Search Results for "computations"

...The Jupyter notebook combines two components. A web application, which is a browser-based tool for interactive authoring of documents which combine explanatory text, mathematics, computations and their rich media output. And Notebook documents, which are a representation of all content visible in the web application, including inputs and outputs of the computations, explanatory text, mathematics, images, and rich media representations of objects. Notebook documents contain the inputs and outputs of a interactive session as well as additional text that accompanies the code but is not meant for execution.

...Available across all common operating systems (desktop, server and mobile), TensorFlow provides stable APIs for Python and C as well as APIs that are not guaranteed to be backwards compatible or are 3rd party for a variety of other languages. The platform can be easily deployed on multiple CPUs, GPUs and Google's proprietary chip, the tensor processing unit (TPU). TensorFlow expresses its computations as dataflow graphs, with each node in the graph representing an operation. Nodes take tensors—multidimensional arrays—as input and produce tensors as output. The framework allows for these algorithms to be run in C++ for better performance, while the multiple levels of APIs let the user determine how high or low they wish the level of abstraction to be in the models produced. ...

The Earth Engine API provides Python and JavaScript client libraries for Google Earth Engine, a planetary-scale geospatial analysis platform. With it, users compose lazy, server-side computations over massive catalogs of satellite imagery and vector datasets without handling raw files locally. The API exposes functional operators for map algebra, reducers, joins, and machine learning that scale transparently on Earth Engine’s backend. Developers authenticate once, work interactively in notebooks or the Code Editor, and export results to Cloud Storage, Drive, or asset collections. ...

...All changes to and uses of your data are tracked at runtime, building a dependency tree that captures all relations between state and output. This guarantees that computations depending on your state, like React components, run only when strictly needed. There is no need to manually optimize components with error-prone and sub-optimal techniques like memoization and selectors.

CrypTen is a research framework developed by Facebook Research for privacy-preserving machine learning built directly on top of PyTorch. It provides a secure and intuitive environment for performing computations on encrypted data using Secure Multiparty Computation (SMPC). Designed to make secure computation accessible to machine learning practitioners, CrypTen introduces a CrypTensor object that behaves like a regular PyTorch tensor, allowing users to seamlessly apply automatic differentiation and neural network operations. Its design mirrors PyTorch’s modular and library-based structure, enabling flexible experimentation, debugging, and model development. ...

...Prepack is still in an early development stage and not ready for production use just yet. Please try it out, give feedback, and help fix bugs. Prepack is a tool that optimizes JavaScript source code: Computations that can be done at compile-time instead of run-time get eliminated. Prepack replaces the global code of a JavaScript bundle with equivalent code that is a simple sequence of assignments. This gets rid of most intermediate computations and object allocations. Prepack operates at the AST level, using Babel to parse and generate JavaScript source code. ...

...ReactiveX's operators often reduce what was once an elaborate challenge into a few lines of code. Traditional try/catch is powerless for errors in asynchronous computations, but ReactiveX is equipped with proper mechanisms for handling errors.