Machine Learning Libraries in Python That Are Changing the World

Gone are the days when Python’s only identity was attributed to the family of the reptiles. Today, when you say Python, it is often the programming language that is the first guess. 

The wave of Python has swept the world of science and technology off its feet, and it continues to do. Be it Google or Microsoft, everybody is jumping right into the ocean of multitudinous benefits that Python has to offer. As days pass by, Python is becoming the number one choice of programming language for programming experts, statistical technicians, data analysts, and machine learning researchers.

Today, Python is even leaving behind the world’s once most popular development language JAVA. According to the recent research conducted by the IEEE Spectrum, Python stands firmly as the top language of the year 2022.

With this, it beats popular languages such as JAVA Script, C, C++ and R. This sudden growth in the usage of the language can be attributed to its research utility. As artificial intelligence and machine learning expand their domains to every other industry, it is Python that is becoming the go-to language for everything. Thanks to its libraries that are facilitating machine learning and helping change the world for good.

As many as 45 % of the companies across the globe are using Python for machine learning. This fact leaves us fathoming about the abundance that its various libraries hold. Let’s take a look at the front running libraries of Python that are transforming the world like never before-

TENSOR FLOW

Implementing any machine learning algorithm needs an environment that is not just well-structured but also complements the development of complex models. Python’s library Tensor flow is a perfect combination of flexibility and simplicity that aids machine learning by deploying large datasets. 

Developed by Google’s Brain team, Tensor flow is an open-source end-to-end library that facilitates high-end numerical computations. One of the best things about tensor flow is that it lets you run the same code both on the CPU and GPU. You are no more writing at the C++ level separately. All of Google’s applications use tensor flow. So, next time, when you use Google voice search or photos, remember that its models are built using tensor flow. 

One of the most promising technologies in the world that have emerged right out of tensor flow is image recognition and voice recognition. You can easily spot its application in industries like automotive, IoT, telecom, aviation, entertainment and more. 

KERAS

Designed to implement neural networks and advanced machine learning projects, Keras is one of the most widely-used Python libraries. It is packed with multiple standalone modules that can be used for processing datasets, evaluating results, visualizing graphs, compiling models among others.

The expression is one of the fundamentals of the Keras framework. Having said this, Keras is one of the quintessential machine learning libraries when combined with a backend such as Tensor flow or Theano. Whether you realize it or not, you are already surrounded by Keras in your day-to-day life. Be it Netflix, Uber, Instacart, or others, Keras lies at the core of many popular applications.

Believe it or not, Keras is not just changing the world but contributing to extraterrestrial research domains as well. It is extensively used by scientific organizations such as CERN and NASA.

NUMPY

Python’s famous library Numpy is a great asset to the world. Not only is it the go-to library of any machine learning enthusiast but also the most significant mathematical computation library of Python.

Numerical Python, better known as Numpy, comes with complex mathematical operations such as linear algebra, random numbers, Fourier transformations, etc. Many advanced libraries of Python rely internally on it for scientific computations. Being interactive, Numpy has a great deal of contribution to the world.

It is changing the way full-stack developer’s code, making it much more comfortable, intuitive, and portable than before. Numpy is also excessively utilized to express images, sound waves, and more.

THEANO

When it comes to multi-dimensional arrays, Theano is the ultimate python library. Even though you may find it quite similar to Tensor flow, it is the GPU optimization that sets it apart.

Theano’s tight integration with Numpy and speed makes it exceptionally valuable for deep learning algorithms. When running on CPU alone, it can perform 100 times faster mathematical computations and data-intensive calculations. Additionally, its efficient symbolic differentiation and self-testing capabilities make it a perfect fit for high-performing software. Theno’s popularity is growing with each passing day because of its scientific contribution to the world.

PYTORCH

Pytorch is a Python library that has gained massive popularity in a very less amount of time. It is an excellent ready-for-production library that has profound applications in multiple industries.  Pytorch supports both GPU and CPU and provides the user with high-performance APIs for solving issues related to neural networks. It boosts the performance of deep neural networks and facilitates natural language processing.

Developed by Facebook’s AI research group and outperforms Tensor flow at various levels. Right now it is being used by Uber’s Pyro software for probabilistic programming. Its application in NLP is solving multiple problems in the world. 

CONCLUSION

As the world gets closer to unraveling the potential of artificial intelligence, there is an emerging need for more relevant tools than ever. With its multiple benefits, Python caters to all requirements of machine learning. Its libraries are not just friendly to use but also let people accomplish much with fewer lines of code.

It is driving high-level programming by helping developers become more productive at every stage. Right from development to deployment and maintenance, hire Python developers for changing the world like never before.