• By Arya Admin
• In Science & Technology,
• Posted February 10, 2021

Important machine learning libraries for BTech graduates

Machine Learning Libraries

During the early days of Machine Learning, developers had to perform Machine Learning tasks by manually coding each ML algorithm. For this, they use mathematical and statistical formulas. This process was both time and labour-intensive. By entering the mainstream tech domain, the ML community or Top BTech Colleges in Jaipur is evolving constantly at an unprecedented pace. As a result, we have an exhaustive inventory Machine Learning libraries and Machine Learning frameworks at our disposal.

Essentially, Machine Learning libraries refer to functions and routines written in a specific programming language. These libraries make the task of ML Developers/ML Engineers easier by allowing students of top engineering colleges to perform complex tasks without having to rewrite endless lines of code.

Some of the most popular and widely used Machine Learning libraries are as follows:

TensorFlow

TensorFlow is undoubtedly one of the most popular Machine Learning libraries in the world. It is an open-source, JavaScript-based Machine Learning library explicitly designed for numerical computation using data flow graphs. It comes equipped with an assortment of useful tools, libraries, and resources that help in training, building,and deploying ML applications in a hassle-free manner.

TensorFlow is extensively used for training and deploying models on Node.js along with the browsers. While using core library to develop and train ML models in browsers, students of Best Engineering Colleges in Rajasthan can use TensorFlow Lite to deploy models on mobile and embedded devices.

NumPy

NumPy is basically a Python-based Machine Learning library for scientific computing. It includes sophisticated functions, tools for integrating C/C++ and Fortran code, and a powerful N-dimensional array object. NumPy is extensively used by the students of btech colleges in Jaipur for large multi-dimensional array and matrix processing by using high-level mathematical functions. Apart from this, it is an excellent for Fourier transform, linear algebra, and random number capabilities. They can use NumPy as an efficient multi-dimensional container of generic data wherein arbitrary data-types can be defined. This further encourages seamless and speedy integration with many different databases.

SciPy

SciPy is a Python-based ML ecosystem for mathematics, science, and engineering. It is primarily used for scientific and technical computing. SciPy is a component of the NumPy stack including tools like Matplotlib, SymPy, Pandas, and a host of other scientific computing libraries. The underlying data structure leveraged by SciPy is a multi-dimensional array mainly offered by the NumPy module.

SciPy contains modules for some commonly performed tasks in scientific programming like optimization, integration, linear algebra, interpolation, special functions, FFT, signal, ordinary differential equation solving, and image processing, ad much more.

Scikit-Learn

Scikit-Learn is an open-source Python-based Machine Learning library mainly built on three other Python Libraries like NumPy, SciPy, and Matplotlib. The Scikit-Learn is bound in a host of ML algorithms including classification, clustering and dimensionality reduction, regression, Naive Bayes, Gradient boosting, K-means, model selection, to name a few. It is an excellent tool for data analysis, data-mining, and statistical modelling. It also has an excellent documentation along with a huge support community.

Theano

Theano can take structures and convert them into efficient code that uses NumPy and other native libraries. It is mainly used for numerical computation and can handle different types of computation required for large neural network algorithms used in Deep Learning. Also, it allows students of best engineering colleges in Jaipur define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.

It has neat symbolic differentiation and enable for dynamic code generation in C. Perhaps the greatest aspect of this ML library benefits GPU which makes data-intensive calculations up to 100 times faster than when it runs on CPU alone. Theano’s speed is what makes it a potent tool for complex computation tasks and Deep Learning projects.

PyTorch

PyTorch is one of the open-source Deep Learning libraries that helps you draw inspiration from the Torch library. It was developed by Facebook’s AI research team. While it has a C++ frontend, it features a highly polished Python interface for the students of best BTech colleges in Rajasthan.

PyTorch is mainly used for natural language processing along with a computer vision application. The “torch.distributed” backend of PyTorch enables scalable distributed training and performance optimization both in production and research. The two core features of PyTorch are Deep Neural Networks and Tensor computing using GPUs.

Keras

Keras an open-source neural network library written in Python. It can run on top of TensorFlow, Microsoft Cognitive Toolkit, Theano, and PlaidML. Since Keras was designed to facilitate fast experimentation to the students of engineering colleges with Deep Neural Networks, it is highly modular, user-friendly, and extensible. While Keras can very well handle rapid experimentation with Deep Neural Nets, it cannot support low-level computation and uses the “backend” library for this purpose. Speed is the biggest benefit of Keras. It has built-in support for data parallelism and can process large volumes of data while speeding up the time needed to train models.

Pandas

Pandas is one of the best open-source data manipulation and data analysis libraries available. It is based on NumPy that contributes various useful functions for accessing, merging, indexing,and grouping data. In fact, Pandas can be considered as the Python equivalent of Microsoft Excel in terms of any kind of tabular data, you must consider Pandas.

Pandas developed explicitly for data extraction and preparation. So, while it may not be directly related to ML, it comes in handy for data preparation before training ML models. It has many high-level data structures and a wide variety of tools for data analysis. Along with inbuilt methods for groping, combining and filtering data. Therefore, Pandas enable students of engineering colleges Jaipur to perform standard operations by writing only a few lines of code.

Conclusion

These are some Machine Learning libraries an individual can get their hands on. The Machine Learning libraries should take care of almost every ML need and requirement. Also, they can check their PG Diploma in Machine Learning and AI. Which provides practical hands-on workshops, 12 case studies and assignments, one-to-one industry mentor, and more.

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• machine learning libraries

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