Machine Learning Using TensorFlow

Android Lietotņu Analīze Un Pārskats: Machine Learning Using TensorFlow, Izstrādājusi reSolution. Uzskaitīts Izglītība Kategorijā. Pašreizējā Versija Ir 1.0, Atjaunināta 11.-22. Saskaņā Ar Lietotāju Pārskatiem Vietnē Google Play: Machine Learning Using TensorFlow. Sasniegts Vairāk Nekā 6 Tūkstoš Instalācijas. Machine Learning Using TensorFlow Pašlaik Ir 42 Atsauksmes, Vidējais Vērtējums 2.2 Zvaigznes

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to progressively improve their performance on a specific task.

This Tutorial provides various applications of ML using TensorFlow.

TensorFlow™ is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.

This Tutorial provides following Applications:
1)Basic classification
This guide trains a neural network model to classify images of clothing, like sneakers and shirts.

2)Text classification
This application classifies movie reviews as positive or negative using the text of the review.

3)Save and restore models
Model progress can be saved during—and after—training. This means a model can resume where it left off and avoid long training times.This application provides how to save and restore a model.

4)Image Recognition
This tutorial will teach you how to use Inception-v3. You'll learn how to classify images into 1000 classes in Python.

5)Retrain an Image
In this tutorial, we will reuse the feature extraction capabilities from powerful image classifiers trained on ImageNet and simply train a new classification layer on top.