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[Deep Learning Expert Course DL1102] Python Level 2 for Deep Learning

Name: [Deep Learning Expert Course DL1102] Python Level 2 for Deep Learning
Price: 33000 KRW
Rating: 5 (11 reviews)

Expand on the basic grammar of Python and work on machine learning/deep learning projects.

(5.0) 11 reviews

223 learners

Level Beginner

Course period 12 months

asdfghjkl13551941

Python

Deep Learning(DL)

Anaconda

Python

Deep Learning(DL)

Anaconda

Reviews from Early Learners

5.0

alex.na

100% enrolled

I felt that the class was structured in a way that I could learn a lot, and the practical learning was good. Thank you.

5.0

endymion cheon

100% enrolled

It was a great lecture! I had some basic knowledge of grammar and numpy, but it was very helpful to learn various methods that can be used in relation to deep learning. In particular, I think I was able to understand it more deeply by implementing Data Generation, Convolutional Layer, K-Nearest Neighbor, and K-means Clustering myself. I plan to take follow-up lectures. Thank you for the great lecture!

5.0

lym930920

100% enrolled

Thank you so much, it's really helpful!

What you will gain after the course

Python Basics
Deep Learning Basics Items
How CNN related modules work
Problem solving skills

Solve mini-projects on your own and develop the implementation skills needed to learn deep learning!

Orientation video

[L4DL] Project Curriculum 📑

[Full screen link]

From Lv.1 to Lv.2

In this course, [Python for Deep Learning Level 1] , you'll expand on Python syntax and implement more complex concepts used in deep learning . Furthermore, through six mini-projects, you'll significantly improve your implementation skills, not just lectures.

6 Mini-pojects

Top-5 Accuracy
Edge Detection
Convolutional Layer
K-Nearest Neighbor Classification
K-means Clustering

Mini-projects aren't just about listening to programming lectures; they're designed to cultivate implementation skills . Instead, they first provide time to listen to a problem situation and then try to solve it on your own . Afterward, they receive an explanation and then review the problem.

Programming skills are determined by how well you can translate your thoughts into programs. Through these projects, you will be able to: Practice the implementation skills needed to learn deep learning .

Advanced Equations

In Level 2, you'll learn slightly more complex formulas than in Level 1. These formulas are actively used in deep learning .

Through this course, you will be able to greatly improve the following abilities:

Ability to understand formulas
Ability to implement formulas into programs

You can gain the following knowledge:

The operating principles of items you will learn in deep learning in the future
The Need for Vectorization

Assembling Building Blocks

If you break down any program into small modules, those small modules are made up of basic operations .

In mini-projects, we will combine the small modules we have learned so far to directly implement machine learning algorithms such as K-nearest neighbor classification and K-means clustering, as well as deep learning-related topics such as convolutional layers and edge detection .

Lecture Materials

All source code and brief explanations covered in this lecture are provided as Jupyter Notebook files.

Recommended for
these people

Who is this course right for?

For those who are new to deep learning
For those who are learning Python for the first time
People who lack program implementation skills
For those who want to start learning deep learning and Python together
Anyone who wants to join the deep learning specialized course

Need to know before starting?

[Python Level 1 for Deep Learning] Students

Hello
This is

3,608

Learners

165

Reviews

Answers

4.9

Rating

Courses

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Curriculum

All

60 lectures ∙ (42hr 28min)

Course Materials:

Lecture resources

Section 1. Introduction to the Course

1 lectures ∙ (13min)

1. Orientation
13:56

Section 2. Exercise Group.1: Using Jupyter Notebook

1 lectures ∙ (18min)

2. Exercise 1~3
18:09

Section 3. Exercise Group.2: Extending the For Loop

1 lectures ∙ (51min)

3. Exercise 4~6
51:36

Section 4. Exercise Group.3: Tuple and zip

2 lectures ∙ (48min)

Section 5. Exercise Group.4: Tuple Unpacking & zip practice

2 lectures ∙ (1hr 17min)

