TensorFlow is a Python-friendly open-source library developed by Google for numerical computation which allows machine learning to be faster and easier. TensorFlow is also an entire ecosystem to help you solve challenging, real-world problems with machine learning. The increase of Artificial Intelligence (AI) and Deep Learning has propelled the growth of TensorFlow, an open-source AI library that allows for data flow graphs to build models. If you would like to pursue a career in AI, knowing the basics of TensorFlow is essential.
TensorFlow accepts data in the form of multi-dimensional arrays of higher dimensions called tensors. Multi-dimensional arrays are very handy in handling large amounts of data.
TensorFlow works on the basis of data flow graphs that have nodes and edges. As the execution mechanism is in the form of graphs, it is much easier to execute TensorFlow code in a distributed manner across a bunch of computers while using GPUs.
TensorFlow 2.0, released in October 2019, revamped the framework in many ways based on many feedback of users, to make it easier to work with (e.g., by using the relatively simple Keras API for model training) and bring more performance. Distributed training is easier to run thanks to a new API, and support for TensorFlow Lite makes it possible to deploy DL models on a greater variety of platforms. However, code written for earlier versions of TensorFlow must be rewritten in order to take maximum advantage of new TensorFlow 2.0 features.
TensorFlow Offers Both C++ and Python API’s
Before the development of libraries, the coding mechanism for ML and DL was much more complicated. This library provides a high-level API, as well as complex coding isn’t needed to prepare a neural network, configure a neuron, or program a neuron. The library completes all of these tasks, and TensorFlow also has integration with Java and R.
TensorFlow Supports Both CPUs and GPUs Computing Devices
Deep learning applications are very complicated, with the training process requiring a lot of computations. It takes a long time due to the large data size, and it involves several iterative processes, mathematical calculations, matrix multiplications, and so on. If you perform these activities on a normal CPU (Central Processing Units), typically it would take much longer.
Graphical Processing Units (GPUs) are popular in the context of games, where you need the screen and image to be of high resolution. GPUs were originally designed for this purpose. However, they are being used for developing deep learning applications as well. In Deep Learning, what we have to do is crunching together big matrices of numbers, instead of a single one. Humans know that what happens in GPU (Graphics Processing Units) is somehow the same as the way they deal with Deep Learning models where the matrices represent pixels. It is clear to say that computing graphics is about doing computations across big matrices of pixel data. It is the reason why Deep Learning needs GPUs: doing computations across big matrices.
One of the major advantages of TensorFlow is that it supports GPUs, as well as CPUs. It also has a faster compilation time than other deep learning libraries, like Keras and Torch.
Easy model building
Build and train ML models easily using intuitive high-level APIs like Keras with eager execution, which makes for immediate model iteration and easy debugging.
Robust ML production anywhere
Easily train and deploy models in the cloud, on-prem, in the browser, or on-device no matter what language you use.
Powerful experimentation for research
A simple and flexible architecture to take new ideas from concept to code, to state-of-the-art models, and to publication faster.
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