Machine learning is an invaluable skill to have in our tech-savvy world. Today, we can get almost anything done with the help of artificial intelligence. With the advancement of deep learning and machine learning, companies are continuously looking for new ways to leverage this technology to achieve greater results with less time and money.
This post will review the main differences between TensorFlow and PyTorch when it comes to data science computing. It will also highlight the pros and cons of each one of them, so that you make the right choice for your specific needs.
What is TensorFlow?
TensorFlow is a high-level software library for the analysis of complex data, developed by Google. It is intended to be used as a drop-in replacement for the low-level routines of specific programming languages.
Although it is designed to be used with numerical languages such as Python and R, it can be used with any high-level programming language, provided it has been compiled for the target platform.
What is PyTorch?
PyTorch is a fast, high-level, general-purpose neural network engine. It was developed as a drop-in replacement for the low-level Torch code in the Google DeepMind Challenge. It has been designed with speed and efficiency in mind, with the emphasis on parallel execution and stability.
How Does TensorFlow differ from PyTorch?
First, let’s get to the details of what makes TensorFlow and PyTorch different. Both TensorFlow and PyTorch use the same high-level programming language, called Python.
However, TensorFlow is focused on being used as a software library with applications to machine learning. In contrast, PyTorch is meant to be used as a general-purpose engine that can be used for any kind of data analysis.
Pros & Cons of TensorFlow
Now that we know what makes TensorFlow and PyTorch different, let’s get into the main reasons why each one of them is a good fit for your specific needs.
Pros of TensorFlow
Ease of Use: TensorFlowUI is a high-level, visual programming language interface that makes it easy to create and train models. It works well with modern computing platforms and is optimized for fast execution.
Compute Power: With the addition of the Tensor Processing Power (TAPP) tool, it is possible to speed up complex model training by a factor of 100,000. TensorFlow is state-of-the-art in this regard, using hardware-accelerated TensorFlow implementation in the NVIDIA GPU.
Cons of TensorFlow
Extensive Learning Curve: Deep learning requires an expert level of skill to master. It is therefore an advanced technique that only the most sophisticated AI systems can handle.
However, that said, if you are just getting into this field, you may have to spend some time getting comfortable with the programming concepts required to implement these technologies effectively.