As you know, every machine learning application, including deep learning applications, follows a standard pipeline structure consisting of several steps. Over the years, most of these steps are also standardized in step level for the most part. Since the nature of the workload has become standard, researchers began to build frameworks, which provide solutions for repetitive tasks. The frameworks such as TensorFlow and PyTorch already offers us several modules for all these steps.
Even though deep learning frameworks are very powerful for model building, training, evaluation, and prediction tasks, they fail to compete with specialized complementary data libraries. … The most popular complementary libraries are as follows:
- NumPy for Array Processing
- SciPy for Scientific Computing
- Pandas for Array Pocessing & Data Analysis
- Matplotlib for Data Visualization
- Seaborn for Data Visualization
- Scikit Learn for Machine Learning
- Flask for Deployment
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