# tensorflow/tfjs-core

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8,437 stars · 930 forks · TypeScript · Apache-2.0 · archived

## Links

- GitHub: https://github.com/tensorflow/tfjs-core
- Homepage: https://js.tensorflow.org
- awesome-repositories: https://awesome-repositories.com/repository/tensorflow-tfjs-core.md

## Topics

`deep-learning` `deep-neural-networks` `gpu-acceleration` `javascript` `machine-learning` `neural-network` `typescript` `webgl`

## Description

TensorFlow.js is a JavaScript machine learning library and browser-based runtime used to build, train, and execute models. It functions as a WebGL accelerated tensor engine, providing a foundation for high-performance linear algebra operations and an automatic differentiation framework for computing gradients.

The project distinguishes itself through its ability to run machine learning directly in web environments, supporting both client-side inference and browser-based training. It enables the deployment of Python-based models by converting Keras or TensorFlow models into compatible formats and provides native support for TFLite models via flatbuffers.

The library covers a broad surface of capabilities, including model construction and transfer learning, hardware-accelerated inference, and the management of model lifecycles. It also includes utilities for data preprocessing, media decoding from camera feeds and images, and a suite of visualization tools for monitoring training progress and model architecture.

## Tags

### Artificial Intelligence & ML

- [In-Browser ML Runtimes](https://awesome-repositories.com/f/artificial-intelligence-ml/in-browser-ml-runtimes.md) — Provides a browser-based runtime for executing pre-trained machine learning models including TFLite and Keras conversions.
- [Automatic Differentiation](https://awesome-repositories.com/f/artificial-intelligence-ml/automatic-differentiation.md) — Implements a full automatic differentiation framework for computing gradients during model training.
- [Automatic Differentiation Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/automatic-differentiation-frameworks.md) — Provides a comprehensive engine for computing exact gradients to support model training and optimization.
- [Browser-Based Deep Learning](https://awesome-repositories.com/f/artificial-intelligence-ml/browser-based-deep-learning.md) — Enables running pre-trained models or training new ones entirely within the web browser.
- [Client-Side Inference](https://awesome-repositories.com/f/artificial-intelligence-ml/client-side-inference.md) — Executes machine learning predictions directly on user devices to enable real-time responses.
- [Hardware-Accelerated Inference](https://awesome-repositories.com/f/artificial-intelligence-ml/hardware-accelerated-inference.md) — Executes loaded models using GPU-accelerated operations for real-time inference performance. ([source](https://js.tensorflow.org/api_tflite/0.0.1-alpha.9/))
- [In-Browser Model Execution](https://awesome-repositories.com/f/artificial-intelligence-ml/in-browser-model-execution.md) — Enables the execution of machine learning models directly in the web browser using JavaScript.
- [JavaScript Machine Learning Libraries](https://awesome-repositories.com/f/artificial-intelligence-ml/javascript-machine-learning-libraries.md) — Provides a complete library for implementing and training neural networks and ML models using JavaScript.
- [JavaScript Model Training](https://awesome-repositories.com/f/artificial-intelligence-ml/javascript-model-training.md) — Provides a full environment for developing and optimizing neural networks using JS and automatic differentiation.
- [Eager-Execution Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/model-training/eager-execution-frameworks.md) — Creates and optimizes machine learning models using an eager API for definition and training. ([source](https://js.tensorflow.org/))
- [Backend-Agnostic Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-research/neural-network-toolkits/backend-agnostic-engines.md) — Provides a backend-agnostic engine that decouples tensor operations from specific hardware like WebGL or WASM.
- [Tensor Initialization](https://awesome-repositories.com/f/artificial-intelligence-ml/tensor-initialization.md) — Initializes multidimensional tensors of varying ranks and shapes from arrays or hardware buffers. ([source](https://js.tensorflow.org/api/4.22.0/))
- [Pretrained Model Integrations](https://awesome-repositories.com/f/artificial-intelligence-ml/custom-model-training/pretrained-model-integrations.md) — Retrieves model architecture and weight shards from web servers to perform inference or continue training. ([source](https://js.tensorflow.org/tutorials/import-keras.html))
- [Eager Execution Modes](https://awesome-repositories.com/f/artificial-intelligence-ml/eager-execution-modes.md) — Supports an eager execution mode where operations are evaluated immediately as they are called.
- [FlatBuffer Model Interpreters](https://awesome-repositories.com/f/artificial-intelligence-ml/flatbuffer-model-interpreters.md) — Includes native support for TFLite models via FlatBuffers for efficient inference without a full framework.
- [Data Preprocessing](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/data-and-checkpointing/data-preprocessing.md) — Provides tools for cleaning, normalizing, and transforming raw data before it is used in model training. ([source](https://js.tensorflow.org/tutorials/))
- [Model Architecture Visualizations](https://awesome-repositories.com/f/artificial-intelligence-ml/model-architecture-visualizations.md) — TensorFlow.js displays tabular information about model architecture and detailed histograms of layer parameters. ([source](https://js.tensorflow.org/api_vis/1.5.1/))
- [Model Persistence](https://awesome-repositories.com/f/artificial-intelligence-ml/model-training/model-persistence.md) — Saves and retrieves trained model artifacts and topology to local storage or static resources. ([source](https://js.tensorflow.org/api_react_native/1.0.0/))
- [Weight Sharding](https://awesome-repositories.com/f/artificial-intelligence-ml/model-weight-management/weight-distribution/weight-sharding.md) — Retrieves large model parameters in fragmented binary shards to optimize network transfer and memory.
- [Training Progress Monitors](https://awesome-repositories.com/f/artificial-intelligence-ml/training-progress-monitors.md) — Produces real-time plots of metrics like loss and accuracy during model training via callbacks. ([source](https://js.tensorflow.org/api_vis/1.5.1/))
- [Transfer Learning](https://awesome-repositories.com/f/artificial-intelligence-ml/transfer-learning.md) — Implements techniques for adapting pre-trained models to new tasks by training them on new datasets. ([source](https://js.tensorflow.org/))

### Web Development

- [Hardware-Accelerated WebGL Execution](https://awesome-repositories.com/f/web-development/performance-optimizations/hardware-accelerated-webgl-execution.md) — Functions as a WebGL accelerated tensor engine that offloads linear algebra operations to the GPU.

### Scientific & Mathematical Computing

- [Linear Algebra](https://awesome-repositories.com/f/scientific-mathematical-computing/numerical-mathematical-foundations/linear-algebra.md) — Executes high-performance mathematical routines for vector and matrix operations on tensors. ([source](https://cdn.jsdelivr.net/gh/tensorflow/tfjs-core@master/README.md))

### Part of an Awesome List

- [Model Lifecycle Management](https://awesome-repositories.com/f/awesome-lists/ai/model-repositories/model-lifecycle-management.md) — Manages the loading of pre-trained models from URLs and the release of associated hardware resources. ([source](https://js.tensorflow.org/api_tasks/0.0.1-alpha.8/))

### DevOps & Infrastructure

- [TFLite Interpreters](https://awesome-repositories.com/f/devops-infrastructure/deployment-management/model-export-formats/tflite-interpreters.md) — Imports TFLite flatbuffers from URLs or memory buffers into an interpreter for execution. ([source](https://js.tensorflow.org/api_tflite/0.0.1-alpha.9/))

### Mobile Development

- [Web-Compatible Format Converters](https://awesome-repositories.com/f/mobile-development/model-format-optimizers/web-compatible-format-converters.md) — Transforms models from frameworks like Keras into compatible JSON and binary formats for web execution. ([source](https://js.tensorflow.org/tutorials/))