Before you dive in, you can visit one of my previous blogs and understand if you really need a GPU for getting started with Deep Learning from the link provided below. In this article, we will explore how to get the latest version of TensorFlow and stay updated with modern technology.

This experience is rather frustrating for most individuals because it is a common occurrence while dealing with deep learning models. However, one of the most annoying issues that deep learning programmers, developers, and enthusiasts face is the trouble of CUDA errors. The GPU improvises the performance of this deep learning framework to reach new heights and peaks.

While this library performs effectively with most smaller and simpler datasets to achieve tasks on a CPU, its true power lies in the utilization of the Graphics Processing Unit (GPU). TensorFlow is regarded as one of the best libraries to solve almost any question related to neural networks and deep learning. One such popular deep learning library to build and construct models to find solutions to numerous tasks is TensorFlow. One of the most popular ways to handle deep learning models to solve complex computational problems is with the help of deep frameworks. From dominating in almost every single competition with its innovative and groundbreaking technologies, it has also led to several new types of research and training methods. The rise to prominence of deep learning over the past decade is spectacular. Photo by ThisisEngineering RAEng on Unsplash

Pip install tensorflow