ML Libraries

TensorFlow

TensorFlow works like a computational library for writing new algorithms that involve a large number of tensor operations, since neural networks can be easily expressed as computational graphs, they can be implemented using TensorFlow as a series of operations on Tensors. Plus, tensors are N-dimensional matrices which represent your data. All the libraries created in TensorFlow are written in C and C++. However, it has a complicated front-end for Python. Your Python code will get compiled and then executed on TensorFlow distributed execution engine built using C and C++. The number of applications of TensorFlow is literally unlimited and that is the beauty of TensorFlow.

Scikit-Learn

It is a Python library is associated with NumPy and SciPy. It is considered as one of the best libraries for working with complex data. There are a lot of changes being made in this library. One modification is the cross-validation feature, providing the ability to use more than one metric. Lots of training methods like logistics regression and nearest neighbours have received some little improvements. It contains a numerous number of algorithms for implementing standard machine learning and data mining tasks like reducing dimensionality, classification, regression, clustering, and model selection

Numpy

Numpy is considered as one of the most popular machine learning libraries in Python. TensorFlow and other libraries uses Numpy internally for performing multiple operations on Tensors. Array interface is the best and the most important feature of Numpy. This interface can be utilized for expressing images, sound waves, and other binary raw streams as an array of real numbers in N-dimensional.

Keras

Keras is considered as one of the coolest machine learning libraries in Python. It provides an easier mechanism to express neural networks. Keras also provides some of the best utilities for compiling models, processing datasets, visualization of graphs, and much more. In the backend, Keras uses either Theano or TensorFlow internally. Some of the most popular neural networks like CNTK can also be used. Keras is comparatively slow when we compare it with other machine learning libraries. Because it creates a computational graph by using back-end infrastructure and then makes use of it to perform operations. All the models in Keras are portable.

PyTorch

PyTorch is the largest machine learning library that allow developers to perform tensor computations with acceleration of GPU, creates dynamic computational graphs, and calculate gradients automatically. Other than this, PyTorch offers rich APIs for solving application issues related to neural networks. This machine learning library is based on Torch, which is an open-source machine library implemented in C with a wrapper in Lua. PyTorch is primarily used for applications such as natural language processing.

Pandas

Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language. In particular, it offers data structures and operations for manipulating numerical tables and time series. A fast and efficient DataFrame object for data manipulation with integrated indexing.