Python Programming Language in AI
We all are in the era of Artificial Intelligence. Tech giants like Google, Microsoft & Facebook investing in AI. Amazon’s Alexa also shows how stiff competition is going into the market of AI, not only Google & Apple but other companies also dominate the tech market if they have the power of AI. There is a tremendous opportunity in 2018 for people who are looking for jobs in Machine Learning, Deep Learning & AI. Sometimes it seems like Machine Learning & AI both are the same things, but there is a fundamental difference between them. Here’s the best explanation about this:
Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider “smart”.
Here I’m going to tell you about how a programming language plays a crucial roll in the production of AI product. If you are looking for an opportunity in AI, you must understand the power of Python.
1. About Python
Python is a powerful high-level, object-oriented and most popular open source programming language created by Guido van Rossum. It has simple easy-to-use syntax, making it the perfect language for someone trying to learn computer programming for the first time.
Python is a general-purpose language. It has a wide range of applications from Web development (like Django, Flask & Web2py), scientific and mathematical computing (Orange, SymPy, NumPy) to desktop graphical user Interfaces (Pygame, Panda3D).
If you are a beginner, you can quickly start with Python. There are huge Python Learning resources available on the Internet. I suggest few books if you don’t know anything about Python :
2. Python Library & Framework for AI
In this section, we will be looking at powerful, popular Python libraries that are used for artificial intelligence. We will be looking at their advantages & drawbacks, and their features. So, Let’s begin and explore these fantastic Python libraries:
1. TensorFlow:
TensorFlow is an open-source software library for dataflow programming and widely used for machine learning applications such as neural networks. TensorFlow was developed by the Google Brain team for internal Google use. It was released under the Apache 2.0 open source license on November 9, 2015
“Computation using data flow graphs for scalable machine learning.”
TensorFlow is an interface for expressing Machine Learning algorithm & implementation for executing such algorithm. It helps for creating ensemble algorithms for today’s most challenging problems.
Advantages:
- Flexible Structure
- Rich Documentation with a great community
- Predefined implementation available as well as open-source
Drawbacks:
- It requires knowledge of Python
- Computation is bit slow
- Overkill for simpler tasks
2. PyTouch:
PyTorch is a Python-based open-source Machine Learning library with GPU support. PyTorch is a python package that provides two high-level features:
- Tensor computation (like NumPy) with strong GPU acceleration
- Deep Neural Networks built on a tape-based auto diff system
PyTorch is a cousin of lua-based Torch framework which is actively used at Facebook. The main feature is that Neural Networks can be built dynamically making way for learning more advanced and complex AI tasks.
“Best fits for those who are looking for more Pythonic Environment.”
PyTorch is fast and lean because it allows you to integrate acceleration libraries such as Intel MKL and NVIDIA (CuDNN, NCCL) to maximize speed.
“PyTorch is quite fast – whether you run small or large neural networks.”
Advantages:
- Deep learning research platform that provides maximum flexibility
- Fast & Lean
- Pretty good documentation
Drawbacks:
- PyTorch only supports Linux and OSX.
3. Keras:
Keras is an open-source high-level neural networks Library, written in Python. It is running on top of TensorFlow, CNTK, or Theano. Keras is an easy to use neural network library that promotes a clean and straightforward syntax. Keras is layer-oriented. I suggest use Keras If you’re using known network design.
Keras code is portable. There is no need to change your code written in Keras, whether you change your backend from Theano to TensorFlow. Keras is more mature then PyTorch with excellent community support.
Advantages:
- Documentation is very clean
- Keras code is Portable
- Community support is excellent
Drawbacks:
- Keras is not good when you implement your own layers and doing prototyping and research
4. Scikit Learn:
Scikit-learn is probably the most useful library for machine learning in Python. David Cournapeau initially developed Scikit-learn as a Google summer of code project in 2007. It is on NumPy, SciPy, and matplotlib. This library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.
The library is built upon the SciPy (Scientific Python) that must be installed before you can use Scikit-Learn. This stack that includes:
NumPy: Base n-dimensional array package
SciPy: Fundamental library for scientific computing
Matplotlib: Comprehensive 2D/3D plotting
IPython: Enhanced interactive console
Sympy: Symbolic mathematics
Pandas: Data structures and analysis
Scikit-Learn is characterized by a clean, uniform, and streamlined API, as well as by handy and complete online documentation. A benefit of this uniformity is that once you understand the basic use and syntax of Scikit-Learn for one type of model, switching to a new model or algorithm is very straightforward.
Advantages:
- Easy to use
- supports most practical tasks
Drawbacks:
- Bad for deep learning
3. Popular Implemented Projects in Python
Getting into Machine Learning and AI is not an easy task. Lots of effort put together for implementing some useful task to build an AI model. Open source projects can be useful for data scientists. You can learn by reading the source code and build something on top of the existing projects.
Lots of people start contributing to opensource just because of these awesome opensource projects. Here I will tell you about five most popular opensource projects which are widely used for developing any AI models are as follows :
1. TensorFlow : 169% up, from 493 to 1453 contributors
2. Keras: 656 contributors
3. PyTorch : 613 contributors
4. Scikit Learn: 1069 contributors
5. Theano: 330 contributors
4. Motivation
Machine learning is about teaching computers how to learn from data to make decisions or predictions. For true machine learning, the computer must be able to learn to identify patterns without being explicitly programmed.
You may also hear it labeled several other names or buzzwords:
Data Science, Big Data, Artificial Intelligence, Predictive Analytics, Computational Statistics, Data Mining, etc.
The best way to dive into AI is to learn on your own which is know as The Self-Starter Way. Self-Starter way is far better than the academic approach where you don’t have to follow lots of guidelines. Doing Practice is the only way to learn things.