TensorFlow vs PyTorch: Comparing Two Popular Deep Learning Frameworks

This report provides a comparative analysis of TensorFlow and PyTorch, two prominent deep learning frameworks. It explores the key differences in their architecture, adoption, and functionality. The report examines TensorFlow's strengths, including its distributed training support and visualization capabilities through TensorBoard. It also highlights PyTorch's advantages, such as its dynamic graph definition and Pythonic approach, making it more developer-friendly. The report discusses aspects like dynamic vs. static graph definition, distributed training, visualization tools (TensorBoard vs. Visdom), and deployment options. Furthermore, it delves into whether each framework is more of a library or a framework, emphasizing PyTorch's modular design and TensorFlow's flexibility. The report concludes by summarizing the key distinctions and offering insights to assist developers in selecting the most suitable framework for their AI and deep learning projects.