Tensor Flow is an all-encompassing open-source Machine Learning (ML) platform. It empowers developers in creating applications involving Deep Learning, besides being crucial for training and inferential analysis of Deep Neural Networks. It is a comprehensive and flexible system constituted of tools, libraries, and community-based resources. Tensor Flow has the capacity to handle vast amounts of data through its higher dimension and multi-dimensional arrays called Tensors. Data Flow Graphs enable distributed code execution across a cluster of systems.

Easy Debugging Tensor Flow comes with an Eager Execution mode that lends efficiency to debugging process. It allows the operations immediate execution instead of waiting for the computational graph stage. It offers the developer to debug immediately and at each step inducing transparency to the process.

Faster Execution You can distribute computation across systems by choosing a distribution strategy that suits your needs. It helps in the faster execution of complex Tensor Flow models, especially those involving training, inference, and evaluation.

Minimizing Errors Tensor Flow brings you the advantage of special Loss Functions (Cost Functions) that help in minimizing the error between the expected and actual output. There is a variety of losses used depending upon the datasets, Tensor Flow models, and performance. Examples of Loss Functions include Binary Cross-Entropy, Poisson, Hinge, Means Squared, and Kullback-Leibler Divergence, etc.