Updated April 17, 2023
What is GitHub TensorFlow?
GitHub TensorFlow hosts numerous repositories on Machine learning in the form of a. Libraries to build robust, advanced data models b. Tools to enable simpler / faster execution of Tensor codes and Tensor workflows, to visualize the working of Tensor programs and debug the issues, to do what-if analysis on the Tensor models to enhance its effectiveness c. Valuable inputs from end-user community, developers’ forum and their experiences.
Developers draw much-needed resources from these repositories while building state of the art Machine Learning models in Artificial Intelligence (AI) applications. This repository contains program interfaces (API) in Python, C++ and few other languages as well.
Top GitHub Repositories related to TensorFlow
GitHub basically allows real-time collaboration between developers in program development by offering them rich content from their repositories. There is a big, separate group of TensorFlow users among the GitHub community and they consume Python Packages in a large scale. These Python components in GitHub repositories are used quite frequently by TensorFlow Machine Learning development team and let us analyze some of them.
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.
3. 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.
4. 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.
5. 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.
6. 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
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 provides a perfect platform for hosting numerous TensorFlow repositories for developers to exploit and leverage the power of data models in their Machine learning applications and improve the results of the solutions built-in AI platform.
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