GitHub: Tensorflow

GitHub: Tensorflow
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Written by Debarghya DasJanuary 12, 2022
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Debarghya Das

Junior Front-End Developer

Hello everyone, we will discuss in this article about GitHub Tensorflow and top GitHub repositories to related with tensorflow.

About GitHub

GitHub, Inc. is a provider of Internet hosting for software development and version control using Git. It offers the distributed version control and source code management (SCM) functionality of Git, plus its own features. It provides access control and several collaboration features such as bug tracking, feature requests, task management, continuous integration, and wikis for every project. Headquartered in California, it has been a subsidiary of Microsoft since 2018.

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About GitHub Tensorflow

GitHub TensorFlow hosts various archives on Machine learning as a. Libraries to assemble powerfully, progressed information models b. Apparatuses to empower easier/quicker execution of Tensor codes and Tensor work processes, to imagine the working of Tensor projects and investigate the issues, to do consider the possibility that examination on the Tensor models to upgrade its adequacy c. Significant contributions from the end-user community, developers' discussion, and their encounters.

Developers draw truly necessary assets from these vaults while building cutting-edge Machine Learning models in Artificial Intelligence (AI) applications. This store contains program interfaces (API) in Python, C++, and scarcely any different dialects too.

Top GitHub repositories related to Tensorflow

1. Matplotlib:

It is hosted in GitHub and the developments, issues are tracked systematically. This repository consists of libraries to develop impactful visualizations in Python (both static and dynamic). This library is capable of producing contents that can be published in any format interactively. Matplotlib is deployed in IPython shells, Web Servers, Python scripts and other GUI based toolkits.

Some of its features are:

  • With very little coding effort a high quality of visual plots can be created that are highly interactive and its attributes can be changed quite dynamically.
  • These graphical contents can easily be customized and it can be easily downloaded into any other environments
  • It can be interfaced with other third-party tools with ease
  • This open-source software package gets shipped with several other tool kits like 3D Plotter to view 3 axes in different colours for better visibility.
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2. Pandas

It is a python library that handles data analysis and manipulation effectively and it manages big volumes of data by splitting them into subsets based on some conditions and forming multiple decision trees. These decision trees help in predicting the right results in the search operation.

Its features include:

  • It is an open-sourced tool built in Python. It provides flexible and easy operation Data analysis/manipulation operations.
  • It offers DataFrame objects with integrated indexing to manipulate data in a faster and efficient manner
  • It provides data structures with separate labels and it resembles with data frames in R programming.
  • It allows the reshaping of data from one dimension to another dimension and creating multiple pivots
  • Data alignment based on the labels in an intelligent way and extraction of cleaner orderly data from messy data is the main USPs of this tool.
  • Pandas allow the creation of multiple subsets of data from a global data set and create a data forest for validations
  • It allows faster joining of the dataset for quicker results
  • Data from any sources like Excel, CSV, Text and databases can be extracted and loaded into data structures in the memory and vice versa.
  • Time Series and statistical functionalities are easily managed in Pandas.
  • Aggregation of data, hierarchical indexing, slicing of data based on labels are the specialities of Pandas.
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3. Numpy

This package facilitates mathematical and scientific computing in Python. Several mathematical functions, algebra routines, Fourier conversions and Random number functions are offered as part of this tool.

Highlights of NumPy are:

  • The power of NumPy is being exploited invariably by all data scientists in their research using complex data models
  • Indexing, Broadcasting and Vectors principles of NumPy are the default benchmark standards in Data Arrays management
  • NumPy adds the needed Mathematical strength to Python and makes it as powerful as C, Fortran
  • It is easy to use and its English like syntax makes it simple for developers to adopt it and increase their productivity considerably.
  • NumPy is an opensource tool maintained by GitHub with support from developer community.
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4. Scipy

  • SciPy offers vibrant Mathematical, Engineering and Science modules for video/image processing. Its stack includes Ordinary Differential Equation (ODE) solvers, Statistical functions, Fourier Series and linear algebra etc., SciPy library has multiple numerical routines that are efficient and user friendly.
  • Other strengths of SciPy are
  • It integrates well with NumPy and its arrays.
  • SciPy is deployed in Numerical integration and Optimization areas.
  • It supports all the operating systems
  • SciPy is easy to install and it is open-sourced free tool.
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5. Scikit-learn

  • It offers Machine learning modules in Python and it is built over data models built on NumPy, SciPy and matplotlib.
  • Some of its features are
  • This tool is deployed in Predictive analytics to study drug responses and share price movement.
  • It is an open-source tool and it has reusable functionalities.
  • Image processing and spam detections are the other applications for this tool.
  • Others
  • Pillow – Imaging Library Fork to support Continuous Integrations (CI) / Continuous
  • Development (CD)
  • Six – Utility Tools to manage Python code compatibility
  • H5PY – Enables developers to store huge data and manipulate them easily
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GitHub Tensorflow learning

  • Neural Structured learning (NSL) uses structured signals along with feature inputs to train neural networks. Structures can be a graph in the explicit model or they can be adversarial perturbation in the implicit model. The commonality among samples both labelled or unlabeled are represented through structured signals and it helps in harnessing data to improve the model accuracy especially when the data volume is low.
  • Neural graphs and adversarial learnings are generalized in NSL. Several simple Program interfaces (API) and tools are used to train models with signals. Any neural network can be trained by this flexible NSL framework including unsupervised learning. Performance of workflow remains unchanged due to the fact that signals are incorporated only during training.

Conclusion

GitHub gives an ideal stage to facilitating various TensorFlow archives for engineers to take advantage of and influence the force of information models in their Machine learning applications and work on the consequences of the arrangements worked in AI platform.

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GitHub Tensorflow
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Debarghya Das
Junior Front-End Developer
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