Section 6. Exercise Group.5: For Loop + enumerate

2 lectures ∙ (1hr 0min)

Section 7. Exercise Group.6: zip + enumerate

2 lectures ∙ (57min)

Section 8. Exercise Group.7: (Mini-project) Top-5 Accuracy

3 lectures ∙ (1hr 37min)

Section 9. Exercise Group.8: String Formatting

2 lectures ∙ (1hr 47min)

Section 10. Exercise Group.9: Dictionary

2 lectures ∙ (1hr 28min)

Section 11. Exercise Group.10: Dictionary Looping and Meta Data

2 lectures ∙ (1hr 35min)

Section 12. Exercise Group.11: (Mini-project) Mean Subtraction & Normalization

3 lectures ∙ (1hr 41min)

Section 13. Exercise Group.12: List Comprehension

2 lectures ∙ (1hr 5min)

Section 14. Exercise Group.13: Dictionary Comprehension

2 lectures ∙ (1hr 5min)

Section 15. Exercise Group.14: Importing Modules

2 lectures ∙ (1hr 23min)

Section 16. Exercise Group.15: Graphs of Transcendental Functions

2 lectures ∙ (1hr 20min)

Section 17. Exercise Group.16: Average / Max Pooling

3 lectures ∙ (1hr 44min)

Section 18. Exercise Group.17: Correlation / Convolution

2 lectures ∙ (1hr 40min)

Section 19. Exercise Group.18: AveragePooling2D / MaxPooling2D

2 lectures ∙ (1hr 52min)

Section 20. Exercise Group.19: Corr2D / Conv2D

2 lectures ∙ (1hr 47min)

Section 21. Exercise Group.20: (Mini-project) Edge Detection

3 lectures ∙ (1hr 15min)

Section 22. Exercise Group.21: Zero-padding

2 lectures ∙ (1hr 37min)

Section 23. Exercise Group.22: 2D Zero-padding

2 lectures ∙ (1hr 57min)

Section 24. Exercise Group.23: Strides

2 lectures ∙ (1hr 54min)

Section 25. Exercise Group.24: 2D Strides

2 lectures ∙ (2hr 7min)

Section 26. Exercise Group.25: (Mini-project) Convolutional Layer

3 lectures ∙ (1hr 55min)

Section 27. Exercise Group.26: (Mini-project) K-Nearest Neighbor

2 lectures ∙ (2hr 40min)

Section 28. Exercise Group.27: (Mini-project) K-means Clustering

2 lectures ∙ (3hr 18min)

Section 29. Outro to the Course

2 lectures ∙ (3min)

Published:

Last updated:

Reviews

All

11 reviews

5.0

11 reviews

alexna9046
Reviews 5
∙
Average Rating 5.0
5
100% enrolled
I felt that the class was structured in a way that I could learn a lot, and the practical learning was good. Thank you.
endymion
Reviews 14
∙
Average Rating 5.0
5
100% enrolled
It was a great lecture! I had some basic knowledge of grammar and numpy, but it was very helpful to learn various methods that can be used in relation to deep learning. In particular, I think I was able to understand it more deeply by implementing Data Generation, Convolutional Layer, K-Nearest Neighbor, and K-means Clustering myself. I plan to take follow-up lectures. Thank you for the great lecture!
lym9309201853
Reviews 8
∙
Average Rating 5.0
5
100% enrolled
Thank you so much, it's really helpful!
junyak2
Reviews 2
∙
Average Rating 5.0
5
100% enrolled
It's a really good lecture. I was able to improve my implementation skills a lot by implementing various algorithms with limited grammar.
airjoy2460
Reviews 18
∙
Average Rating 5.0
5
80% enrolled
Python feels more and more familiar to me.

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[Deep Learning Expert Course DL1102] Python Level 2 for Deep Learning

Reviews from Early Learners

5.0

What you will gain after the course

Orientation video

[L4DL] Project Curriculum 📑

From Lv.1 to Lv.2

6 Mini-pojects

Advanced Equations

Assembling Building Blocks

Lecture Materials

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Lecture History

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