TensorFlow: The past, present, and future
Google felt the need for a deep neural network when it noticed that deep learning started to outperform all other machine learning algorithms when given a colossal amount of data. As an answer, Google created TensorFlow — a machine learning framework that makes the course of acquiring data, training models, serving predictions, and refining future results far easier. Google uses machine learning in all of its products to better translation, searches, recommendations, and image captioning.
There are three parts to Tensorflow architecture — processing the data, building a model, and finally train and estimate the model. It uses the popular Python programming language. Tensorflow lets developers create dataflow graphs that can be defined as a series of processing nodes or structures that describe how data moves through a graph. Because it takes input as a multi-dimensional array, also known as tensors it is called Tensorflow. Python objects are nodes and tensors in TensorFlow. While TensorFlow applications are themselves Python applications; the math operations are not performed in Python. Python merely directs the traffic between the pieces and offers high-level programming abstractions to hook them together. When it comes to computing the matrix multiplication TensorFlow is very fast as it is written in C++.
Built with production in mind, one of the main reasons why TensorFlow is so popular is because it has the best library accessible to all. It incorporates different APIs. TensorBoard is another significant feature of TensorFlow. TensorBoard allows you to monitor the visual and graphical aspects of TensorFlow. It also helps to debug the program. Not only can it run on almost any target like CPUs or GPUs, local machines, mobile servers, or clusters in the cloud, it can also run on Google’s custom TensorFlow Processing Unit (TPU) silicon for further acceleration. As compared to the other deep learning frameworks Tensorflow attracts the largest popularity on GitHub.
The biggest advantage of TensorFlow is an abstraction. The machine learning framework allows the developer to focus on the overall logic of the app while taking care of the nitty-gritty in the background. Some examples of TensorFlow applications include speech recognition systems, self-driving cars, image and video recognition and tagging, text summarization, and sentiment analysis.
TensorFlow Dev Summit 2020 launched the new update — TensorFlow 2.2. The main features of this update included an emphasis on performance, better compatibility with the rest of the TensorFlow ecosystem, and stability in the core library. The Performance Profiling tool was released in Tensorflow 2.x. The tool creates automated performance guidance and suggestions for improving model performance. Thus the tool will assist in enhancing the productivity of performance engineers. Several other updates like new features in TensorFlow Lite and TensorFlow.js, and exciting libraries introduced in TensorFlow, including tensorboard.dev and TFRT.
TensorFlow has many other competing machine learning frameworks. The three major frameworks that address similar needs are PyTorch, CNTK, and MXNet. However, TensorFlow still remains the AI modeling framework to beat. Its users are working in an astounding range of refined projects that push AI boundaries from all the directions. The TensorFlow ecosystem constantly gets updated with new tools, extensions, and add-ons to meet this demand.
TensorFlow is widely used by startups, large companies as well as academics. Google uses it on all its daily products like Google search, Gmail, and Photo.