Since Google open sourced its machine learning framework in 2015, Tensorflow has risen in popularity with more than 1500 projects mentions on Github. Riding on the back of its popularity, Mountain View company made a significant announcement earlier this year, updating TensorFlow to Version 1.0 that is packed with newer features and promises more performance improvements with high level APIs.
In the showdown of AI frameworks, TensorFlow emerges as the winner, thanks to its soaring popularity among developers, contributors and researchers in forums such as Github andStack Overflow. Interestingly, TensorFlow is being used in over 6000 open source repositories online and is being used by a wide array of people from academia and coders for language translation and early detection of skin cancer among other cases. And it is also changing the way developers are interacting with machine learning technology.
According to Fei Fei Li, in the highly rarefied field of AI, TensorFlow is tearing down by the barriers by providing the infrastructure and hardware. “AI requires enormous computing and deep learning algorithms can easily boast of tens of millions of parameters and billons of connections. Training and using such models requires computational resource,” she said, adding the TensorFlow library allows one to focus on the creativity of their solution and leave the infrastructure aside.