The Top Python Libraries Every Developer Should Learn

  1. TensorFlow
  • TensorFlow uses automatic high-performance APIs such as — Keras.
  • It offers an immediate iteration of machine learning models.
  • This library features eager execution, which allows you to create, manipulate machine learning models, and make the debugging way easier.
  • With TensorFlow, we can easily imagine each and every part of the graph.
  • With TensorFlow, you can easily move your ML models in clouds, on any device and on-premises in any browser.
  • TensorFlow comes with an easy to learn architecture. You can easily develop your concept into code and make your publications even easier.
  • It has a solution to all of your common machine learning issues. You can easily implement it and go for giving your best.
  • Makes complex mathematical implementations very simple.
  • Makes coding real simple and understanding the concepts is easy.
  • This python package provides useful tools for integration. You can easily integrate NumPy with programming languages such as C, C++, and Fortran code.
  • Broadly used, therefore a lot of open source contributions.
  • NumPy provides such functionalities that are comparable to MATLAB. They both allow users to get faster with operations.
  • SciPy fulfills all the efficient numerical routines like optimization, numerical integration.
  • All the functions in all submodules of SciPy are well documented.
  • It makes the best use of NumPy arrays for general data structures. In fact, NumPy is an integrated part of Scipy.
  • Scipy can handle 1-d polynomials in two ways. Whether you can use poly1d class from NumPy or you can use co-efficient arrays to do the job.
  • Keras, being modular in nature is amazingly expressive, flexible, and well-suited for innovative research.
  • It doesn’t only support neural networks only but also provides a fully supportive environment for convolutional and recurrent neural networks.
  • It runs smoothly on both CPU and GPU.
  • This python library features a variety of implementations from neural networks forming blocks — functions, layers, optimizers, objectives, and others.
  • Keras also features many useful tools that allow you to work with different images and texts easily.
  • Using Keras, you can build deep models for smartphones — both Android and iOS or for Java Virtual Machine also.
  • Pandas make sure that the entire process of manipulating data will be easier.
  • Smart alignment and indexing featured in Pandas offer you a perfect organization and data labeling.
  • Pandas have some special features that allow you to handle missing data or value with a proper measure.
  • This package offers you such a clean code that even people with no or basic knowledge of programming can easily work with it.
  • It provides a collection of built-in tools that allows you to both read and write data in different web services, data-structure, and databases as well.
  • Pandas can support JSON, Excel, CSV, HDF5, and many other formats. In fact, you can merge different databases at a time with Pandas.
  • Ability to use NumPy arrays effectively in Theano-compiled functions.
  • Perform data-intensive computations much quicker than on a CPU.
  • Theano performs your derivatives for functions with one or many inputs.
  • Evaluate expressions faster than ever before, thereby, increasing efficiency by a lot.
  • Detect and diagnose multiple types of errors and ambiguities in the model.
  • Theano allows you to avoid dirty bugs while working with expressions. You can work seamlessly on expressions without wasting any time.
  • This library makes computation 140x faster. Computation of data-intensive applications is easier with Theano.
  • PyTorch can be used with other popular libraries, as well. You can easily integrate it with libraries/packages like Cython and Numba.
  • PyTorch uses TorchScript, which offers a flexible and simple eager mode. You can evaluate different functions and operations instantly.
  • While in the graph mode, PyTorch provides absolute transitioning, fast optimizations, and offers a C++ run-time environment.
  • PyTorch has good support for async. Execution for cumulative operations. This way, you can boost up your project performance.
  • This library also allows P2P (Peer to Peer) communication, which can be gained by both Python and C++.
  • https://maiyalily830.medium.com/4-lessons-from-my-4-years-at-instagram-facebook-as-a-software-engineer-a895d2493c90

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