TensorFlow is a free, open-source software library designed to work with machine learning and artificial intelligence. It is used for a wide variety of tasks but is centered on working with training and interference of deep neural networks. It is a symbolic math library that works on the foundation of Google’s Dataflow and differentiable programming. It is built by the Google Brain team for internal Google use to help with research and production. 

The TensorFlow is capable of running on multiple CPUs and GPUs along with optional CUDA and SYCL extensions for general-purpose computing on graphics processing units. At the same time, the other reference implementations only execute on a single window. The tool is compatible with 64-bit Linux, macOS, Windows and mobile computing platforms, including both Android and iOS. 

TensorFlow offers a flexible architecture that allows the developers for the easy deployment of the computation in a variety of platforms like CPUs, GPUs, and TPUs. It also facilitates deployment from desktops to clusters of servers to mobile and edge devices. 

TensorFlow computations are displayed as stateful Dataflow graphs. Neural networks execute on multidimensional data arrays that are referred to as tensors. Kubeflow performs operations and deployment of TensorFlow on Kubernetes. TensorFlow.js version 1.0 supports machine learning in JavaScript, while TensorFlow Graphics is designed by Google for deep learning in computer graphics. 

Tensor Processing Unit (TPU), an application-specific integrated circuit (ASIC), is a hardware chip primarily developed for machine learning and designed for TensorFlow. TPU is a programmable AI accelerator built to ensure high throughput of low-precision arithmetic like 8-bit. It is aligned to use or run models instead of training them. Google has been running TPUs in its data centers for more than a year. It also offers an order of magnitude, optimized performance per watt for machine learning. 

Google also deployed the second generation and also the availability of TPUs in Google Computing Engine. The second-generation TPUs have the capacity to deliver up to 180 teraflops of performance. When they are ordered into clusters of 64 TPUs, it provides up to 11.5 petaflops. 

The third-generation TPUs developed by Google deliver upto 420 teraflops of performance and 128 GB high bandwidth memory (HBM). The cloud TPU v3 pods provide 100+ petaflops of performance and 32 TB HBM. TPUs are available in beta on Google's cloud platform. 

Google’s Edge TPU is a purpose-built ASIC chip developed to run TensorFlow Lite machine learning models on small client computing tools like smartphones and tablets, called edge computing. 

TensorFlow offers a stable Python for version 3.7 to all the platforms and the C APIs. The API does not guarantee backward compatibility for C++, Go, Java, JavaScript, and Swift, which is currently archived, and the deployment has ceased. Third-party packages like Haskell, Julia, MATLAB, R, Scala, Rust, OCaml, Crystal, and C# are available in TensorFlow. The new language support is built on C API, but not all functions are available in C. A few functions are available in Python. 

The automated image capturing software like DeepDream is one of the applications which has its base foundation as TensorFlow. 

TensorFlow is a tool that helps in training and modeling to work with image patterns. The free TensorFlow course offered by Great Learning will help you understand the subject and its application in machine learning and deep neural networks. The tutorial will take you through various real-time application examples. At the end of the program, you will also be able to apply it to other problems and derive the solution for the same. You will earn a certificate after the successful completion of the course. We wish you Happy Learning!