As I teach deep learning, I find that people from diversefields and majors are interested in learning deep learning. So, can we all start deep learning together? Unfortunately, no.
To get started with deep learning ,you need to start with basic knowledge and skills related to this field.
The most fundamental skill in deep learning is programming . And the programming language we need to learn is Python . Many people attempt to learn deep learning without a solid foundation in this fundamentals.
Scipy
Scikit-learn
OpenCV
TensorFlow
PyTorch
Many people begin deep learning without mastering the fundamentals of Python, simply learning the APIs provided by the libraries and frameworks mentioned above. This approach quickly runs into limitations when attempting to tackle real-world projects.
This course is designed to help you focus solely on deep learning without being swayed by the fundamentals.
How to learn programming
I understand that fundamentals are important, but how should I learn Python? To answer this question, I'll ask the following questions.
Do you memorize entire grammar books to become fluent in English conversation? Probably not many people do. Just because you know grammar doesn't mean you'll be fluent in English! Isn't the most effective way to learn English conversation to speak English frequently , even if it means using simple grammar?
Being fluent in English means being able to express your thoughts well in English , not just knowing English grammar. And Python is a language like English .
Memorizing a grammar book to learn Python is like memorizing an entire English grammar book and hoping to become fluent in English conversation. Just as learning English conversation is crucial, it's crucial to frequently converse with computers , and being able to convey your thoughts to them is essential for becoming a good programmer.
And computers are ready to talk to you even now. Through this lecture, you will have more conversations with computers than anyone else in the future.
Lecture Features
So what topics should we talk about with computers? We learn Python to learn deep learning . That means we need to have many conversations with computers about topics covered in deep learning . In fact, in the lecture, we will cover the following essential topics in deep learningalong with Python.Let's learn.
Throughout the course, you'll engage in numerous conversations using basic grammar to become proficient at communicating with computers. This is something I always say when teaching programming.
You will continue to practice repeatedlywith the essential knowledge you need to learn deep learning.
Through these exercises, by the end of the lecture, you will be able to
You will gain confidence in programming
You will have the amazing experience of the keyboard sticking to your hands.
You will become familiar with the basic items used in deep learning .
Deep learning, like any other specialized field , isn't something you can master in a short period of time . If you're new to deep learning and try to skim through it and achieve results with a project, you're likely to end up with a negative relationship with the subject.
Jumping into deep learning without a solid foundation and out of impatience is like running a marathon with shackles on your feet. What happens in the long run between someone who starts running with their shackles off and someone who starts running with shackles on their feet?
This course aims to guideyou in the "difficult but right direction" to become a deep learning expert .
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
1. It is structured so that you can build a solid foundation (basics of probability, vector, and matrix operations) with the central concept of 'codification of operations'.
2. The learning content is systematic and allows you to practice codes for the same concept repeatedly, which helps you master it a lot. I also felt that the teacher put a lot of thought into the structure in which the codes for the same mathematical concept gradually increase in difficulty, and I think it was very effective.
I plan to continue taking the teacher's lectures,
and I definitely want to participate in lectures or seminars myself.
Thank you!!
Although it has some very basic parts, it seems like a lecture that will help develop coding logic skills by only using basic logical coding methods without using specific function features.