User:ElliottKau/TFSandbox

This is the lead section for TFSandbox, the sandbox for the TensorFlow Article

AutoDifferentiation
AutoDifferentiation is the process of automatically calculating the gradient vector of a model with respect to each of its parameters. With this as a feature, TensorFlow can automatically compute the gradients for the parameters in a model, which proves useful in algorithms such as Back Propagation. To do so, the framework must keep track of the order of operations done to the input Tensors in a model, and then compute the gradients with respect to the appropriate parameters.

Losses
For training models, as well as potentially assessing their performance, TensorFlow provides a set of popular loss functions (also known as cost functions). Some of these losses include mean squared error (MSE), binary cross entropy (BCE), and Possion loss. The purpose of these loss functions is to compute the “error” or “difference” between a model’s output and the expected output (more broadly, the difference between two tensors). For different datasets and models, different losses are used to prioritize certain aspects of performance.

Optimizers
TensorFlow offers a breadth of popular optimizers for training Neural Networks including ADAM, ADAGRAD, and Stochastic Gradient Descent (SGD). When training a model, different optimizers offer different modes of parameter updating often affecting the way a model converges and its performance.

TensorFlow
TensorFlow serves as the core platform and library for machine learning. TensorFlow’s APIs use Keras to allow users to make their own machine learning models. In addition to building and training their model, TensorFlow can also help load the data to train the models, as well as deploy the models using TensorFlow serving.

TensorFlow provides stable Python (for version 3.7 across all platforms) and C APIs; and without API backwards compatibility guarantee: C++, Go, Java, JavaScript and Swift (archived and development has ceased). Third-party packages are available for C#, Haskell, Julia, MATLAB, R, Scala, Rust, OCaml, and Crystal.

TensorFlow.js
TensorFlow also has a library for machine learning in JavaScript. Using the provided JavaScript APIs, TensorFlow.js allows users to use either Tensorflow.js models or converted models from TensorFlow or TFLite, retrain the given models, and run on the web.

TFLite
TensorFlow Lite has APIs for mobile apps or embedded devices to generate and deploy TensorFlow models. These models are compressed and optimized in order to be more efficient and have a higher performance on smaller capacity devices.

TFX
TensorFlow Extended (abbrev. TFX) provides numerous components to perform all the operations needed for end-to-end production. Components include loading, validating, and transforming data, tuning, training, and evaluating the machine learning model, and pushing the model itself into production.

Extensions
TensorFlow also offer a variety of libraries and extensions to advance and extend the models and methods used. For example, TensorFlow Recommenders and TensorFlow Graphics are libraries for functionalities in recommendation systems and graphics, respectively, the TensorFlow Federated library provides a framework for decentralized data, and TensorFlow Cloud allows users to directly interact with Google Cloud to integrate their local code to Google Cloud. Other add-ons, libraries, and frameworks include TensorFlow Model Optimization, TensorFlow Probability, TensorFlow Quantum, and TensorFlow Decision Forests.

Medical
GE Healthcare used TensorFlow to increase the speed and accuracy of MRIs in identifying specific body parts. Google used TensorFlow to create DermAssist, a free mobile application that allows users to take pictures of their skin and identify potential health complications. Sinovation Ventures used TensorFlow to identify and classify eye diseases from optical coherence tomography (OCT) scans.

Social media
Twitter implemented TensorFlow to rank tweets by importance for a given user, and changed their platform to show tweets in order of this ranking. Previously, tweets were simply shown in reverse chronological order. The photo sharing app VSCO used TensorFlow to help suggest custom filters for photos.

Education
InSpace, a virtual learning platform, used TensorFlow to filter out toxic chat messages in classrooms. Liulishuo, an online English learning platform, utilized TensorFlow to create an adaptive curriculum for each student.. TensorFlow was used to accurately assess a student’s current abilities, and also helped decide the best future content to show based on those capabilities.

Retail
The e-commerce platform Carousell used TensorFlow to provide personalized recommendations for customers. The cosmetics company ModiFace used TensorFlow to create an augmented reality experience for customers to test various shades of make-up on their face.