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TensorFlow is mainly used to train and run (infer with) neural networks.
TensorFlow is a machine learning software library designed primarily for training and inference of neural networks. In training, it supports the full workflow needed to optimize model parameters, including automatic differentiation for computing gradients, loss functions (cost functions) to measure prediction error, and optimizers such as Adam, Adagrad, and SGD to update parameters. It also provides metrics to evaluate model performance during training and validation. For inference, TensorFlow enables deploying trained models to make predictions on new data. Its flexible architecture supports running computations across many hardware and software environments (e.g., CPUs, GPUs, and specialized accelerators like TPUs), and it can be deployed from desktops and server clusters to mobile and edge devices. TensorFlow also offers deployment-focused variants and tools—such as TensorFlow Serving for deployment and TensorFlow Lite/LiteRT for efficient on-device inference—so models can be used in real-world applications after training.
TensorFlow is mainly used to train and run (infer with) neural networks.
Training support includes automatic differentiation, loss functions, metrics, and optimizers.
TensorFlow supports inference deployment across many platforms, including CPUs/GPUs and specialized accelerators, plus mobile/edge via LiteRT/TensorFlow Lite.
The process of adjusting a neural network’s parameters using data so the model learns to make accurate predictions.
Using a trained neural network model to generate predictions on new, unseen inputs.
TensorFlow’s ability to automatically compute gradients of a model with respect to its parameters.
A function that quantifies prediction error during training (e.g., MSE or binary cross-entropy) to guide optimization.
An algorithm (e.g., Adam, Adagrad, SGD) that updates model parameters based on computed gradients to improve performance.
Quantitative measures (e.g., accuracy, precision, recall, IoU) used to assess model performance.
A tool for deploying trained TensorFlow models for production inference.
A lightweight stack for running compressed, optimized TensorFlow models on mobile and embedded devices for efficient inference.
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