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tensorflow trading strategy development software

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Machine learning software library

TensorFlow

TensorFlow logo

Developer(s) Google Brain Team[1]
Initial release Novemberdannbsp;9, 2022; 6 years ago dannbsp;(2015-11-09)
Stable release

2.6.1[2]Edit this on Wikidata (1 November 2022; 2 months ago dannbsp;(1 Nov 2022)) / Maydannbsp;14, 2022; 7 months past dannbsp;(2021-05-14)

Repository github.com/tensorflow/tensorflow
Written in Python, C++, CUDA
Platform Linux, macOS, Windows, Android, JavaScript[3]
Case Machine erudition subroutine library
Licence Apache Licence 2.0
Website www.tensorflow.org

TensorFlow is a free and acceptive-rootage software library for car learning and AI. It can be used across a range of tasks but has a particular focus on grooming and inference of deep neural networks.[4] [5]

TensorFlow was developed past the Google Brain team for internal Google use in research and production.[6] [7] [8] The initial version was released under the Apache License 2.0 in 2022.[1] [9] Google released the updated version of TensorFlow, named TensorFlow 2.0, in September 2022.[10]

TensorFlow crapper cost used in a wide variety of computer programming languages, most notably Python, as fortunate as Javascript, C++, and Java.[11] This flexibility lends itself to a range of applications in many an different sectors.

History [edit]

DistBelief [blue-pencil]

Starting in 2011, Google Brain collective DistBelief as a proprietary machine eruditeness system based on wakeless learning neural networks. Its use grew speedily across different Alphabet companies in both research and technical applications.[12] [13] Google allotted multiple information processing system scientists, including Jeff Dean, to simplify and refactor the codebase of DistBelief into a faster, more healthy application-grade depository library, which became TensorFlow.[14] In 2009, the team, led away Geoffrey Hinton, had implemented unspecialised backpropagation and unusual improvements which allowed generation of neural networks with well higher accuracy, e.g. a 25% reduction in errors in lecture recognition.[15]

TensorFlow [edit]

TensorFlow is Google Brain's intermediate-generation system. Version 1.0.0 was released on Feb 11, 2022.[16] Spell the reference implementation runs on single devices, TensorFlow terminate run connected multiple CPUs and GPUs (with optional CUDA and SYCL extensions for general computing on graphics processing units).[17] TensorFlow is available along 64-bit Linux, macOS, Windows, and mobile computation platforms including Android and iOS.

Its flexible architecture allows for the relaxed deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices.

TensorFlow computations are expressed As stateful dataflow graphs. The name TensorFlow derives from the operations that such vegetative cell networks perform along multidimensional data arrays, which are referred to A tensors. During the Google I/O League in June 2022, Jeff James Byron Dean stated that 1,500 repositories on GitHub mentioned TensorFlow, of which exclusive 5 were from Google.[18]

In December 2022, developers from Google, Cisco, RedHat, CoreOS, and CaiCloud introduced Kubeflow at a conference. Kubeflow allows cognitive process and deployment of TensorFlow on Kubernetes.

In Border district 2022, Google proclaimed TensorFlow.js version 1.0 for machine encyclopaedism in JavaScript.[19]

In Jan 2022, Google proclaimed TensorFlow 2.0.[20] It became formally available in Sep 2022.[10]

In English hawthorn 2022, Google announced TensorFlow Graphics for deep eruditeness in computer nontextual matter.[21]

Tensor processing unit (TPU) [edit]

In Crataegus laevigata 2022, Google announced its Tensor processing unit (TPU), an practical application-specified integrated circuit (ASIC, a hardware knap) built specifically for machine learning and tailored for TensorFlow. A TPU is a programmable AI accelerator designed to provide high througHput of low-preciseness arithmetic (e.g., 8-bit), and oriented toward exploitation or running models rather than breeding them. Google announced they had been working TPUs at bottom their information centers for more than a year, and had found them to pitch an order better-optimized performance per watt for auto encyclopaedism.[22]

In May 2022, Google announced the second-generation, as well every bit the availability of the TPUs in Google Figure out Railway locomotive.[23] The second-generation TPUs deliver up to 180 teraflops of performance, and when organized into clusters of 64 TPUs, provide up to 11.5 petaflops.

In May 2022, Google announced the third-generation TPUs delivering raised to 420 teraflops of performance and 128 GB high bandwidth memory (HBM). Cloud TPU v3 Pods offer 100+ petaflops of performance and 32 Atomic number 65 HBM.[24]

In February 2022, Google announced that they were making TPUs available in beta happening the Google Cloud Platform.[25]

March TPU [edit]

In July 2022, the Edge TPU was announced. Edge TPU is Google's purpose-built ASIC chip designed to run TensorFlow Lite machine learning (Millilitre) models on young client computing devices such as smartphones[26] illustrious as edge computing.

