Much of the deep learning development is implemented in Python, giving rise to various services and products. Do you know why Python is used for deep learning development?
This article introduces the reasons why Python is used for deep learning, what you can do with deep learning using Python, libraries, and recommended study methods.
Table of Contents
- 3 Reasons Why Python is Good for Deep Learning
- ① Extensive library
- ②Can call C and C++ programs
- ③ A lot of information
- Can deep learning be implemented in languages other than Python?
- What you can do with deep learning using Python
- Python sample code for deep learning
- What you can’t do with deep learning using Python
- 5 Recommended Development Environments
- PyCharm
- Atom
- Anaconda
- Visual Studio Code
- Jupyter Notebook
- 7 Python Libraries Suitable for Deep Learning
- Numpy
- Pandas
- Tensorflow
- Seaborn
- Matplotlib
- SciPy
- Scikit Learn
- Recommended study method
- Learn on programming learning sites
- Learn online
- Learn from books
- Summary
3 Reasons Why Python is Good for Deep Learning
It is said that Python is suitable for deep learning.
So why is Python suitable for deep learning? There are three reasons:
- Extensive library
- Can call C and C++ programs
- A lot of information
I will explain each.
① Extensive library
Python is heavily used for deep learning and there are libraries for deep learning rather than writing code from scratch.
- Open CV: A library for processing images and videos
- Numpy: a library for doing numerical calculations
- Chainer : Library for natural language processing and speech processing
- scikit-learn : artificial intelligence library
- Jupyter Notebook: a data analysis library
By using these libraries, you can implement it faster than developing in other languages.
②Can call C and C++ programs
Time reduction is important for programs that process data, and it is possible to write and call only the processing part in C or C++.
③ A lot of information
It is often used in deep learning, so when an error or something you don’t understand occurs, you can find a solution by investigating it.
Also, since many of the deep learning implementations overseas use Python, there is a lot of information available.
Can deep learning be implemented in languages other than Python?
Deep learning can be implemented in languages other than Python.
- JavaScript
- Swift
- Ruby
- PHP
- R.
- C, C++
- Julia
Machine learning and AI can be developed with the languages listed above. Inevitably, the ease of development, such as the number of libraries, is inferior to Python, but I don’t think that the whole system will be built only in Python, so let’s choose a suitable language for development.
What you can do with deep learning using Python
By using Python to implement deep learning,
- Image recognition
- Voice recognition
- Language processing
- Text generation from images
etc. will be possible. Since it is a language used to implement machine learning, it can do a wide range of things.
The link below explains deep learning, so please refer to it.
Python sample code for deep learning
As described above, you can do various things with deep learning using Python.
Now, for those who want to actually run the sample, I will introduce the site that introduces the sample code.
What you can’t do with deep learning using Python
There are things that even Python, which is highly versatile, cannot do.
Insufficient data for machine learning. No matter how sophisticated the code or system is, if there is a lack of data, it will be impossible to respond unexpectedly.
Also, abstract concepts are difficult to understand and do not work well. For more information, please see the link below if you are interested.
5 Recommended Development Environments
There are five development environments.
- PyCharm
- Atom
- Anaconda
- Visual Studio Code
- Jupyter Notebook
We will explain each below.
PyCharm
PyCharm is an integrated development environment for Python developed by JetBrains, a Czech software development company.
In addition to supporting various languages such as Japanese, it is also compatible with Windows and Mac, so it is one of the most used development environments around the world when using Python.
Atom
Atom is a development environment developed by GitHub, a subsidiary of Microsoft.
It is a development environment that excels in customizability using extended functions called full packages.
Only the minimum necessary functions are installed at the time of installation. Therefore, you can customize it as needed according to your purpose, so you can develop in a way that is easy for you without making your computer heavy.
Anaconda
Anaconda is a Python execution environment and a platform that provides various tools and libraries required for data science.
Over 250 data science and machine learning packages are automatically installed and ready for users to use immediately after installation.
Visual Studio Code
VisualStudioCode is a free editor provided by Microsoft.
It supports almost all languages, and you can smoothly code by using the extension function.
This editor is highly flexible and expandable, so your work efficiency will definitely increase as you use it.
Jupyter Notebook
Jupyter Notebook is a development environment that allows you to write and run Python on your browser.
The language that can be used is not only Python, but more than 40 languages are supported. Jupyter Notebook is supposed to be used for data analysis such as machine learning, so it is an environment suitable for tasks such as data visualization.
7 Python Libraries Suitable for Deep Learning
Now, I will introduce seven Python libraries that are essential for deep learning development.
- Numpy
- Pandas
- Tensorflow
- Seaborn
- Matplotlib
- SciPy
- Scikit Learn
We will introduce each of them below.
Numpy
Numpy is a library for numerical computation, and is good at numerical processing such as operations on vectors and matrices.
