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What Python libraries do you use in machine learning?
Python is a high-level programming language. It is perfectly interpreted, object-oriented and offers dynamic semantics. Python is known for its enhanced productivity. There are many characteristics associated with Python. These include automatic garbage collection, easy integration with C, C++, CORBA, Java, etc. Other than this, Python offers many features like:
Smaller than other programming languages
Offers enhanced readability
Used by multiple tech giant companies
Interactive & easy to maintain
Object-oriented
Beginner’s language
Portable & Extendable
Python has gained popularity due to its huge collection of libraries. Many machine learning problems are solved with Python. Python libraries are TensorFlow, Theanos, Keras, Scikit-Learn, etc. With all these libraries, machine learning has been made easy. Top Python libraries for machine Translation with descriptions are given below:
Theano: This Python-based library enables the evaluation, optimization, and definition of mathematical expressions easy with multi-dimensional arrays. It is popular because of its large usage to date.
Scikit-Learn: Scikit-learn offers a wide range of supervised and unsupervised learning algorithms that enhance the production system.
TensorFlow: This open-source deep-learning library contains a mixture of symbolic computation libraries and network specification libraries. The models built with TensorFlow include Google photos, Google voice search, etc.
Keras: Keras is one of the most user-friendly libraries with high-level neural networks app programming interface.
PyTorch: PyTorch is adept at handling dynamic computation graphs. Other libraries lack this feature. Other features that PyTorch offers include smooth integration and easy to use API.
Above given Python libraries work uniquely and are extensively used by engineers and data scientists these days. We hope you make the right choice of the library after reading this.
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