification models in the field of computer vision
Those preparing for Kaggle or Dacon image classification competitions.
Anyone interested in other deep learning studies

Need to know before starting?

Basic proficiency in Python and a foundational understanding of Numpy and Pandas are required.
You should have a basic understanding of machine learning. (e.g., overfitting or why you need training/validation/test datasets)

Hello
This is

26,934

Learners

1,367

Reviews

4,010

Answers

4.9

Rating

Courses

(전) 엔코아 컨설팅

(전) 한국 오라클

AI 프리랜서 컨설턴트

파이썬 머신러닝 완벽 가이드 저자

Curriculum

All

135 lectures ∙ (31hr 39min)

Course Materials:

Lecture resources

Section 1. Course Introduction and Setup

3 lectures ∙ (18min)

Section 2. Deep Learning Basics - Overview and Gradient Descent

13 lectures ∙ (3hr 11min)

Section 3. Understanding Deep Learning - Error Backpropagation, Activation Function, Loss Function, Optimizer

11 lectures ∙ (2hr 53min)

17. Understanding Deep Neural Networks and Backpropagation Overview
18:27
18. Understanding Backpropagation - The Chain Rule of Differentiation
23:31
19. Deep Neural Network applying chained function composition
06:00
20. Gradient Application Mechanism of Backpropagation - 01
15:10
21. Gradient Application Mechanism of Error Backpropagation (Backpropagation) - 02
10:13
22. Understanding Activation Function
20:24
23. Reviewing Deep Learning Model Learning Mechanism in Tensorflow Playground
10:02
24. Understanding Loss Functions and Cross Entropy Details - 01
16:40
25. Understanding Loss Functions and Cross Entropy Details - 02
16:34
26. Understanding Optimizers - Momentum, AdaGrad
21:01
27. Understanding Optimizers - RMSProp, Adam
15:17

Section 4. Keras Framework

15 lectures ∙ (3hr 18min)

Section 5. Understanding CNN

12 lectures ∙ (2hr 40min)

Section 6. Implementing CNN Models and Applying Basic Performance Improvement Techniques

13 lectures ∙ (2hr 49min)

Section 7. Understanding Data Augmentation - Using Keras ImageDataGenerator

7 lectures ∙ (1hr 58min)

Section 8. Using Pre-trained CNN Models and Understanding the Keras Generator Mechanism

10 lectures ∙ (2hr 30min)

Section 9. Augmentation using Albumentation and Keras Sequence

10 lectures ∙ (2hr 33min)

Section 10. Dissecting Advanced CNN Models - AlexNet, VGGNet, GoogLeNet

12 lectures ∙ (2hr 28min)

Section 11. Breaking Down Advanced CNN Models - ResNet Details and EfficientNet Overview

8 lectures ∙ (1hr 54min)

Section 12. Fine-tuning of Pre-trained Models and Application of Various Learning Rate Schedulers

7 lectures ∙ (1hr 30min)

Section 13. Comprehensive Practice 1 - 120 Dog Breed ID Model Optimization

8 lectures ∙ (2hr 11min)

Section 14. Comprehensive Practice 2 - Kaggle Plant Pathology (Plant leaf disease diagnosis) Competition

6 lectures ∙ (1hr 22min)

Published:

Last updated:

Reviews

All

118 reviews

4.9

118 reviews

kbj804941680
Reviews 2
∙
Average Rating 5.0
5
17% enrolled
I haven't seen everything, but here's a review of the course first.. [Advantages] 1. It organizes the prerequisite knowledge well at the beginning 2. The lecture name is CNN, but it is not limited to CNN, and it explains the basics of deep learning (SGD, Backprop, etc.) in detail, so it is easy to understand even if there are some difficult applications later 3. The image preprocessing is also detailed, so even those without basic vision skills can try it (I also recommend listening to Professor Kwon Cheol-min's vision lecture) 4. It is not just a simple CNN image classification, but also a detailed explanation of how CNN has developed recently 5. There are many pictures in the lecture materials for easy understanding [Disappointing points] 1. It is a TF-based lecture, but torch is also...ㅎㅎ [Overall] 5 points. Those who are new to deep learning in the image field should definitely listen to it, and those who are just new to deep learning should also listen to it because the basics of deep learning are explained in detail. CNN itself is honestly being used not only for images these days, but also for NLP and predictive modeling, so it is good to understand CNN deeply and utilize it.
dohoonkim
Reviews 1
∙
Average Rating 5.0
5
49% enrolled
Hello, I am currently studying artificial intelligence as a major. I will not lie, but the lectures are conducted according to the university level curriculum.... The difficulty level is about 90% of the university level(?). It is not as deep as a master's degree, but this lecture definitely covers everything from beginner to intermediate level. And apart from the difficulty level, school classes end with just reading PPTs, but this lecture was so good because I could follow the code line by line. Sometimes, in some areas, he explained at a level that made me think, 'Huh?! They go into this much depth!?' I was surprised. (I was surprised because the points and explanations that the professor made were almost the same.) The regrettable thing is that the parts that are the pinnacle of modern machine learning, such as transformers and attention, are not covered yet. If this lecture comes out, I think Professor Kwon Chul-min will almost become the king of domestic machine learning private education. It is the most satisfying lecture among the lectures that I paid for at Inflearn.
yoonducklim0908
Reviews 7
∙
Average Rating 5.0
5
50% enrolled
I thought I had a general understanding of deep learning while working on the project, but this lecture made me realize that I had been doing it by the rule of thumb. I keep listening to it whenever I have free time during the day. It is very helpful, and if an advanced course comes out later, I would like to take that lecture as well.
poptato1037
Reviews 1
∙
Average Rating 5.0
5
75% enrolled
I've watched about half of it now.. When will the advanced lecture come out? I feel dizzy, so please make it before I listen to it 100%. I want to listen to it quickly.
hdongle5478
Reviews 2
∙
Average Rating 5.0
5
96% enrolled
CNN is a relatively new field if you have done a little bit of deep learning, so you may think you know it, but it is harder to respond flexibly than you think because you don't know the principles and use it. Whenever the company needs it, I search for github or quickly combine the functions I need at the time without knowing the principles. While doing it while doing other work, I kept putting off studying tensorflow2.4, keras, and kaggle, thinking that I would study them someday. However, I remember successfully applying object detection to robot movement in a project I took the last lecture, and being recognized as an expert(?) at the company, so I am taking the class to organize CNN again and look into it in depth. The class time is not short, but it is not bad because the source code is explained in detail, so I think it would be good to quickly look through it and mark it separately and listen to the parts I need in more detail later. --------------------------------------------------------- I am adding this to the review I left before. I did other work at the company, and then the computer vision department was created again, so I listened to it from the beginning again to review it. When I listened to it again, I realized that I had a wider range of understanding of the parts that I had rushed through before. This is not a lecture that you can listen to once and finish. I highly recommend it again.

$84.70

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A Complete Guide to Deep Learning CNN - TensorFlow Keras Version

Reviews from Early Learners

What you will gain after the course

Why I created this course 😚

The fastest-growing field of deep learning, CNN

What you will learn in this lecture

Deep Learning CNN Lecture, Ends with This Lecture.

Features of this course

Guide to the Practice Environment

Because I know how valuable your efforts are.

Kwon Chul-min, a knowledge sharer, lecture series on 'Machine Learning'

People met by Inflearn

Recommended for these people

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Curriculum

Reviews

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