ProjectBlog: How TensorFlow 2.0 makes Deep Learning Development More Efficient

Blog: How TensorFlow 2.0 makes Deep Learning Development More Efficient

TensorFlow is an open-source machine-learning framework designed for executing high-performance numerical computation. TensorFlow 2.0 offers the best architecture support, which allows the smooth deployment of computation on different platforms. These range from desktop, server cluster, mobile, and edge devices. At present, more than 6000 open source repositories use TensorFlow 2.0 in different research and real-world applications.

How TensorFlow 2.0 Will Effect Deep Learning Efficiency?

With the in-depth research of live cases becoming a reality, programmers and developers have witnessed a massive change in preference for TensorFlow 2.0. Its popularity measured by choice of AI developers in choosing TensorFlow as the first option. Apart from this, big companies including NVIDIA, Twitter, Uber, and Snapchat use the application to execute all significant operations.

Given below is to how TensorFlow 2.0 will help deep learning become efficient.

1. Support for Mobile, Edge, Web and Embedded Device

Google TensorFlow 2.0 offers a wide variety of services and modules in its system. It has made it as one of the best end-to-end tools for providing deep learning for multiple platforms including mobile, web, edge, and embedded devices.

2. TensorFlow.JS for Machine Learning

The TensorFlow 2.0 JavaScript library offers training and deployment of machine learning models in a web browser. It provides intuitive APIs for creating and training new and pre-existing models from scratch inside the browser or under the Node.JS file. Thus, it offers an excellent option to those working on artificial intelligence.

3. TensorBoard for Visual Debugging

During the training of a complex neural network, the computation utilized in TensorFlow might appear confusing. TensorFlow 2.0 makes it easier to understand and debug programs in the form of visuals. It allows developers and programmers to inspect and understand how TensorFlow runs and graphs.

4. TensorFlow Lite for Mobile and Embedded ML

The TensorFlow Lite refers to a lightweight solution for mobile and embedded devices. It is fast and enables on-device machine learning inference with low latency. The program offers support for hardware acceleration with Android Neural Network API. Upcoming updates for TensorFlow Lite would include many performance improvements, built-in operators, and support for more mobile and embedded devices. Thus, TensorFlow 2.0 would further simplify the experience of the developer in bringing machine learning service within more mobile devices.

5. TensorFlow Hub for Machine Learning

The TensorFlow Hub refers to a library. When you plan to reuse machine-learning models, then TensorFlow Hub is a good option. Developers and programmers can quickly transfer learning by reusing parts of machine learning models.

TensorFlow 2.0 Architecture

6. TensorFlow Eager Execution

The eager execution for TensorFlow is an imperative programming environment, which evaluates operations without the need for graphs. It makes it easier to get started with TensorFlow and debug models. In short, the eager execution is a flexible machine-learning platform for research and experimentation. It includes a natural control flow, intuitive interface, and easy debugging.


It is in this way in which the TensorFlow 2.0 would help make the development of deep learning efficient.

You can learn more about TensorFlow 2.0 from online learning sources including Udemy for a more in-depth understanding.

Source: Artificial Intelligence on Medium

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