What is the difference between using Matlab and Python in the field of deep learning?
Advantages of Matlab
MatLab has a large number of committed users which include many universities and a few companies who have the budget to buy a license for the program. Even though it is used in many universities, Matlab is easy for beginners who are just starting to learn about programming language because the package, when purchased, includes all that you will need.
When using Python you are required to install extra packages. One part of MatLab is a product called Simulink, which is a core part of the MatLab package for which there does not yet exist a good alternative in other programming languages.
Disadvantages of Matlab
Disadvantage is its cost of License. Its very costly user has to buy each and every module and pay for it. Disadvantage is during cross compiling or converting Matlab to other language code is very difficult. Its very difficult or requires deep devel Matlab knowledge to deal with all errors.
Matlab is not suggested to make any product. Because, Matlab doesn’t create application deployment like task (like setup files and other executable which copies during installation).
Advantages of Python
The Python language has diversified application in the software development companies such as in gaming, web frameworks and applications, language development, prototyping, graphic design applications, etc.
– User Friendly and Easy to learn – Cross platform supported – Vast community support – Very powerful – Open source
– Python Packages Index ( PyPI ) – hosts thousands of third-party modules for python.
Applications
- Web and Internet Development
- Database Access
- Desktops GUIs
- Scientific and Numeric
- Education
- Network Programming
- Software and Game Development
This provides the language a higher plethora over other programming languages used in the industry. Some of its advantages in details are-
Extensive Support Libraries It provides large standard libraries that include the areas like string operations, Internet, web service tools, operating system interfaces and protocols. Most of the highly used programming tasks are already scripted into it that limits the length of the codes to be written in Python.
Integration Feature Python integrates the Enterprise Application Integration that makes it easy to develop Web services by invoking COM or CORBA components. It has powerful control capabilities as it calls directly through C, C++ or Java via Jython. Python also processes XML and other markup languages as it can run on all modern operating systems through same byte code.
Improved Programmer’s Productivity The language has extensive support libraries and clean object-oriented designs that increase two to ten fold of programmer’s productivity while using the languages like Java, VB, Perl, C, C++ and C#.
Productivity With its strong process integration features, unit testing framework and enhanced control capabilities contribute towards the increased speed for most applications and productivity of applications. It is a great option for building scalable multi-protocol network applications.
Disadvantages of Python
Python has varied advantageous features, and programmers prefer this language to other programming languages because it is easy to learn and code too.
However, this language has still not made its place in some computing arenas that includes Enterprise Development Shops. Therefore, this language may not solve some of the enterprise solutions, and limitations include-
Difficulty in Using Other Languages The Python lovers become so accustomed to its features and its extensive libraries, so they face problem in learning or working on other programming languages. Python experts may see the declaring of cast “values” or variable “types”, syntactic requirements of adding curly braces or semi colons as an onerous task.
Weak in Mobile Computing Python has made its presence on many desktop and server platforms, but it is seen as a weak language for mobile computing. This is the reason very few mobile applications are built in it like Carbonnelle.
Gets Slow in Speed Python executes with the help of an interpreter instead of the compiler, which causes it to slow down because compilation and execution help it to work normally. On the other hand, it can be seen that it is fast for many web applications too.
Run-time Errors The Python language is dynamically typed so it has many design restrictions that are reported by some Python developers. It is even seen that it requires more testing time, and the errors show up when the applications are finally run.
Underdeveloped Database Access Layers As compared to the popular technologies like JDBC and ODBC, the Python’s database access layer is found to be bit underdeveloped and primitive. However, it cannot be applied in the enterprises that need smooth interaction of complex legacy data.
Let’s consist a small combination of them – following can be incredibly useful –
MATLAB
– Invaluable for signal processing – Incredibly broad array of useful libraries – Simplest and most concise language for anything involving matrix operations – Works very well for anything that is simply represented as a numeric feature matrix – Huge pain to use for anything that isn’t simply represented as a numeric feature matrix – Lacking a good open source ecosyste
Python
– Very fragmented but comprehensive scientific computing stack – Pandas, scikit.learn, numpy, scipy, ipython, & matplotlib are my most-used scientific computing libraries – IPython notebook makes a nice interactive data analysis tool – All the benefits of a general purpose programming language – Unfortunately slow if you don’t drop into C – Some of the scientific computing stack is still stuck in Python 2.7 – Very good for problems that don’t come as a simple feature matrix, between tools like pandas and nltk – Incredible open source ecosystem
Python is most popular language in the AI field.
Why ? Because –
Python comes with a huge amount of libraries. Many of the libraries are for Artificial Intelligence and Machine Learning. Some of the libraries are Tensorflow (which is high-level neural network library), scikit-learn (for data mining, data analysis and machine learning), pylearn2 (more flexible than scikit-learn), etc. The list keeps going and never ends.
For other languages, students and researchers need to get to know the language before getting into ML or AI with that language. This is not the case with python. Even a programmer with vert basic knowledge can easily handle python.
