TensorFlow is an open-source collection of tools and libraries that helps developers build and train deep learning models.
It has become one of the most widely used software frameworks since it can help build complex Artificial Intelligence (AI) models relatively quickly and easily.
Jad Khalife, Director of Sales Engineering, Middle East & Turkey, at Dataiku, says one of the features that make Tensorflow suitable for machine learning is that it’s an end-to-end framework that offers everything from data preprocessing to model deployment.
TensorFlow uses a dataflow graph to represent computations. It shares this space with another open-source machine-learning framework called PyTorch.
Developed and released by the Google Brain Team in November 2015, the framework received a major update in 2019 in the form of TensorFlow 2.0.
TensorFlow applications can run on either conventional CPUs or GPUs. Furthermore, Google Cloud users can run TensorFlow on Google’s own TensorFlow Processing Unit (TPU) chips, which are designed to speed up TensorFlow tasks.
Uses for TensorFlow
TensorFlow has many applications in different industries. It has been used by AirBnB to improve guest experience, by Airbus to detect anomalies in ISS telemetry data, by NASA to hunt for new planets, and to fight illegal deforestation.
Among its most important uses are:
Image recognition: This is one of the most popular uses of TensorFlow. Developers can leverage TensorFlow’s pre-trained models or build their own to identify and classify objects within digital images and videos. This technology has applications in fields like medical image analysis, and autonomous driving.
Natural Language Processing (NLP): Developers can use TensorFlow to process and analyze large volumes of textual data. This helps automate language understanding and generation, enabling developers to create chatbots, language translation systems, sentiment analysis tools, and other such NLP-based systems. Not surprisingly, many digital assistants are based on models trained using TensorFlow.
Reinforcement learning: Reinforcement learning (RL) involves an agent that learns to make decisions by interacting with an environment, through trial and error. TensorFlow can be used for this task through its library called TensorFlow Agents (TF-Agents), which provides a framework for building and training RL agents. This is particularly useful in fields such as robotics where TensorFlow can help develop models that enable robots to perceive and interact with their environment, improving tasks like navigation.
Generative Adversarial Networks (GANs): TensorFlow bundles a library called TF-GAN that allows developers to easily implement GANs. This comprehensive library simplifies the setup and training of GAN models. These models can then be used for tasks like generating all kinds of realistic media.
Time Series analysis: TensorFlow provides several methods and models for time series analysis and forecasting. This comes in handy to forecast outcomes, detect anomalies, and for financial modeling. It’s widely used in predicting stock prices, weather forecasting, and such. Recommendation engines, such as those used by Netflix, are one of the most common use cases for time series.
Advantages of TensorFlow
TensorFlow offers several advantages that make it a popular choice for developing and deploying machine learning models. Here’s why it’s the preferred choice for many AI developers:
Scalability: TensorFlow is designed to be scalable, which allows it to work efficiently across various devices, from mobile phones to high-end servers. It can also easily handle large datasets and computations, whether on a local machine, distributed across multiple machines, or in a cloud environment.
Support for multiple devices: TensorFlow supports multiple devices, such as CPUs, GPUs, and TPUs. This capability allows models created with TensorFlow to be deployed easily across different platforms without rewriting code.
Parallelism: By distributing its workload across multiple processors or machines, TensorFlow can significantly reduce the time required to train models. This is particularly useful when working with large datasets and complex models that would otherwise take a long time to train on a single device.
Open Source: TensorFlow is open source, which means it’s accessible to AI developers all over the world. Being open source also helps foster trust and transparency. Backed by Google, TensorFlow also has a very active and vibrant community of developers, data scientists, and engineers who work together to modify and extend the framework and provide support.
Greater developer control: Although TensorFlow uses Python as a front-end API for building applications with the framework, it offers wrappers in several other programming languages including C++ and Java. This means developers can train and deploy machine learning models regardless of the programming language or platform.
Extensive ecosystem: TensorFlow boasts a rich ecosystem of libraries and tools to help make development faster and easier. This includes TensorFlow Lite for mobile and embedded devices, TensorFlow.js for web-based applications, the TensorFlow Hub repository of pre-trained models, and a lot more.
TensorFlow components
There are a few key components in TensorFlow that help facilitate its functionality as one of the leading machine-learning libraries.
Tensors: As its name suggests Tensors are a crucial aspect of TensorFlow. Think of a tensor as a multi-dimensional array. In TensorFlow, all data is represented as tensors, which are the primary data structures that are used to represent and manipulate data in TensorFlow.
Flows: This is the other critical aspect of TensorFlow. As we know, TensorFlow accepts input in the form of tensors. This input passes through a series of steps. The term “flow” refers to this movement of data through the various stages of model training or inference.
Graphs: One of the reasons for TensorFlow’s popularity is its graph-based architecture. All operations in TensorFlow are depicted and executed inside a graph, which helps define how data is processed in the model.
TensorBoard: TensorBoard is a visualization tool that helps developers track, and understand the training of machine learning models in TensorFlow. It is primarily used for monitoring and debugging the machine learning models and provides insights into how the models are learning and performing.
What is TensorFlow Lite?
While TensorFlow is a wonderful library to train and infer machine learning models, it requires powerful CPUs, GPUs, or TPUs to work its magic. In 2017, Google released TensorFlow Lite to enable developers to bring machine learning-powered experiences to mobile and embedded devices.
Now called LiteRT, TensorFlow Lite allows developers to deploy machine learning models on devices with limited computational resources, such as smartphones, tablets, and other IoT devices.
“[TensorFlow Lite] enables efficient inference with minimal computational resources, making it ideal for real-time and low-latency machine learning applications,” says Khalife.
It is tuned for speed and optimizes power consumption to run efficiently in devices with limited hardware resources. Models created with TensorFlow Lite are lightweight enough to be deployed on embedded devices, like the Raspberry Pi, and at the edge. Like TensorFlow, LiteRT is also open source.