#106 Machine Learning & Data Science Challenge 106
Greetings.
I am a machine learning engineer based in India, possessing a sustained interest in machine learning since my undergraduate studies. I have completed Stanford University's machine learning course (Andrew Ng) via Coursera, and IBM's machine learning and deep learning curriculum. My current focus is on machine learning and data science projects, aiming to leverage my expertise for impactful, real-world problem-solving.
What is TensorFlow?
TensorFlow is an open-source software library released in 2015 by Google to make it easier for developers to design, build, and train deep learning models.
TensorFlow originated as an internal library that the Google developers used to build the models in-house, and we expect additional functionality to be added in the open-source version as they are tested and vetted in an internal flavor.
Although TensorFlow is the only one of several options available to the developers and we choose to use it here because of its thoughtful design and ease of use.
At a high level, TensorFlow is a Python library that allows users to express arbitrary computation as a graph of data flows.
Nodes in this graph represent mathematical operations, whereas edges represent data communicated from one node to another.
Data in TensorFlow are represented as tensors, which are multidimensional arrays.
Although this framework for thinking about computation is valuable in many different fields, TensorFlow is primarily used for deep learning in practice and research.
