Welcome Turk Hack Team Members, we'll talk about AI and Python today.
Python and Artificial Intelligence(AI) - How do they relate?
Python is one of the most popular programming languages used by developers today. Guido Van Rossum created it in 1991 and ever since its inception has been one of the most widely used languages along with C++, Java, etc.
In our endeavour to identify what is the best programming language for AI and neural network, Python has taken a big lead. Let us look at why Artificial Intelligence with Python is one of the best ideas under the sun.
Features and Advantages of Python
Python is an Interpreted language which in lay mans terms means that it does not need to be compiled into machine language instruction before execution and can be used by the developer directly to run the program. This makes it comprehensive enough for the language to be interpreted by an emulator or a virtual machine on top of the native machine language which is what the hardware understands.
It is a High-Level Programing language and can be used for complicated scenarios. High-level languages deal with variables, arrays, objects, complex arithmetic or Boolean expressions, and other abstract computer science concepts to make it more comprehensive thereby exponentially increasing its usability.
Python is also a General-purpose programming language which means it can be used across domains and technologies.
Python also features dynamic type system and automatic memory management supporting a wide variety of programming paradigms including object-oriented, imperative, functional and procedural to name a few.
Python is available for all Operating Systems and also has an open-source offering titled CPython which is garnering widespread popularity as well.
Let us now look as to how using Python for Artificial Inelegance gives us an edge over other popular programming languages.
AI and Python: Why?
The obvious question that we need to encounter at this point is why we should choose Python for AI over others.
Decoding Python alongside AI
Python along with packages like NumPy, scikit-learn, iPython Notebook, and matplotlib form the basis to start your AI project.
NumPy is used as a container for generic data comprising of an N-dimensional array object, tools for integrating C/C++ code, Fourier transform, random number capabilities, and other functions.
Another useful library is pandas, an open source library that provides users with easy-to-use data structures and analytic tools for Python.
Matplotlib is another service which is a 2D plotting library creating publication quality figures. You can use matplotlib to up to 6 graphical users interface toolkits, web application servers, and Python scripts.
Your next step will be to explore k-means clustering and also gather knowledge about decision trees, continuous numeric prediction, logistic regression, etc.
Some of the most commonly used Python AI libraries are AIMA, pyDatalog, SimpleAI, EasyAi, etc. There are also Python libraries for machine learning like PyBrain, MDP, scikit, PyML.
Let us look a little more in detail about the various Python libraries in AI and why this programming language is used for AI.
Python Libraries for General AI
AIMA Python implementation of algorithms from Russell and Norvigs Artificial Intelligence: A Modern Approach.
pyDatalog Logic Programming engine in Python
SimpleAI Python implementation of many of the artificial intelligence algorithms described on the book Artificial Intelligence, a Modern Approach. It focuses on providing an easy to use, well docmented and tested library.
EasyAI Simple Python engine for two-players games with AI (Negamax, transposition tables, game solving).
Python for Machine Language (ML)
Let us look as to why Python is used for Machine Learning and the various libraries it offers for the purpose.
PyBrain - A flexible, simple yet effective algorithm for ML tasks. It is also a modular Machine Learning Library for Python providing a variety of predefined environments to test and compare algorithms.
PyML A bilateral framework written in Python that focuses on SVMs and other kernel methods. It is supported on Linux and Mac OS X.
Scikit-learn Scikit-learn is an efficient tool for data analysis while using Python. It is open source and the most popular general purpose machine learning library.
MDP-Toolkit Another Python data processing framework that can be easily expanded, it also has a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures. The implementation of new algorithms is easy and intuitive. The base of available algorithms is steadily increasing and includes signal processing methods (Principal Component Analysis, Independent Component Analysis, and Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others.
Python Libraries for Natural Language & Text Processing
NLTK Open source Python modules, linguistic data and docmentation for research and development in natural language processing and text analytics with distributions for Windows, Mac OSX, and Linux.
Python vs. C++ for AI
Python is a more popular language over C++ for AI and leads with a 57% vote among developers. That is because Python is easy to learn and implement. With its many libraries, they can also be used for data analysis.
Performance wise C++ outperforms Python. This is because C++ has the advantage of being a statically typed language and hence there are no typing errors during runtime. C++ also creates more compact and faster runtime code.
Python is a dynamic (as opposed to static) language and reduces complexity when it comes to collaborating meaning you can implement functionality with less code. Unlike C++, where all significant compilers tend to do specific optimisation and can be platform specific, Python code can be run on pretty much any platform without wasting time on specific configurations.
With the rise of GPU-accelerated computing offering capabilities for parallelism which has led to the creation of libraries such as CUDA Python and cuDNN, Python has the edge over C++. This means is that more and more of the actual computing for machine learning workloads is being offloaded to GPUs and the result is that any performance advantage that C++ may have is becoming increasingly irrelevant.
Python wins over C++ regarding simplicity of code, especially amongst new developers. C++ being a lower-level language requires more experience and skill to master.
Pythons simple syntax also allows for a more natural and intuitive ETL (Extract, Transform, Load) process, and means that it is faster for development when compared to C++, allowing developers to test machine learning algorithms without having to implement them quickly.
Between C++ and Python, the latter has more edge and is more suitable for AI. With its simple syntax and readability promoting the rapid testing of complex machine learning algorithms and a thriving community bolstered by collaborative tools like Jupyter Notebooks and Google Colab, Python wins the crown.
//Quoted. Thanks for reading.
Python and Artificial Intelligence(AI) - How do they relate?
Python is one of the most popular programming languages used by developers today. Guido Van Rossum created it in 1991 and ever since its inception has been one of the most widely used languages along with C++, Java, etc.
In our endeavour to identify what is the best programming language for AI and neural network, Python has taken a big lead. Let us look at why Artificial Intelligence with Python is one of the best ideas under the sun.
