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We have dedicated team of TensorFlow experts to help you create your next machine learning project.
What is TensorFlow
TensorFlow makes it easy for beginners and experts to create machine learning models for desktop, mobile, web, and cloud. See the sections below to get started.
What is TensorFlow ecosystem
Here are list of technology and topics We offer in TensorFlow
For Mobile & IoT: Run inference with TensorFlow Lite on mobile and embedded devices like Android, iOS, Edge TPU, and Raspberry Pi.
For Production: Deploy a production-ready ML pipeline for training and inference using TensorFlow Extended (TFX).
Swift for TensorFlow: Integrate directly with Swift for TensorFlow, the next generation platform for deep learning and differentiable programming.
Looking to expand your ML knowledge?
TensorFlow is easier to use with a basic understanding of machine learning principles and core concepts. Learn and apply fundamental machine learning practices to develop your skills. Our expert are ready to help you.
TensorFlow Topics we cover:
Models & datasets
Official models : Official models and examples built with TensorFlow.
TensorFlow official datasets: A collection of datasets ready to use with TensorFlow.
Google research datasets: Explore large-scale datasets released by Google research teams in a wide range of computer science disciplines.
Additional dataset resources: Explore other datasets available to use with TensorFlow.
We offer following tools to support and accelerate TensorFlow workflows
CoLab: Colaboratory is a free Jupyter notebook environment that requires no setup and runs entirely in the cloud, allowing you to execute TensorFlow code in your browser with a single click.
TensorBoard: A suite of visualization tools to understand, debug, and optimize TensorFlow programs.
What-If Tool:A tool for code-free probing of machine learning models, useful for model understanding, debugging, and fairness. Available in TensorBoard and jupyter or colab notebooks.
ML Perf: A broad ML benchmark suite for measuring performance of ML software frameworks, ML hardware accelerators, and ML cloud platforms.
XLA: XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear algebra that optimizes TensorFlow computations. The results are improvements in speed, memory usage, and portability on server and mobile platforms.
TensorFlow Playground: Tinker with a neural network in your browser. Don’t worry, you can’t break it.
TensorFlow Research Cloud: The TensorFlow Research Cloud (TFRC) program enables researchers to apply for access to a cluster of more than 1,000 Cloud TPUs at no charge to help them accelerate the next wave of research breakthroughs.
MLIR: A new intermediate representation and compiler framework.
Libraries & extensions
Explore libraries to build advanced models or methods using TensorFlow, and access domain-specific application packages that extend TensorFlow.
Model Optimization : The TensorFlow Model Optimization Toolkit is a suite of tools for optimizing ML models for deployment and execution.
TensorFlow Graphics : A library of computer graphics functionalities ranging from cameras, lights, and materials to renderers.
TensorFlow Federated : An open source framework for machine learning and other computations on decentralized data.
Probability : TensorFlow Probability is a library for probabilistic reasoning and statistical analysis.
Tensor2Tensor : Tensor2Tensor is a library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.
TensorFlow Privacy: A Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy.
TensorFlow Agents : A library for reinforcement learning in TensorFlow.
Dopamine :A research framework for fast prototyping of reinforcement learning algorithms.
TRFL :TRFL (pronounced “truffle”) is a library for reinforcement learning building blocks created by DeepMind.
Mesh TensorFlow :A language for distributed deep learning, capable of specifying a broad class of distributed tensor computations.
RaggedTensors :Makes it easy to store and manipulate data with non-uniform shape, including text (words, sentences, characters), and batches of variable length.
Unicode Ops :Supports working with Unicode text directly in TensorFlow.
TensorFlow Ranking : TensorFlow Ranking is a library for Learning-to-Rank (LTR) techniques on the TensorFlow platform.
Magenta : Magenta is a research project exploring the role of machine learning in the process of creating art and music.
Nucleus : Nucleus is a library of Python and C++ code designed to make it easy to read, write and analyze data in common genomics file formats like SAM and VCF.
Sonnet : A library from DeepMind for constructing neural networks.
Neural Structured Learning :A learning framework to train neural networks by leveraging structured signals in addition to feature inputs.
TensorFlow Addons : Extra functionality for TensorFlow, maintained by SIG Addons.
Tensorflow I/O : Dataset, streaming, and file system extensions, maintained by SIG IO.
Machine learning In Education
When beginning your educational path, it’s important to first understand how to learn ML. We’ve helped the learning process that will uplevel your abilities, and prepare you to use ML for your projects. Start with our guided curriculums designed to increase your knowledge, or choose your own path by exploring our expert guide.
Coding skills: Building ML models involves much more than just knowing ML concepts—it requires coding in order to do the data management, parameter tuning, and parsing results needed to test and optimize your model.
Math and stats: ML is a math heavy discipline, so if you plan to modify ML models or build new ones from scratch, familiarity with the underlying math concepts is crucial to the process.
ML theory: Knowing the basics of ML theory will give you a foundation to build on, and help you troubleshoot when something goes wrong.
Build your own projects: Getting hands on experience with ML is the best way to put your knowledge to the test, so don’t be afraid to dive in early with a simple colab or tutorial to get some practice
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