Since the contents of Numpy are implemented in machine languages such as C language and Fortran, it is possible to process even a large amount of data in a short time. Suitable for arithmetic.
Pandas
Pandas is a library for structuring data and helping with data analysis.
It is suitable for concatenating and dividing data, capturing statistical features such as the mean and standard deviation of data, and is often used in the cleaning stage such as processing outliers.
In addition, Pandas can display table data in an easy-to-read manner, and is used when dealing with numerical tables and time series data.
Tensorflow
TensorFlow is a library developed by Google for numerical analysis and neural networks. It supports major OSs and multiple programming languages, and its strength is its high versatility.
In addition, TensorFlow is characterized by its large number of users, and it has a wealth of learning materials such as books and articles on the Internet, making it a recommended library for those who want to try deep learning for the first time.
Seaborn
Seaborn is a library specialized for visualization of representative statistical data, and is based on Matplotlib. Create sophisticated graphs with less code.
Matplotlib
Matplotlib is a graph drawing library. By drawing complex data on a graph, you will be able to understand trends, deviations, and characteristics. The full range of graph types and display customization functions is a masterpiece.
Also, by using Matplotlib, you can create easy-to-read graphs. It is possible to customize not only the shape of the graph, but also the colors and characters.
SciPy
Scipy is also a representative numerical calculation library, and has a huge structure compared to Numpy, and is divided into subpackages according to functions and uses.
Since it covers a wide range of scientific and technical calculations, it is possible to handle statistical processing, linear algebra, calculus, and so on.
Scikit Learn
scikit-learn is a library that specializes in data analysis and data mining.
Many functions are available to perform complex preprocessing in batch, making it possible to easily perform technical processing such as standardization and regularization of data.
Recommended study method
To learn deep learning,
- Learn on programming learning sites
- Learn online
- Learn from books
You can learn programming using the methods above.
Let’s take a look at each.
Learn on programming learning sites
Many programming languages can be learned from free learning sites, so it is recommended for those who are worried about whether they can continue, those who do not want to spend the initial cost as much as possible, and those who do not know how to study. It’s a way of studying.
Progate
Progate is a free programming learning site used by over 1.4 million users (as of 2020).
You can learn many programming languages such as Python and Java, and you can learn as input in an illustrated and easy-to-understand slide format. After that, you can also learn how to write code to actually move your hands as output.
paizalearning
paiza learning is a learning site where you can learn without a learning environment.
It is a learning style of watching videos, and it is recommended for busy people because each video is about 3 minutes long.
Also, there is a skill check function that measures your coding ability, so you can understand the code more accurately.
Learn online
It is recommended for those who feel uneasy about studying on their own, or for those who want to study efficiently in a short period of time.
Udemy
Udemy is the world’s largest online learning platform. It has a good reputation for having a full range of paid courses taught by top-notch instructors. This is a popular service for working adults who find it difficult to secure a large amount of study time, as it allows them to study in their spare time using videos.
coursera
coursera is a service that allows you to take courses from universities all over the world for free. A particularly popular course is the “Machine Learning Course” by Professor Andrew Ng of Stanford University.
In this course, in 60 hours (11 weeks), it is possible to overview everything from the basics of machine learning to the mechanism of neural networks and their applications. The content is a little advanced, but you can thoroughly learn the theoretical mechanism of AI.
AiDemy
AiDemy provides AI and machine learning courses for individuals and corporations. Even if you are inexperienced with Python, you can learn data analysis using AI and implementation of web applications by continuing learning along the curriculum.
Learn from books
I want to learn Python more seriously. I want to know the structure and library of Python in detail. For those who say, I recommend studying with books.
Introduction to Python3 2nd Edition
Introductory Python 3 2nd Edition covers the basic Python syntax completely, and has a lot of content for applications such as science, art, and business, so you can use Python not only for grammar but also for work. It is a book.
Introduction to AI programming with Python
Introduction to AI programming with Python covers the knowledge necessary for AI programming using Python, making it an ideal introductory book for AI programming.
While dealing with various artificial intelligence related fields, it explains the basic usage of frequently used libraries in Python.
Python [Complete] Introduction
Python [Complete] Introduction covers a wide range of topics, from Python syntax to knowledge necessary for application development, and the content is organized in color for easy understanding, so it is a recommended book for those who are new to programming.
A clear introduction to Python
The easy-to- understand introduction to Python not only covers the basic knowledge of Python, but also introduces examples of failures in interactive sentences, so it is recommended for those who want to proceed with learning smoothly. .
Summary
What did you think.
In this article, we looked at what can be done with deep learning using Python and recommended libraries, based on the relationship between deep learning and Python.
In developing deep learning, the existence of Python is a major and indispensable programming language. Recognizing that Python is important for deep learning, let’s proceed with the study of deep learning.
