A high-performance, open-source learning framework for numerical computations, TensorFlow has become an incredibly popular computational software across almost all devices. TensorFlow rates highly amongst all AI frameworks and currently gets used in over 6000 open source repositories.
The Origin Story
Google Brain developed the library when looking for machine and deep learning support, as well as support for cross-domain calculations. It has been open source since 2015, and forums like GitHub have sent its popularity skyrocketing. It is updated frequently, beating down challenges as they arise and making things easier for users worldwide. It continually has updated promises, features, and performance matrices and APIs.
Open-sourcing the project has given the Google research team nigh-unprecedented insight from outside contributors and popularised the framework beyond what they could have otherwise managed.
Huge companies like Snapchat, Twitter, and others, are all employing TensorFlow. So what makes it stand out from the rest? It’s true that the origin story probably plays a part – Google’s name still holds cloud, after all, and it is invaluable in its marketing even though the framework is now open source.
However, there’s a lot that lets TensorFlow stand on its own, too. The key functionalities that follow are essential to its success over other similar frameworks. For example:
- It has a readable and easily accessible syntax for ease of use, making it easier for developers new and old to get to know well.
- Comparatively excellent high-end operations compared to other deep learning frameworks.
- It offers flexibility from a low-level library, giving users the ability to customize to their wants and needs. These parameters are invaluable for researchers.
- Network control, meaning everyone can be fully aware and up to date with changes and alterations.
Distributed Deep Learning
Experimentation time and processes get significantly reduced because tasks run across many GPUs at once. This process means that TensorFlow can learn a lot simultaneously, cutting processing and development times down from weeks to mere hours. TensorFlow uses two particular efficient distribution methods:
- Multiple server distribution for training
- Parallel experiments to create the right hyperparameters
The Democracy of Deep Learning
TensorFlow makes deep learning accessible by everyone, providing functions and operations that make neural networks easy to build and grow. The infrastructure and hardware make a field day for researchers and students alike.
TensorFlow Serving is also designed for production environments, helping promote flexible and efficient machine learning, and making model integration much more comfortable.
It also offers features like:
- TensorFlow Lite – for mobile and embedded devices
- TensorFlow Hub – for reusable machine learning
- TensorBoard – for easy visual debugging
- Sonnet – a DeepMind library built on top of TensorFlow to build complex neural networks.
The marketing of TensorFlow means that community progress is fast, with ten people per hour, contributing to the machine learning project worldwide. It has the most significant active community in the world.
Rapid updates mean that TensorFlow’s utilities and abilities will continue to expand and overcome challenges as they arise. Improvements to ease of use in the 2.0 version will only be the beginning as the framework continues to grow, both with investments from Google and with help from its worldwide community.