Is Python Fast Enough for Machine Learning?
A favorite for everything from apps and process automation to web development and scripting, Python is a versatile language that’s highly readable, has numerous libraries, has a scalable language, and can produce prototypes and minimum viable products (MVPs) in a short period of time. It’s able to address problems that may crop up in the long run easily. Python is also rapidly shaping up to be developers’ choice for artificial intelligence (AI), deep learning, and machine learning projects.
Application developers enjoy a new wealth of opportunities for app developers. AI empowers apps like Spotify and Netflix to make recommendations based on previous user consumption, as well as improve the productivity of employees and the quality of customer service in business.
Some may argue that Python is too slow for machine learning. In this article, we’ll be looking into how good a fit Python is for AI, and whether or not developers should choose another language when working on machine or deep learning projects.
AI, machine learning, and deep learning
AI refers to any sort of intelligence that a machine can produce that leads to a solution after it’s been presented with a problem. Machine learning takes that further by parsing data via algorithms, and then makes decisions informed by what it’s learned.
Deep learning works similarly, but has the ability to come to a conclusion in a way that’s similar to a human making a decision. Deep learning involves a number of algorithms arranged in a layered structure that was inspired by the human brain’s neural network. The resulting model is able to learn different levels of representation that correlate to various levels of abstraction.
The top programming languages for AI
The most popular programming languages when working on AI projects aside from Python are:
- Java – great user-friendliness, platform independence, and a flexible nature.
- R – excellent at crunching massive numbers, ability to work on different paradigms of programming, can work with C, C++, and Fortran, wide array of libraries and packages that can extend its functionalities.
- Prolog – has both “The Symbolic Approach” and “The Statistical Approach”, rapid prototyping, garbage collection, dynamic object creation, and flexibility.
- Haskell – strong abstraction capabilities, code reusability, built-in memory management, and easy to understand.
- Julia – built-in package manager, dynamic type system, can work for parallel and distributed computing, macros and metaprogramming abilities, direct support for C functions, and support for multiple dispatches.
What’s not so great about Python machine learning
As far as programming languages go, Python doesn’t do everything as well as the languages mentioned above do. Java for example handles concurrency better, and Julia is faster. Here’s more on why a developer might not want to use Python for AI.
Python uses whitespace to denote blocks of code, where other languages use more explicit curly brackets. Whitespacing is quite strict, and while the rules of it can be learned, it can still be tricky for beginners to get the hang of.
Python is a dynamic language, and as such is interpreted at runtime instead of being compiled. The Python interpreter is gaining improvements though and has become easier to speed up. The mypyc project translates Python that’s type-annotated into native C in a far smoother way than Cython. Additionally, programmers (especially beginner ones) should be more concerned with how well their code works rather than how fast.
Python doesn’t support threading due to Global Interpreter Lock (GIL). This means that only one thread can execute at a time. Multi-threaded CPU-bound programs are usually slower than single-threaded ones, but this issue can be fixed by replacing multi-threaded applications with multiprocessing programs.
Being a dynamically-typed language, Python doesn’t require the variable type to be declared at the time of coding. It employs duck-typing, where the type of object is only a concern at run-time and it’s not a requirement to mention the type explicitly. This causes a run-time error that programmers can easily see during coding.
Underdeveloped database access layers
The database access layers in Python aren’t as developed as other widely-used technologies like Java Database Connectivity (JDBC). It’s not practical to use in big enterprises because of this.
Why is Python the most popular?
Python may have its shortcomings, but it’s still the most popular programming language for AI. Here’s why Python machine learning is the most widespread.
Python allows for quick prototyping. Learning the stack’s intricacies can waste a lot of time, but with Python, AI development can begin quickly and then developers can move on to building AI programs and algorithms. Additionally, Python code is very similar to English. It’s easy to read and write, and developers aren’t forced to spend a lot of time writing complex code.
What’s more is that the process is greatly simplified by the availability of really useful libraries and frameworks for machine learning and AI.
Easy to integrate
Python is super easy to integrate with other lower-level languages like C, C++, and Java. For business environments, it even incorporates better than R. The Python based-stack is a dream to incorporate with work done by data-scientists, which is really useful for AI and machine learning.
Python offers the option for users to use either OOPS or scripting. There’s no need to recompile source code, and developers can make changes and see the results of those changes quickly. Developers can also choose which programming style they’re most comfortable with, or even mix and match styles to tackle different problems in a way that they think is best. The styles offered are:
- Imperative – made up of commands that describe how a computer should execute the commands. The developer defines the sequence of computations that happen as a change of the program state.
- Functional/declarative – declares what operations are to be performed in the form of mathematical equations. Doesn’t consider the program state.
- Object-oriented – based on class and object (similar objects form classes). Python can’t fully perform encapsulation, so this style isn’t fully supported by this language, but this style can still be used to some degree.
- Procedural – most common for beginners, tasks are proceeded in a step-by-step way. Most often used for iteration, sequencing, selection, and modularization.
Python is an extremely readable, high-level language that’s really friendly to developers of all skill and experience levels. Its syntax is consistent so people learning the language are able to read others’ code as well as write their own quite easily. The algorithms and calculations that implementation requires are complex enough with the language used being difficult too. Python’s simplicity really lends itself to AI and machine learning.
Python machine learning is also simpler than with other languages because its semantics are similar to a lot of mathematical concepts. This is great because of the math necessary for machine learning.
The most important part of machine learning, AI, and deep learning algorithms is data. Handling data requires a lot of visualization in order to pick out patterns and make sense of factors and variables. Python machine learning makes use of Python’s great software packages.
Developers can make plots, charts, and histograms to better understand how data will work together and interact with each other. There are also APIs that make visualization easier by enabling developers to outline clear reports of data.
Huge selection of frameworks and libraries
Implementing machine learning and AI algorithms can be time-consuming and difficult, so it’s really important to have an environment that’s structured and tested well so that developers have as few barriers to success as possible.
Python has a large number of libraries and frameworks. Because of its rich tech stack, Python has an impressive set of libraries for AI and machine learning. They include:
- TensorFlow, Scikit-learn and Keras for machine learning
- SciPy for advanced computing
- NumPy for high-performance data analysis and scientific computing
- Seaborn for data visualization
- Pandas for general-purpose data analysis
There’s a select group of languages that are open-source, and Python is one of them. Open-source languages can be customized based off the developer’s needs, and with AI and machine learning, use cases are always growing and changing. Open-source languages provide unparalleled flexibility as well as constant peer reviewing, so that bugs can be fixed quicker.
Because it’s so versatile, Python machine learning can run on any platform – Linux, Unix, MacOS, Windows, and 21 others. Developers only need to make a few small-scale changes and change a few lines of code to make a form of code that’s executable for a move from one platform to another. There are packages like PyInstaller that help developers prepare their code for a different platform too. This saves a lot of time and money that would be wasted testing on various platforms, and the process is made a lot simpler and more convenient.
Python has a massive amount of documentation and online community. The sheer amount of online resources on Python means that it’s far easier to find answers and solve bugs for this language than others. There are a lot of resources for beginners and pros alike, and there are plenty of communities and forums where programmers and AI/machine learning developers solve problems together, discuss errors, and just help each other out.
While far from the only choice for AI and ML projects, Python is a great one and fast enough for machine learning. Beginners looking to work on AI or ML projects should definitely learn Python, because even if they should go on to use another language in the process or in future projects, they will need some understanding of this common programming language. This will help in understanding previous and future projects and provide them a great foundation in programming in general.
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