Role Of Python Applications in AI Systems
Python applications are deeply ingrained in the technological universe because they are simple, adaptive, and easy to iterate with, especially when it comes to working with artificial intelligence systems. From building websites and software developments to creating scripts and cracking complex mathematical solutions, python has worked through a myriad of areas and always strives to make a coder’s job easy.
With a variety of pre-built libraries like NumPy, which takes care of scientific computations, Scipy works with advanced computing, and Pybrain deals in machine learning, python qualifies as the apt choice for language AI. They can basically form the skeletal framework of any AI project.
In this article, we are looking at the different python applications that are a part of artificial intelligence systems.
AIMA, pyDatalog, SimpleAI, EasyAi, etc. are a few most commonly used Python AI libraries, and PyBrain, MDP, scikit, and PyML are a few machine learning python libraries. Let’s start with Python libraries for General AI.
It provides an overview of the algorithm in the textbook, Artificial Intelligence: A Modern Approach., also known as AIMA. The code, which is free to use under the MIT license brings together algorithms in a pseudo-code format and it is meant for Python 2.5 through 2.7.
This logic programming code enables you to run queries on various databases or python objects. Primarily, pyDatalog can be used as a query language as it is capable of performing multi-dimensional queries (from memory datastore, 11 relational databases, and NoSQL database with appropriate connectors). With a cleaner syntax, it is also considered more expressive. Also, it can facilitate the re-use of SQL code snippets.
Amongst the several algorithms described in the textbook, Artificial Intelligence: A Modern Approach, SimpleAI offers an easy, well-documented library.
It works for two-player games like Tic Tac Toe, Connect 4, etc with Negamax algorithm with transposition tables, game solving features.
Python for ML
Tailor-made for predictive data analysis, scikit-learn works smoothly when blending it with python. Also, it is considered the most common general-purpose machine learning library and can be reused multiple times. Built on NumPy, SciPy, and matplotlib, scikit-learn can be used commercially and has a BSD license.
It is a bilateral, interactive framework that primarily focuses on Support Vector Machines (SVM) kernel methods. PyML is compatible with MAC OS X and Linux. Some of its features include model selection, normalization, preprocessing, the syntax of combining classifiers, and feature selection.
PyBrain is considered one of the most basic yet powerful, easy-to-use, and effective algorithms for different ML tasks. Given that it’s a modular Machine Learning Library for Python, it enables developers to test and compare algorithms in various predefined environments. As the name suggests, PyBrain consists of neural networks which favor reinforcement learning and unsupervised learning making it a perfect choice for applying in everyday tasks.
This data processing framework is easily expandable and contains a host of supervised and unsupervised learning algorithms besides other data processing units which can further be combined into sequences and network architectures.
Python developers from around the world are always available to provide comprehensive support through forums, tutorials, etc, hence making a coder’s job far easier.
In the current innovations and dynamics happening in the coding world, python applications, which are considered extremely flexible and simple to use, constitute the main coding language for about 80% of developers. Smart frameworks like scikit-learn and fast prototyping abilities make it the perfect AI programming language to predict, perceive, and analyze flawlessly.
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