Apart from that, the time someone spends on writing and debugging code in python is way less when compared to C, C++ or Java. This is exactly the students of AI and ML wants. They don’t want to spend time on debugging the code for syntax errors, they want to spend more time on their algorithms and heuristics related to AI and ML. Not just the libraries but their tutorials, handling of interfaces are easily available online. People build their own libraries and upload them on GitHub or elsewhere to be used by others
Python has a solid claim to being the fastest growing major programming language. Recommended to check ground breaking statistics on incredible growth of python and why is python growing so quickly from stack overflow.
Advantages of Python over Matlab
1. Python code is more compact and easier to read than Matlab code —- Unlike Matlab, which uses end statement to indicate the end of a block, Python determines block size based on indentation. —- Python uses square brackets for indexing and parentheses for functions and methods, whereas Matlab uses parentheses for both, making Matlab more difficult to differentiate and understand. —- Python’s better readability leads to fewer bugs and faster debugging.
2. While most programming languages, including Python, use zero-based indexing, Matlab uses one-based indexing making it more confusing for users to translate.
3. The object-oriented programming (OOP) in Python is simple flexibility while Matlab’s OOP scheme is complex and confusing
4. Python is free and open —- While Python is open source programming, much of Matlab is closed —- The developers of Python encourage users to input suggestions for the software, while the developers of Matlab offer no such interaction
5. There is no Matlab counterpart to Python’s import statement 6. Python offers a wider set of choices in graphics package and toolsets
In Steve Hanly’s research on the speed test between Python and MATLAB for vibration analysis
Utilization of Python
Python has been gaining momentum as being the programming language for novice users. Highly ranked Computer Science departments at MIT and UC Berkeley use Python to teach their novice programming language students. The three largest Massive Open Online Course (MOOC) providers (edX, Coursera andUdacity) all use Python as their programming language for their beginning courses in programming. A variety of professors in other disciplines now utilize the need for novice students to understand Python and its key features.
Conclusion
There is no such thing as a ‘best language for machine learning’
Popularity is not a good yardstick to use when selecting a programming language for machine learning and data science. There is no such thing as a ‘best language for machine learning’ and it all depends on what you want to build, where you’re coming from and why you got involved in machine learning.
In most cases developers port the language they were already using into machine learning, especially if they are to use it in projects adjacent to their previous work?—?such as engineering projects for C/C++ developers or web visualizations for JavaScript developers.
Reference: https://www.codementor.io/@innat_2k14/preferable-tools-for-machine-learning-python-matlab-r-jfozzpphz
Advantages of Matlab
MatLab has a large number of committed users which include many universities and a few companies who have the budget to buy a license for the program. Even though it is used in many universities, Matlab is easy for beginners who are just starting to learn about programming language because the package, when purchased, includes all that you will need.
When using Python you are required to install extra packages. One part of MatLab is a product called Simulink, which is a core part of the MatLab package for which there does not yet exist a good alternative in other programming languages.
Disadvantages of Matlab
Disadvantage is its cost of License. Its very costly user has to buy each and every module and pay for it. Disadvantage is during cross compiling or converting Matlab to other language code is very difficult. Its very difficult or requires deep devel Matlab knowledge to deal with all errors.
Matlab is not suggested to make any product. Because, Matlab doesn’t create application deployment like task (like setup files and other executable which copies during installation).
Advantages of Python
The Python language has diversified application in the software development companies such as in gaming, web frameworks and applications, language development, prototyping, graphic design applications, etc.
– User Friendly and Easy to learn – Cross platform supported – Vast community support – Very powerful – Open source
– Python Packages Index ( PyPI ) – hosts thousands of third-party modules for python.
Applications
- Web and Internet Development
- Database Access
- Desktops GUIs
- Scientific and Numeric
- Education
- Network Programming
- Software and Game Development
This provides the language a higher plethora over other programming languages used in the industry. Some of its advantages in details are-
Extensive Support Libraries It provides large standard libraries that include the areas like string operations, Internet, web service tools, operating system interfaces and protocols. Most of the highly used programming tasks are already scripted into it that limits the length of the codes to be written in Python.
Integration Feature Python integrates the Enterprise Application Integration that makes it easy to develop Web services by invoking COM or CORBA components. It has powerful control capabilities as it calls directly through C, C++ or Java via Jython. Python also processes XML and other markup languages as it can run on all modern operating systems through same byte code.
Improved Programmer’s Productivity The language has extensive support libraries and clean object-oriented designs that increase two to ten fold of programmer’s productivity while using the languages like Java, VB, Perl, C, C++ and C#.
Productivity With its strong process integration features, unit testing framework and enhanced control capabilities contribute towards the increased speed for most applications and productivity of applications. It is a great option for building scalable multi-protocol network applications.
Disadvantages of Python
Python has varied advantageous features, and programmers prefer this language to other programming languages because it is easy to learn and code too.
However, this language has still not made its place in some computing arenas that includes Enterprise Development Shops. Therefore, this language may not solve some of the enterprise solutions, and limitations include-
Difficulty in Using Other Languages The Python lovers become so accustomed to its features and its extensive libraries, so they face problem in learning or working on other programming languages. Python experts may see the declaring of cast “values” or variable “types”, syntactic requirements of adding curly braces or semi colons as an onerous task.
