The open-source framework TensorFlow is a practical companion in the areas of machine learning and deep learning, which was developed directly by Google. At the time, it was still intended for internal purposes far away from the public, but today it is indispensable for developers. But how exactly did the framework manage to become such a popular solution in just a few years and what are the advantages of the practical framework called TensorFlow?
Inhaltsverzeichnis
What is TensorFlow?
In general terms, TensorFlow is a software framework dedicated to the computation of data flow diagrams. This involves the concrete description of machine learning algorithms in order to realise various deep learning models in operation.
A look at the concrete functions of TensorFlow shows that the framework is based on data flow-oriented programming. The so-called data flow graph has several nodes at this point, which are connected to each other by individual edges. Both the modelled creation of the graphs and their execution can be implemented with TensorFlow. The architecture of the framework shows in more detail which options are available.
The software framework can be used in a wide variety of environments. The framework supports the development of analytical applications for desktop devices, on the web, in the cloud or in the mobile sector. Here, the individual models can be trained on many computing units to accelerate machine learning. Units such as CPU, GPU or TPU can be deepened at any time during the application to promote a smooth application.
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TensorFlow for mobile devices
The productive use of TensorFlow also becomes possible on mobile devices without restrictions. The TensorFlow 2.0 version offers users four central components, including TensorFlow Lite. With this, models can be made available specifically on mobile devices, for example to enable concrete predictions on the basis of large amounts of data.
Models can be created for iOS as well as for ARM64 and Raspberry Pi. The concept is based on an interpreter and a converter. While the interpreter executes the models on numerous types of hardware, the converter ensures greater efficiency. The models are converted into a more efficient format in order to make them usable for the interpreter. This increases performance and makes it possible to create automated sequences even with pre-trained models.
Programming languages in competition with TensorFlow
In order to be able to work in the field of machine learning and AI, there are several alternative programming languages and frameworks in addition to TensorFlow. These include Keras, Pytorch, Theano and Caffe, for example. Nevertheless, TensorFlow is characterised by excellent documentation, which is mainly due to the prominent developer Google. The high usage values also ensure more content diversity, which means that there are now numerous tutorials, books and guides. In this way, the framework remains learnable for everyone themselves.
The areas of application
In the meantime, TensorFlow is present as a programming language in many areas when it comes to automating important processes. Three categories in particular can be divided at this point in order to define the application areas of TensorFlow as precisely as possible:
Open Source Machine Learning Platform
As open source Machine Learning platform, TensorFlow enables precise training of the desired models. The best known models include BERTa natural language recognition solution and ResNet for image recognition. The large amount of predefined data for training, in combination with the open source approach, creates the possibility to provide for enhancements oneself. In this way, specific development does not become a problem.
Machine learning and AI
With TensorFlow Enterprise, an in-house cloud offering has been created that is fully dedicated to machine learning. This makes it easy to further develop new applications in order to also benefit from machine learning in an entrepreneurial context. Artificial intelligence and automatic recognition. TensorFlow Enterprise makes it even easier for developers to build reliable, high-level AI applications for any business.
NLP
For the creation of scalable algorithms, TensorFlow is also a suitable solution. By creating efficient natural language processing systems that can use both textual content and acoustic signals, internal processes can be made much more specific. In the area NLP the algorithms can therefore be comprehensively trained on the basis of integrated data to achieve the best result for new projects.
The advantages
One of the biggest advantages is the enormous performance. This makes the framework a popular choice for machine learning and in the field of artificial intelligence for any size of project. Above all, the ability to develop your own models and display individual data flow graphs sets the solution apart from the competition. These usually only deliver prefabricated models that do not correspond 100 % to the actual application.
Above all, the four central components of TensorFlow enable fast and precise development. While TensorFlow Core is an open source library for training modern machine learning models, TensorFlow.js is a practical JavaScript library. This means that models can also be trained on Node.js and in the browser. TensorFlow Lite, on the other hand, is ideally suited for mobile devices. As a fourth component, the framework also offers a platform for experts, which provides professional environments as TensorFlow Extended.
Also extremely practical are the many application possibilities that are made available with TensorFlow. For example, the functions can be used to develop for smartphones, for desktop PCs, for servers or even for distributed systems. Thanks to the runnability of the framework, no translation into other languages is required despite the numerous environments. This saves you the effort of creating new content for each platform.
TensorFlow as an effective software framework
If you are looking for a freely customisable and professional solution with an interface to Python is guaranteed to be satisfied with TensorFlow. The numerous operations make it easy to decide on the appropriate applications and to create a concrete data flow graph. By adjusting the individual variables, a suitable picture can then be drawn, which forms the basis for AI-supported programming and development.
In the future, the framework will continue to receive updates and new versions that simplify modern and sector-specific programming. As things stand, however, no other framework comes close to the enormous performance, especially due to the many options and the efficient documentation. Convince yourself and choose TensorFlow for your project!
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