What is TensorFlow?
TensorFlow is an open-source platform for running ML, DL, and other statistical and predictive analytics workloads developed by Google researchers. It’s aimed, like previous platforms, to make designing and implementing sophisticated analytics applications easier for users like data scientists, statisticians, and predictive modelers.
TensorFlow is a program that works with data sets that are graphed as computational nodes. Tensors are created by connecting the nodes of a network with edges that can represent multidimensional vectors or matrices. TensorFlow programs are well suited to large-scale parallel processing applications, such as neural networks, since they employ a data flow architecture that works with generalized intermediate outputs of computations.
Both high-level and low-level APIs are included in the framework. When feasible, Google suggests choosing the high-level ones to make data pipeline building and application programming easier. Knowing how to use the low-level APIs, known as TensorFlow Core, might be useful for application testing and troubleshooting.
TensorFlow applications may be executed on either standard CPUs or high-performance graphics processing units (GPUs), as well as Google’s own tensor processing units (TPUs), which are unique hardware created specifically to speed up TensorFlow workloads.
TensorFlow follows in the footsteps of DistBelief, a closed-source Google framework for unsupervised feature learning and deep learning applications based on extremely large neural networks and the backpropagation method, which the firm utilized internally.
TensorFlow is distinct from DistBelief in several aspects. TensorFlow’s code was more easily transferable for other users since it was built to run independently of Google’s own computational infrastructure. It’s also a broader machine learning framework than DistBelief, with less of a focus on neural networks. It’s also built to facilitate speedier configuration and to work with high-level APIs.
Advantages of TensorFlow
TensorFlow’s most important feature for ML development is abstraction. The developer may focus on the overarching logic of the program rather than the nitty-gritty details of developing algorithms or finding out correct methods to hook the result of one function to the input of another. Behind the scenes, TensorFlow takes care of the specifics.
TensorFlow provides extra features for developers that need to debug and inspect TensorFlow applications. Instead of generating the whole graph as a single opaque object and evaluating it all at once, the eager execution mode allows you to evaluate and change each graph action independently and transparently. Through an interactive, web-based interface, the TensorBoard visualization package allows you to analyze and profile the way graphs perform.
TensorFlow also benefits from Google’s sponsorship, which gives it a lot of advantages. Google has not only accelerated the development of TensorFlow but has also created a number of significant offerings around the framework that make it easier to deploy and use: the aforementioned TPU silicon for accelerated performance in Google’s cloud; an online hub for sharing models created with the framework; in-browser and mobile-friendly incarnations of the framework; and much more.
TensorFlow is a machine learning framework that competes with a plethora of others. Three prominent frameworks, CNTK, MXNet and PyTorch serve many of the same objectives.
- CNTK – employs a graph structure to represent data flow, similar to TensorFlow, although it focuses on deep learning neural networks. CNTK is quicker at handling numerous neural network tasks and offers a wider range of API, but CNTK is not as simple to understand or use as TensorFlow.
- MXNet – it can grow virtually linearly across numerous GPUs and servers. It supports many program languages but the same as CNTK. It’s harder to use than TensorFlow.
PyTorch- is the most comparable with TensorFlow, including hardware-accelerated components beneath the hood, a highly interactive development paradigm that allows for design-as-you-go work, and a large number of pre-installed components. TensorFlow wins out for larger projects and more sophisticated processes. PyTorch is a better choice for the quick development of projects that need to be up and running in a short amount of time.
