You may wonder, with TensorFlow remaining a prominent framework in the deep learning industry, why we bothered to write a PyTorch book of the Python Machine Learning series, Machine Learning with PyTorch and Scikit-Learn. As a matter of fact, PyTorch has become the most widely-used deep learning framework in the academic and research community. To examine this further, let me provide an up-to-date and more comprehensive comparison between PyTorch and TensorFlow. Supporting dynamic computational graphs is an advantage of PyTorch over TensorFlow. The computational graphs in PyTorch are built on-demand compared to their static TensorFlow counterparts. This makes PyTorch more debug-friendly: you can execute the code line by line while having full access to all variables.

Before going through the list of popular Machine Learning frameworks, let's understand why it is important to embrace ML skills. We all know that Machine Learning and Artificial Intelligence are the hot trends in Technology. According to Gardner, these Technologies will create 2.3 million jobs in the future. It has been seen that there are lots of tools and Frameworks that help us to easily implement Machine learning in our application. So let's have a quick introduction to Machine Learning.

A recent survey revealed that 84% of data pros have used at least one ML framework in the last 5 years while 51% of data pros have used at least one ML product in the last 5 years. The most popular ML frameworks include Scikit-Learn, Tensorflow and Keras. The most popular ML products include SAS, Cloudera and Azure. Machine Learning Frameworks used in last 5 years. The practice of data science requires the use of machine learning products and frameworks to help data professionals automate processes that drive their business forward.

In the last few years, we've seen an amazing rise of software tools for Machine Learning (ML). Google's TensorFlow is currently "the king" of all ML frameworks, based on GitHub stars and Google searches. What makes TensorFlow so special and successful? What are its downsides compared to other competitors, and will it manage to stay on top? To explain this, we'll have to go way back to the basics.