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Top 5 Python Libraries for Machine Learning

Machine Learning libraries are available in many programming languages but Python is best suited for Machine Learning purposes. Python is the most user-friendly and easy-to-manage language and has a large developer community. In this, we will give you a brief introduction to the best Python Libraries for Machine Learning.

Python libraries are collections of modules that contain useful codes and functions, eliminating the need to write them from scratch. Below is the list of Python Libraries for Machine Learning:

  1. PyTorch

  2. TensorFlow

  3. NumPy

  4. Pandas

  5. Keras

1. PyTorch

PyTorch is an open-source Machine Learning Python library based on the C programming language framework, Torch. It is mainly used in ML applications that involve natural language processing or computer vision. PyTorch is known for being exceptionally fast at executing large, dense data sets and graphs.

2. TensorFlow

TensorFlow’s open-source Python library specializes in what’s called differentiable programming, meaning it can automatically compute a function’s derivatives within a high-level language. Both machine learning and deep learning models are easily developed and evaluated with TensorFlow’s flexible architecture and framework. TensorFlow can be used to visualize machine learning models on both desktop and mobile.

3. NumPy

NumPy is a popular Python library for multi-dimensional array and matrix processing because it can be used to perform a great variety of mathematical operations. Its capability to handle linear algebra, Fourier transform, and more, makes NumPy ideal for machine learning and artificial intelligence (AI) projects, allowing users to manipulate the matrix to easily improve machine learning performance. NumPy is faster and easier to use than most other Python libraries.

4. Pandas

Pandas is another Python library that is built-on top of NumPy, responsible for preparing high-level data sets for machine learning and training. It relies on two types of data structures, one-dimensional (series) and two-dimensional (DataFrame). This allows Pandas to be applicable in a variety of industries including finance, engineering, and statistics. Unlike the slow-moving animals themselves, the Pandas library is quick, compliant, and flexible.

5. Keras

Keras provides a Python interface of TensorFlow Library especially focused on AI neural networks. The earlier versions also included many other backends like Theano, Microsoft cognitive platform, and PlaidMl. Keras contains standard blocks of commonly used neural networks, and also the tools to make image and text processing faster and smoother. Apart from standard blocks of neural networks, it also provides re-occurring neural networks.

Difference between NumPy and Pandas



It was developed by Wes McKinney and was release in 2008

It was developed by Travis Oliphant and was released in 2005

It is preferred when you have to work on tabular data

It is preferred when you have to work on numerical data

Data frame and series are used in Pandas

Arrays are used in NumPy

It consumes more memory

It consumes less memory

Indexing is very slow as compared to numpy arrats

Indexing is fast.

It is used in organizations like Trivago, Abeja etc.,

It is used in organizations like Walmart, Instacart etc.,

Difference between TensorFlow, PyTorch, and Keras




TensorFlow is an end-to-end open-source deep learning framework developed by Google

PyTorch is a relatively new deep learning framework based on Torch.

Keras is an effective high-level neural network Application Programming Interface (API) written in Python.

It is written in C++, CUDA and Python

It is written in Lua

It is written in Python

It is used for neural networks for dataflow programming across a range of tasks.

It is open-source and used for natural language processing applications.

It is open-source neural network library designed to provide fast experimentation with deep neural networks

It is not easy to use

It is complex and not easily to read

It is simple, concise and easily readable

It has both high and low - level API's

It has low-level API focused on direct work with array expressions

It has high-level API capable of running on top of TensorFlow, CNTK and Theano

It is fast and suitable for high performance

It is fast and suitable for high performance

It is slow

It is difficult to debugg

It has better debugging capabilities as compared to Keras and TensorFlow

Not much need to debug simple network

It is used for large datasets

It is used for large datasets

It is used for small datasets

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