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TensorFlow Basics

TensorFlow is an open-source software library for machine learning developed by Google. It provides a flexible and powerful platform for building and deploying machine learning models, including deep neural networks (DNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). TensorFlow provides a variety of tools for training and deploying machine learning models, including a computation engine, a collection of pre-built and user-defined operations (called “ops”), and a library of machine learning algorithms.

TensorFlow allows developers to define, optimize, and efficiently compute mathematical expressions involving multi-dimensional arrays (tensors) with a great level of abstraction. It can be used for a wide range of tasks, such as image and speech recognition, natural language processing, and time series forecasting.

One of the key features of TensorFlow is its ability to run on a variety of platforms, including CPUs, GPUs, and TPUs (Tensor Processing Units), which are specialized hardware designed to accelerate machine learning workloads. This allows developers to easily scale their models to handle large datasets and perform complex computations.

TensorFlow also provides a number of pre-built models and libraries, such as the TensorFlow Object Detection API and the TensorFlow Lite library for deploying models on mobile and embedded devices. Additionally, TensorFlow has a large and active community that contributes to the development and maintenance of the library and provides support to users.