TensorFlow Lite [edit]

In Crataegus laevigata 2022, Google announced a software spate specifically for mobile development, TensorFlow Lite.[27] In Jan 2022, TensorFlow squad released a developer preview of the peregrine GPU inference engine with OpenGL ES 3.1 Figure Shaders on Android devices and Metal Compute Shaders on iOS devices.[28] In May 2022, Google announced that their TensorFlow Lite Micro (also known every bit TensorFlow Lite for Microcontrollers) and ARM's uTensor would be coming together.[29]

Picture element Visual Kernel (PVC) [edit]

In October 2022, Google released the Google Pixel 2 which conspicuous their Picture element Visual Core (PVC), a fully programmable image, vision and AI processor for mobile devices. The Polyvinyl chloride supports TensorFlow for political machine learning (and Halide for image processing).

TensorFlow 2.0 [edit]

As TensorFlow's market part among inquiry papers was declining to the advantage of PyTorch,[30] the TensorFlow Team announced a release of a new Major version of the library in September 2022. TensorFlow 2.0 introduced many changes, the virtually significant being TensorFlow eager, which changed the automatic differentiation scheme from the static computational graphical record, to the "Delimit-by-Pass around" scheme to begin with made popular away Chainer and afterward PyTorch.[30] Other major changes included removal of old libraries, cross-compatibility between trained models on different versions of TensorFlow, and significant improvements to the performance on GPU.[31] [ non-primary source requisite ]

Features [edit]

AutoDifferentiation [edit]

AutoDifferentiation is the mental process of mechanically calculating the gradient vector of a framework with respect to each of its parameters. With this feature, TensorFlow can mechanically compute the gradients for the parameters in a model, which is useful to algorithms such every bit backpropagation which postulate gradients to optimise performance.[32] To do so, the framework must observe track of the order of operations done to the stimulant Tensors in a model, and then reckon the gradients with respect to the appropriate parameters.[32]

Eager execution [edit]

TensorFlow includes an "great carrying into action" mode, which way that operations are evaluated immediately as opposed to being added to a computational graphical record which is dead later.[33] Code executed eagerly can be examined step-by footstep-through and through a debugger, since data is augmented at apiece job of code rather than later in a computational graphical record.[33] This execution prototype is advised to be easier to debug because of its step out by step transparency.[33]

Pass around [delete]

In both eager and graph executions, TensorFlow provides an API for distributing computation crosswise multiple devices with various distribution strategies.[34] This distributed computing can often speed up the executing of training and evaluating of TensorFlow models and is a popular practice in the field of AI.[34] [35]

Losses [edit]

To train and evaluate models, TensorFlow provides a set of loss functions (also known arsenic cost functions).[36] Some popular examples include imply square misplay (MSE) and binary cross entropy (BCE).[36] These loss functions compute the "error" or "difference" between a manakin's output and the hoped-for end product (more broadly speaking, the difference 'tween two tensors). For different datasets and models, different losses are secondhand to prioritize certain aspects of performance.

Metrics [edit]

In order to assess the performance of machine learning models, TensorFlow gives API approach to usually put-upon metrics. Examples include various truth metrics (binary, categorical, sparse categorical) along with opposite prosody such A Preciseness, Recall, and Intersection-over-Union (IoU).[37]

TF.nn [edit]

TensorFlow.nn is a faculty for executing archaic neural network operations on models.[38] Some of these operations let in variations of convolutions (1/2/3D, Atrous, depthwise), activation functions (Softmax, RELU, GELU, Colon, etc.) and their variations, and other Tensor operations (max-pooling, predetermine-add, etc.).[38]

Optimizers [edit]

TensorFlow offers a go down of optimizers for training neural networks, including ADAM, ADAGRAD, and Random Slope Declination (SGD).[39] When training a modelling, different optimizers offer different modes of parameter tuning, often affecting a model's convergence and performance.[40]

Usage and extensions [redact]

TensorFlow [edit]

TensorFlow serves as the core political program and library for machine learning. TensorFlow's APIs use Keras to allow users to make their own machine learning models.[41] In addition to building and breeding their model, TensorFlow can also help load the data to train the mannikin, and deploy it victimization TensorFlow Serving.[42]

TensorFlow provides a stable Python API,[43] likewise as APIs without backward compatibility guarantee for Javascript,[44] C++,[45] and Coffee[46].[11] Third-company language binding packages are also accessible for C#,[47] [48] Haskell,[49] Julia,[50] MATLAB,[51] R,[52] Scala,[53] Chromatic,[54] OCaml,[55] and Crystal.[56] Bindings that are now archived and unsupported let in Go[57] and Swift.[58]

TensorFlow.js [blue-pencil]

TensorFlow besides has a subroutine library for machine learning in JavaScript. Using the provided JavaScript APIs, TensorFlow.js allows users to function either Tensorflow.js models or converted models from TensorFlow or TFLite, retrain the surrendered models, and keep going the web.[42] [59]

TFLite [edit]

TensorFlow Lite has Apis for mobile apps or embedded devices to generate and deploy TensorFlow models.[60] These models are compressed and optimized in order to be more efficient and have a higher performance connected smaller capacity devices.[61]

TensorFlow Lite uses FlatBuffers as the data serialization formatting for network models, eschewing the Protocol Buffers format used away standard TensorFlow models.[61]

TFX [edit]

