If someone talks about machine learning and the name TensorFlow doesn’t come, it is not possible. TensorFlow is a Python friendly software library for dataflow which is developed by Google Brain team. It is a free and open-source software library that is used for research and production in google. Google released it in November 2015.

Machine learning is intimidating and now implementing machine learning models is less cumbersome, and the credit goes to the machine learning framework like Google TensorFlow. It provides training models that ease the process of acquiring data, refining future results and serving predictions. Now machine learning with TensorFlow is easy.

TensorFlow uses Python to provide an appropriate front end Application programming interface For building applications with the framework. While it executes these applications in high-performance C++. TensorFlow offers an application programming interface to a beginner or expert to develop for Mobile, desktop, Cloud, and Web. Now, most of the renowned companies are using TensorFlow; some of them are CocaCola, PayPal, Intel, Lenovo, Airbnb, DropBox, Airbus, Carousell, CEVA and many more.

95% of business execs who indicated that they are skilled at using big data to solve business problems or generate insights also use AI technologies.

Top Libraries of AI and ML

Most of the machine learning and artificial intelligence projects are developed in Python language because it is easy to use and open source. A lot of the libraries work on Python language. There are various libraries and frameworks for artificial intelligence and machine learning. Here I am going to explain some of the AI and ML libraries.

➡️ TensorFlow

The main advantage to learn TensorFlow that is, it supports neural networks and deep learning algorithms. It is an end to end machine learning library which performs high-end computations and also supports TPU, CPU and GPU calculations. With the help of this library, TensorFlow developers can leverage AI and ML to build unique applications, which can respond to the user input like facial expressions and voice command.

➡️ Scikit-learn

Scikit learn is also an open-source python machine learning library with a vast range of algorithms. Scikit-learn is an efficient, simple and easy to use the tool. It is accessible to everyone and can be reused in various contexts. Scikit provides almost every model like Lasso-Ridge, Linear Regression, Decision Trees, Logistics Regression, SVMs and a lot more.

➡️ Pandas

It is a library that is similar to Microsoft Excel and used for tabular data. With the help of the Pandas library, you can access data from various sources like SQL database, Excel, CSV and JSON files. Pandas allow you to manage complex data operations with the coding of lines or two. If you are looking to become a data scientist or want to master machine learning competitions, then Python Pandas it the best tool for you, which can reduce your problems.

➡️ Keras

Keras is an open-source library that is easy to use for beginners to design a neural network. Keras is a popular, mature and high end deep learning library which uses many low-level libraries in backed like TensorFlow, Microsoft Cognitive Toolkit, Theano, and CNTK. Keras offers almost every module like neural layers, initialization schemes, optimizers, cost functions, activation functions, and regularization schemes.

➡️ PyTorch

In addition to GPU and CPU computation, it also offers performance optimization in production and research. It is an open-source deep-learning library that was developed by Facebook. PyTorch has a machine learning compiler that is named as Glow, and It improves the performance of deep learning framework. PyTorch is easier to learn and use in comparison to TensorFlow. PyTorch is less mature than TensorFlow.

➡️ NumPy

A core component of Scikit-learn and Pandas is NumPy. NumPy supports multi-dimensional matrix and arrays with core linear algebra operations. Other machine learning libraries like SciPy, Mat-Plotlib, and SciKit-learn depends upon NumPy. It can deal with complicated mathematical operations like Fourier Transformation, matrix and arrays, random numbers and linear algebra. NumPy can execute scientific computations.

In the end

If you are starting your journey in artificial intelligence and machine learning, then it would be better to choose a library that supports all the operations you need. Also, there is so much scope in machine learning and artificial intelligence for Machine learning developers and artificial intelligence developers because, in this time, every software development company has a team of AI and ML developers.