Weak in Mobile Computing Python has made its presence on many desktop and server platforms, but it is seen as a weak language for mobile computing. This is the reason very few mobile applications are built in it like Carbonnelle.
Gets Slow in Speed Python executes with the help of an interpreter instead of the compiler, which causes it to slow down because compilation and execution help it to work normally. On the other hand, it can be seen that it is fast for many web applications too.
Run-time Errors The Python language is dynamically typed so it has many design restrictions that are reported by some Python developers. It is even seen that it requires more testing time, and the errors show up when the applications are finally run.
Underdeveloped Database Access Layers As compared to the popular technologies like JDBC and ODBC, the Python’s database access layer is found to be bit underdeveloped and primitive. However, it cannot be applied in the enterprises that need smooth interaction of complex legacy data.
Let’s consist a small combination of them – following can be incredibly useful –
MATLAB
– Invaluable for signal processing – Incredibly broad array of useful libraries – Simplest and most concise language for anything involving matrix operations – Works very well for anything that is simply represented as a numeric feature matrix – Huge pain to use for anything that isn’t simply represented as a numeric feature matrix – Lacking a good open source ecosyste
Python
– Very fragmented but comprehensive scientific computing stack – Pandas, scikit.learn, numpy, scipy, ipython, & matplotlib are my most-used scientific computing libraries – IPython notebook makes a nice interactive data analysis tool – All the benefits of a general purpose programming language – Unfortunately slow if you don’t drop into C – Some of the scientific computing stack is still stuck in Python 2.7 – Very good for problems that don’t come as a simple feature matrix, between tools like pandas and nltk – Incredible open source ecosystem
Python is most popular language in the AI field.
Why ? Because –
Python comes with a huge amount of libraries. Many of the libraries are for Artificial Intelligence and Machine Learning. Some of the libraries are Tensorflow (which is high-level neural network library), scikit-learn (for data mining, data analysis and machine learning), pylearn2 (more flexible than scikit-learn), etc. The list keeps going and never ends.
For other languages, students and researchers need to get to know the language before getting into ML or AI with that language. This is not the case with python. Even a programmer with vert basic knowledge can easily handle python.
Apart from that, the time someone spends on writing and debugging code in python is way less when compared to C, C++ or Java. This is exactly the students of AI and ML wants. They don’t want to spend time on debugging the code for syntax errors, they want to spend more time on their algorithms and heuristics related to AI and ML. Not just the libraries but their tutorials, handling of interfaces are easily available online. People build their own libraries and upload them on GitHub or elsewhere to be used by others
Python has a solid claim to being the fastest growing major programming language. Recommended to check ground breaking statistics on incredible growth of python and why is python growing so quickly from stack overflow.
Advantages of Python over Matlab
1. Python code is more compact and easier to read than Matlab code —- Unlike Matlab, which uses end statement to indicate the end of a block, Python determines block size based on indentation. —- Python uses square brackets for indexing and parentheses for functions and methods, whereas Matlab uses parentheses for both, making Matlab more difficult to differentiate and understand. —- Python’s better readability leads to fewer bugs and faster debugging.
2. While most programming languages, including Python, use zero-based indexing, Matlab uses one-based indexing making it more confusing for users to translate.
3. The object-oriented programming (OOP) in Python is simple flexibility while Matlab’s OOP scheme is complex and confusing
4. Python is free and open —- While Python is open source programming, much of Matlab is closed —- The developers of Python encourage users to input suggestions for the software, while the developers of Matlab offer no such interaction
5. There is no Matlab counterpart to Python’s import statement 6. Python offers a wider set of choices in graphics package and toolsets
In Steve Hanly’s research on the speed test between Python and MATLAB for vibration analysis
Utilization of Python
Python has been gaining momentum as being the programming language for novice users. Highly ranked Computer Science departments at MIT and UC Berkeley use Python to teach their novice programming language students. The three largest Massive Open Online Course (MOOC) providers (edX, Coursera andUdacity) all use Python as their programming language for their beginning courses in programming. A variety of professors in other disciplines now utilize the need for novice students to understand Python and its key features.
Conclusion
There is no such thing as a ‘best language for machine learning’
Popularity is not a good yardstick to use when selecting a programming language for machine learning and data science. There is no such thing as a ‘best language for machine learning’ and it all depends on what you want to build, where you’re coming from and why you got involved in machine learning.
In most cases developers port the language they were already using into machine learning, especially if they are to use it in projects adjacent to their previous work?—?such as engineering projects for C/C++ developers or web visualizations for JavaScript developers.
Reference: https://www.codementor.io/@innat_2k14/preferable-tools-for-machine-learning-python-matlab-r-jfozzpphz
For deep learning, Python is more preferred by data scientists because of wide selection of available libraries depending on the task. Important deep learning frameworks like TensorFlow, Keras, Theano, etc are supported by Python. In short, Python has universal support for different deep learning frameworks. If you like programming or ready to learn it, Python is the better option.
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