Deep Learning CNN Complete Guide - Fundamental Edition
From the core theory of deep learning and CNN to various CNN model implementation methods and practical deep learning development know-how through real-world problems, if you want to become a deep learning CNN technology expert, join this course :)
This course is prepared for Basic Learners.
What you will learn!
Deep Learning, Core Technology Elements of CNN
Important foundation framework that constitutes Tensorflow and Keras
Tuning know-how to improve the performance of CNN classification models
Implementing image classification using CNN
Various image augmentation techniques and techniques for improving model performance using them
Detailed mechanism of Keras ImageDataGenerator and Sequence
Image data preprocessing techniques for deep learning CNN
Core CNN models such as AlexNet, VGGNet, Inception, and ResNet
Applying the latest models such as Xception and EfficientNet
Understanding and Applying Fine Tuning Learning for Pretrained Models
Model performance improvement method using various learning rate scheduler techniques
Practical deep learning development methods such as image preprocessing, data processing, model creation, optimal performance improvement, and performance evaluation.
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 on 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 learning content and lecture materials below.
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 performance tuning of CNN models!
Maximize your practical skills by learning the AZ of implementing image classification models through various practical examples.
4. We provide detailed explanations of the core CNN models that have been 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.
You can download lecture materials and practice code 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'
People met by Infraon
Check out Kwon Chul-min's interview! | Go see
Recommended for
these people!
Who is this course right for?
Anyone who wants to greatly improve their basic skills in deep learning and CNN
Anyone who needs a solid understanding of CNN
Anyone who wants to use deep learning image classification models in the computer vision field
Anyone preparing for Kaggle or Deacon's image classification competition
Anyone interested in learning other deep learning
Need to know before starting?
Basic Python implementation skills and basic understanding of Numpy and Pandas are required.
You should have a basic understanding of machine learning (e.g. overfitting, why you need train/validation/test data sets, etc.).
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Curriculum
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135 lectures ∙ (31hr 39min)
are provided.