Python libraries

Python in 2020 is considered one of the best computer languages if you think you can build a career in Machine Learning. It is easy to learn and compatible makes it widely popular among the developers. The syntax used in Python is super easy to learn, and you can even compare it with robust computer languages like C++ or Java. One of the reasons it is so popular is because of its massive collection of Python Libraries.

In computer science, a library is a collection of non-volatile resources used by computer programs, often for software development. These may include configuration data, documentation, help data, message templates, pre-written code and subroutines, classes, values or type specifications. In IBM’s OS/360 and its successors they are referred to as partitioned data sets.” (Wikipedia)

Python Libraries and Packages are a set of useful modules and functions that minimize the use of code in our day to day life.

Python Packages are a set of python modules, while python libraries are a group of python functions aimed to carry out special tasks.

TensorFlow

For those who don’t know, TensorFlow is one of the first-choice libraries if you are engaged in machine learning projects. Developed by Google, this Python library is used in most of the Google applications. You can also use TensorFlow as a computational library that will allow you to write new algorithms.

With TensorFlow, you can easily create Responsive Constructs and train a CPU or a GPU for shared computing. This Python library is flexible in most of the operations, and you can brew several neural networks and various GPUs. Apart from that, TensorFlow is open-source and serves a large community. Thus, you should give it a try.

Features of TensorFlow

TensorFlow is optimized for speed, it makes use of techniques like XLA for quick linear algebra operations.

1. Responsive Construct

With TensorFlow, we can easily visualize each and every part of the graph which is not an option while using Numpy or SciKit.

2. Flexible

One of the very important Tensorflow Features is that it is flexible in its operability, meaning it has modularity and the parts of it which you want to make standalone, it offers you that option.

3. Easily Trainable

It is easily trainable on CPU as well as GPU for distributed computing.

4. Parallel Neural Network Training

TensorFlow offers pipelining in the sense that you can train multiple neural networks and multiple GPUs which makes the models very efficient on large-scale systems.

5. Large Community

Needless to say, if it has been developed by Google, there already is a large team of software engineers who work on stability improvements continuously.

6. Open Source

The best thing about this machine learning library is that it is open source so anyone can use it as long as they have internet connectivity.

Scikit-Learn

Scikit learn is a simple and useful python machine learning library. It is written in python, C, and C++. However, most of it is written in the Python programming language. It is a free machine learning library. It is a flexible python package that can work in complete harmony with other python libraries and packages such as Numpy and Scipy.

Features Of Scikit-Learn

1. Cross-validation: There are various methods to check the accuracy of supervised models on unseen data.

2. Unsupervised learning algorithms: Again there is a large spread of algorithms in the offering — starting from clustering, factor analysis, principal component analysis to unsupervised neural networks.

3. Feature extraction: Useful for extracting features from images and text (e.g. Bag of words)

Numpy

Numpy is considered as one of the most popular machine learning library in Python.

TensorFlow and other libraries use Numpy internally for performing multiple operations on Tensors. Array interface is the best and the most important feature of Numpy.

Features Of Numpy

• Arrays of Numpy offer modern mathematical implementations on a huge amount of data. Numpy makes the execution of these projects much easier and hassle-free.
• Numpy provides masked arrays along with general array objects. It also comes with functionalities such as manipulation of logical shapes, discrete Fourier transform, general linear algebra, and many more.
• While you change the shape of any N-dimensional arrays, Numpy will create new arrays for that and delete the old ones.
• This python package provides useful tools for integration. You can easily integrate Numpy with programming languages such as C, C++, and Fortran code.
• Numpy provides such functionalities that are comparable to MATLAB. They both allow users to get faster with operations.

Keras

Keras is one of the smartest Python libraries to date, which is an ideal choice if you fancy machine learning. It can display neural networks with ease, and when it comes to compiling models, processing data-sets, or viewing graphs, Keras can be your best friend. Moreover, it can also work with other libraries like Theano or TensorFlow, which gives you an edge in backend jobs.

You will be surprised to know that you use Keras almost every day. Popular programs like Netflix, Uber, Yelp, Instacart, Zocdoc, Square, and much more use Keras. However, the most effective use of Keras is Neural Networking. That’s not all, you can do in-depth learning studies using this library as well.

Features Of Keras

• It runs smoothly on both CPU and GPU.
• Keras supports almost all the models of a neural network — fully connected, convolutional, pooling, recurrent, embedding, etc. Furthermore, these models can be combined to build more complex models.
• Keras, being modular in nature, is incredibly expressive, flexible, and apt for innovative research.
• Keras is a completely Python-based framework, which makes it easy to debug and explore.