Features and Advantages of Python
Python is an Interpreted language which in lay mans terms means that it does not need to be compiled into machine language instruction before execution and can be used by the developer directly to run the program. This makes it comprehensive enough for the language to be interpreted by an emulator or a virtual machine on top of the native machine language which is what the hardware understands.
It is a High-Level Programing language and can be used for complicated scenarios. High-level languages deal with variables, arrays, objects, complex arithmetic or Boolean expressions, and other abstract computer science concepts to make it more comprehensive thereby exponentially increasing its usability.
Python is also a General-purpose programming language which means it can be used across domains and technologies.
Python also features dynamic type system and automatic memory management supporting a wide variety of programming paradigms including object-oriented, imperative, functional and procedural to name a few.
Python is available for all Operating Systems and also has an open-source offering titled CPython which is garnering widespread popularity as well.
Let us now look as to how using Python for Artificial Inelegance gives us an edge over other popular programming languages.
AI and Python: Why?
The obvious question that we need to encounter at this point is why we should choose Python for AI over others.
- Python offers the least code among others and is in fact 1/5 the number compared to other OOP languages. No wonder it is one of the most popular in the market today.
- Python has Prebuilt Libraries like Numpy for scientific computation, Scipy for advanced computing and Pybrain for machine learning (Python Machine Learning) making it one of the best languages For AI.
- Python developers around the world provide comprehensive support and assistance via forums and tutorials making the job of the coder easier than any other popular languages.
- Python is platform Independent and is hence one of the most flexible and popular choiceS for use across different platforms and technologies with the least tweaks in basic coding.
- Python is the most flexible of all others with options to choose between OOPs approach and scripting. You can also use IDE itself to check for most codes and is a boon for developers struggling with different algorithms.
Decoding Python alongside AI
Python along with packages like NumPy, scikit-learn, iPython Notebook, and matplotlib form the basis to start your AI project.
NumPy is used as a container for generic data comprising of an N-dimensional array object, tools for integrating C/C++ code, Fourier transform, random number capabilities, and other functions.
Another useful library is pandas, an open source library that provides users with easy-to-use data structures and analytic tools for Python.
Matplotlib is another service which is a 2D plotting library creating publication quality figures. You can use matplotlib to up to 6 graphical users interface toolkits, web application servers, and Python scripts.
Your next step will be to explore k-means clustering and also gather knowledge about decision trees, continuous numeric prediction, logistic regression, etc.
Some of the most commonly used Python AI libraries are AIMA, pyDatalog, SimpleAI, EasyAi, etc. There are also Python libraries for machine learning like PyBrain, MDP, scikit, PyML.
Let us look a little more in detail about the various Python libraries in AI and why this programming language is used for AI.
Python Libraries for General AI
AIMA Python implementation of algorithms from Russell and Norvigs Artificial Intelligence: A Modern Approach.
pyDatalog Logic Programming engine in Python
SimpleAI Python implementation of many of the artificial intelligence algorithms described on the book Artificial Intelligence, a Modern Approach. It focuses on providing an easy to use, well docmented and tested library.
EasyAI Simple Python engine for two-players games with AI (Negamax, transposition tables, game solving).
Python for Machine Language (ML)
Let us look as to why Python is used for Machine Learning and the various libraries it offers for the purpose.
PyBrain - A flexible, simple yet effective algorithm for ML tasks. It is also a modular Machine Learning Library for Python providing a variety of predefined environments to test and compare algorithms.
PyML A bilateral framework written in Python that focuses on SVMs and other kernel methods. It is supported on Linux and Mac OS X.
Scikit-learn Scikit-learn is an efficient tool for data analysis while using Python. It is open source and the most popular general purpose machine learning library.
MDP-Toolkit Another Python data processing framework that can be easily expanded, it also has a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures. The implementation of new algorithms is easy and intuitive. The base of available algorithms is steadily increasing and includes signal processing methods (Principal Component Analysis, Independent Component Analysis, and Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others.
Python Libraries for Natural Language & Text Processing
NLTK Open source Python modules, linguistic data and docmentation for research and development in natural language processing and text analytics with distributions for Windows, Mac OSX, and Linux.
Python vs. C++ for AI
Python is a more popular language over C++ for AI and leads with a 57% vote among developers. That is because Python is easy to learn and implement. With its many libraries, they can also be used for data analysis.
Performance wise C++ outperforms Python. This is because C++ has the advantage of being a statically typed language and hence there are no typing errors during runtime. C++ also creates more compact and faster runtime code.
Python is a dynamic (as opposed to static) language and reduces complexity when it comes to collaborating meaning you can implement functionality with less code. Unlike C++, where all significant compilers tend to do specific optimisation and can be platform specific, Python code can be run on pretty much any platform without wasting time on specific configurations.
With the rise of GPU-accelerated computing offering capabilities for parallelism which has led to the creation of libraries such as CUDA Python and cuDNN, Python has the edge over C++. This means is that more and more of the actual computing for machine learning workloads is being offloaded to GPUs and the result is that any performance advantage that C++ may have is becoming increasingly irrelevant.
Python wins over C++ regarding simplicity of code, especially amongst new developers. C++ being a lower-level language requires more experience and skill to master.
Pythons simple syntax also allows for a more natural and intuitive ETL (Extract, Transform, Load) process, and means that it is faster for development when compared to C++, allowing developers to test machine learning algorithms without having to implement them quickly.
Between C++ and Python, the latter has more edge and is more suitable for AI. With its simple syntax and readability promoting the rapid testing of complex machine learning algorithms and a thriving community bolstered by collaborative tools like Jupyter Notebooks and Google Colab, Python wins the crown.
//Quoted. Thanks for reading.
