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TensorFlow is an open-source collection of tools and libraries that helps developers build and train deep learning models.
TensorFlow uses a dataflow graph to represent computations.
It shares this space with another open-source machine-learning framework called PyTorch.
TensorFlow applications can run on either conventional CPUs or GPUs.
Uses for TensorFlow
TensorFlow has many applications in different industries.
This technology has applications in fields like medical image analysis, and autonomous driving.
Not surprisingly, many digital assistants are based on models trained using TensorFlow.
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.
Its widely used in predicting stock prices, weather forecasting, and such.
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.
Open Source:TensorFlow is open source, which means its accessible to AI developers all over the world.
Being open source also helps foster trust and transparency.
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.
Tensors:As its name suggests Tensors are a crucial aspect of TensorFlow.
Think of a tensor as a multi-dimensional array.
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.
Graphs:One of the reasons for TensorFlows popularity is its graph-based architecture.
What is TensorFlow Lite?
It is tuned for speed and optimizes power consumption to run efficiently in devices with limited hardware resources.
Like TensorFlow, LiteRT is also open source.
