A neural network is synonymous to the neurons in our brains. It is often referred to as end-to-end learning. The data could be images, text, or sound. Why is TensorFlow Preferred in Deep Learning?ĭeep Learning is a part of Machine Learning that learns features and tasks directly from the data. In TensorFlow, computations are run only after the session is created. Inputs are fed into nodes through variables or placeholders. Once the graph is created, an inner loop is written to drive computation. So, this high-level abstraction shows how the data flows between operations. Someone interested in Machine Learning, especially neural networks, should learn TensorFlow.ĭata Flow Graph Architecture of TensorFlow:ĭata Flow graph has two basic units: A Node representing a mathematical operation and an edge that serves a multi-dimensional array known as tensors. Another great feature of TensorFlow is TensorBoard which enables us to monitor graphically and visually its work. TensorFlow is very fast because it’s written in C++, but it can be accessed and controlled by other languages, mainly Python. It could be trained on multiple machines, and then we could run it on a different machine. All of this could be done using a single API. It could be run on different platforms like desktop, or a cloud, or on a mobile device. It is popular because of its extreme versatility.
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