List of Best Open Source ML Libraries For Python

Deep Learning is a subset of Machine Learning and it works on the structure and functions of a human brain. It learns from the data that is structured and uses complex algorithms to train a neural net. This is a learning mechanism. Deep learning is a neural network somewhat looks like this there is something known as an input layer and then there is an output layer and in between there are a bunch of hidden layers so typically it would be at least one hidden layer and anything more than one hidden layer is known as a deep neural network so any neural network with more than three layers altogether right based known as a deep neural.

Open source platform like Python play an important role in the Machine learning market. In the recent years Python has gained a lot of attraction in Machine learning. Python has a large collection of libraries. The simplicity of python has attracted many developers to build libraries for Machine learning.

Some of the best Machine Learning libraries for Python are :

Scikit-Learn.
Numpy.
Keras.
TensorFlow
Theano.

Scikit-Learn:

Simple and efficient tools for data mining and data analysis
It provides efficient solutions to machine learning problems

Numpy:

NumPy is an acronym for "Numeric Python" or Numerical Library.
It provides fast precompiled functions for mathematical and numerical routines

Keras:

Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible.

Tensor Flow:

TensorFlow is an open source software library for numerical computation using data flow graphs
If you are using Google photos or Google voice search then indirectly you are using the models built using Tensorflow.

Theano:

It is a numerical computation library for Python
It allows you to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays

Hence, these are the best open source machine learning libraries written in Python tensor-flow is one of the most popular machine learning frameworks and the tensors are really very handy in terms of keeping the data compact because they are like multi-dimensional arrays so the data is stored in tensors and then it is fed into the neural network.

Source: HOB

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