The furore over machine learning has put TensorFlow, Google's open source software for machine learning projects, on the crest of the wave, especially when combined with the Python programming language. The last report The State of the Octoverse from GitHub highlights that TensorFlow has been one of the projects that have recorded the most movement on the coding platform in 2019. 9,900 users contributed to its growth by asking questions and issues and, to date, it has opened 46,000 repositories using TensorFlow, like those in the NumPy or PyTest libraries.
TensorFlow is an open source platform for graph-based numerical computing. It has the Google stamp and allows the development of machine learning applications with relative ease. These applications will provide us with predictions, ratings or recommendations based on the analysis of a data set. As the system collects more and more information, it learns automatically and the reliability of its results increases. TensorFlow can run on single or multiple CPUs and GPUs as well as mobile devices.
TensorFlow has multiple uses. One of the most important is the development of artificial neural networks used in:
Combining TensorFlow and Python is one of the most effective and simple formulas for developing machine learning applications. Why?
Python will be one of the most widely used programming languages in the year 2020. It is the second most popular in the latest Developer Survey from Stack Overflow and third in the TIOBE index. Using Python in machine learning offers great advantages because:
For TensorFlow, it is worth highlighting:
When developing machine learning projects with TensorFlow in Python, you need to become familiar with some key concepts such as:
In short, we could say that TensorFlow aims to develop a model of machine learning that, with an input and training data set, it is possible to set generalizations. Before the data set is added, it must be pre-processed so that the system can get the most out of it. Then, for the training, enough periods must be forecast, so that the model learns; yet not too many, as this could jeopardise the results.
In machine learning projects with TensorFlow in Python it is possible to use additional libraries that will make programming easier. For example, NumPy and SciPy for mathematical functions; Pandas and Seaborn, for statistical use and data visualization; or Matplotlib to generate graphics.