If you have recently started learning machine learning, you might have already realized the power of artificial neural networks and deep learning compared to traditional machine learning. Compared to other models, artificial neural networks require an extra set of technical skills and conceptual knowledge.

Figure 1. Comparison of Deep Learning and the Traditional Machine Learning Approaches (Figure by Author)

The most important of these technical skills is the ability to use a deep learning framework. A good deep learning framework speeds up the development process and provides efficient data processing, visualization, and deployment tools. When it comes to choosing a deep learning framework, as of 2020, you only have two viable options :

Well, we can compare TensorFlow an PyTorch for days, but this post is not about framework benchmarking. This post is about what you can achieve with TensorFlow.

What is TensorFlow?

TensorFlow is an end-to-end framework and platform designed to build and train machine learning models, especially deep learning models. It was developed by Google and released as an open-source platform in 2015.

The two programming languages with stable and official TensorFlow APIs are Python and C. Besides, C++, Java, JavaScript, Go, and Swift are other programming languages where developers may find limited-to-extensive TensorFlow compatibility. Most developers end up using Python since Python has compelling data libraries such as NumPy, pandas, and Matplotlib.

#machine-learning #data-science #tensorflow #deep-learning #artificial-intelligence