TensorFlow is an open-source machine learning framework developed by Google. It relies upon Tensors (multi-dimensional arrays) which empower a wide range of API to develop machine learning applications, primarily deep neural networks. TensorFlow is commonly used in machine learning practice, so better start using it already.
Thankfully the TensorFlow website provides a guide for programmers as well as detailed tutorials. Here is the basic tutorial to get get started with TensorFlow. To accompany programmers, Google cloud has also created a series of videos on machine learning and TensorFlow.
This next video is going over the basic tutorial with iris flowers images classification. Yufeng Guo walks us through the initial tutorial to develop a linear model to classify flowers, corresponding to the explanations and code available in the page “getting started with TensorFlow: Premade Estimators” and aimed at readers who have some experience in machine learning.
Note: to get this tutorial running well, you will need to have a Python IDE (such as PyCharm, or a Jupyter notebook) with a virtual environment loaded with the TensorFlow, Pandas and Numpy librairies. You will also need a Git client software (Git for Windows if you’re using Windows) to download the data from GitHub. You may use Anaconda to properly load the librairies in your Python IDE.
Though several programming languages can be used to develop Artificial Intelligence applications, the language that is most often used and advised to use is Python, because it is a general purpose language designed for readability.
Documentation and resources can be found to use and learn the language on the official Python website, especially with the complete tutorial available for each version of Python.
If you already have programming experience, you can have a brief overview of the syntax and specificity of Python in this video. This great course on problem-solving with algorithms and data structures will give you a complete, step-by-step approach to learning Python with explanations, videos and exercises, while this other tutorial from Stanford quickly goes over Python basics but dives deeper into using the Numpy, SciPy and Matplotlib modules for scientific computing.
Other languages that are often used include: Java, Lisp, Prolog, C++, R…
Python Programming Tools
Here is a series of programming tools and other systems to assist in the programming of AI applications.
PyCharm: an Integrated Development Environment – IDE – used in computer programming, specifically for the Python language.
Jupyter: the Jupyter Notebook is an open-source web application to create and share documents with live code, equations, visualizations.
NumPy is the fundamental package for scientific computing with Python.
TensorFlow: an open-source machine learning framework originally developed by Google Brain researchers and engineers.
Anaconda: Anaconda is a platform for Python and R data science and machine learning on Linux, Windows, and Mac OS X.
TFLearn: a deep learning library featuring a higher-level API for TensorFlow
Scikit-Learn: an open-source machine learning library for Python
Pandas: open-source data structure and analysis library for Python
Caffe: a deep learning framework made with expression, speed, and modularity in mind
Keras: a high-level neural networks API, written in Python
Pytorch: an open source machine learning library for Python, used for applications such as natural language processing.
Certain AI applications may require large computing power to process important amounts of data. Fortunately, several companies provide such services on a regular or on-demand basis. Here are the most famous cloud computing services.