TensorFlow Extended (abbrev. TFX) provides numerous components to perform all the operations needed for end-to-end production.[62] Components include loading, verifying, and transforming data, tuning, training, and evaluating the automobile encyclopedism mannequin, and pushful the model itself into output.[42] [62]

Integrations [blue-pencil]

Numpy [edit]

Numpy is one of the most popular Python data libraries, and TensorFlow offers desegregation and compatibility with its data structures.[63] Numpy NDarrays, the library's aboriginal datatype, are automatically converted to TensorFlow Tensors in TF operations; the Lapp is also faithful bench vise-versa.[63] This allows for the two libraries to influence in unison without requiring the drug user to write out denotive data conversions. Moreover, the integration extends to store optimization by having TF Tensors ploughshare the underlying memory representations of Numpy NDarrays whenever manageable.[63]

Extensions [edit]

TensorFlow also offers a variety of libraries and extensions to advance and extend the models and methods utilized.[64] For example, TensorFlow Recommenders and TensorFlow Graphics are libraries for their respective functionalities in testimonial systems and graphics, TensorFlow Federated provides a framework for decentralized information, and TensorFlow Cloud allows users to directly interact with Google Cloud to integrate their local encipher to Google Cloud.[65] Other add-ons, libraries, and frameworks include TensorFlow Model Optimization, TensorFlow Chance, TensorFlow Quantum, and TensorFlow Determination Forests.[64] [65]

Google Colab [edit]

Google also free Colaboratory, a TensorFlow Jupyter notebook environment that does not require any setup.[66] It runs on Google Mist and allows users free access to GPUs and the ability to store and part notebooks happening Google Drive.[67]

Applications [edit]

Medical [edit out]

GE Health care victimized TensorFlow to increase the speed and accuracy of MRIs in characteristic specific body parts.[68] Google used TensorFlow to create DermAssist, a free mobile coating that allows users to take pictures of their pelt and identify electric potential health complications.[69] Sinovation Ventures used TensorFlow to identify and classify eye diseases from optical coherence imaging (OCT) scans.[69]

[edit]

Twitter implemented TensorFlow to rank tweets by importance for a given user, and changed their platform to read tweets in grade of this ranking.[70] Antecedently, tweets were simply shown in reverse written account order.[70] The photograph sharing app VSCO used TensorFlow to help suggest custom filters for photos.[69]

Search Engine [edit]

Google formally released RankBrain connected October 26, 2022, high-backed by TensorFlow.[71]

Education [blue-pencil]

InSpace, a virtual learning political platform, used TensorFlow to strain toxic chat messages in classrooms.[72] Liulishuo, an online English learning platform, utilized TensorFlow to create an adaptive curriculum for each student.[73] TensorFlow was wont to accurately assess a student's current abilities, and also helped decide the best future content to show up based on those capabilities.[73]

Retail [edit]

The e-Commerce platform Carousell used TensorFlow to provide individualized recommendations for customers.[69] The cosmetics company ModiFace used TensorFlow to create an augmented reality experience for customers to test various shades of defecate-sprouted on their brass.[74]

Original photo (left) and with TensorFlow neural style practical (just)

Research [edit out]

TensorFlow is the institution for the machine-controlled image-captioning software DeepDream.[75]

Learn also [redact]

  • Equivalence of deep learnedness software
  • Differentiable programing
  • Keras

Bibliography [edit out]

  • Moroney, Laurence (October 1, 2022). AI and Motorcar Learning for Coders (1stdannbsp;ed.). O'Reilly Media. p.dannbsp;365. ISBN9781492078197.
  • Géron, Aurélien (October 15, 2022). Active Machine Eruditeness with Scikit-Learn, Keras, and TensorFlow (2nddannbsp;ed.). O'Reilly Media. p.dannbsp;856. ISBN9781492032632.
  • Ramsundar, Bharath; Zadeh, Reza Bosagh (March 23, 2022). TensorFlow for Deep Learning (1stdannbsp;ed.). O'Reilly Media. p.dannbsp;256. ISBN9781491980446.
  • Hope, Tom; Resheff, Yehezkel S.; Lieder, Itay (August 27, 2022). Learning TensorFlow: A Guide to Building Deep Acquisition Systems (1stdannbsp;ED.). O'Reilly Media. p.dannbsp;242. ISBN9781491978504.
  • Shukla, Nishant (February 12, 2022). Simple machine Learning with TensorFlow (1stdannbsp;ed.). Manning Publications. p.dannbsp;272. ISBN9781617293870.

Extraneous links [cut]

  • Official website

References [blue-pencil]

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  5. ^ {{cite video|url=https://www.youtube.com/watch?v=oZikw5k_2FM%7C file away-url=https://ghostarchive.org/varchive/youtube/20211111/oZikw5k_2FM%7C archive-date=2021-11-11 | url-status=vital|title=TensorFlow: Open source machine learning|year= 2022|author=Google|ref=CITEREFVideo_clip_by_Google_about_TensorFlow2015}} "It is machine learning software being used for various kinds of perceptual and language understanding tasks" – Jeffrey Dean, minute 0:47 / 2:17 from YouTube clip
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