Python libraries for AI and Machine learning are used by developers to perform complex tasks without the need to rewrite the code. In fact, one of the main reasons why the popularity of machine learning is growing tremendously is due to machine learning and deep learning libraries. The reason why Python libraries are preferred for developing sophisticated machine learning and deep learning models is because of its perfect combination of shorter development time, consistent syntax, and flexibility. Here are the most common and popular Python libraries for AI and ML.

Top Python Libraries For AI and Machine Learning

1- Python Libraries: TensorFlow 

TensorFlow is an open-source and free software library mainly used for differential programming. It is a math library that is used by machine learning applications and neural networks. While it was definitely not the first Python library in the world, it is the most popular library for machine learning because of its wide features and ease of use. TensorFlow supports a wide variety of toolkits for creating models of different levels of abstraction. 

While TensorFlow was mainly developed by Google’s Brain team for their internal use, it was released to the public in November 2015, under Apache License 2.0. It can run on a large number of platforms including GPUs, CPUs, and TPUs (Tensor Processing Unit, which is a hardware chip built with TensorFlow).

2- Python Libraries: Keras

Written in Python programming language, Keras is a neural network open-source library. It can run on top of TensorFlow and other libraries like Theano and PlaidML, which gives it an upper hand. Since the library is mainly designed for deep neural networks, it is modular, extensible, and user-friendly making it perfect for beginners. It seamlessly works with other building blocks of neural networks like objectives, layers, optimisers, and activation functions. It can run on both GPU as well as CPU and it also allows for fast prototyping. 

3- Python Libraries: Theano 

Theano is a Python library which is majorly used for fast numerical computation and it can run on both GPU and CPU. Since it is built on top of NumPy, Theano is pretty tightly integrated with NumPy and it has a similar interface as well. The library is perfect for manipulating and evaluating mathematical expressions as well as matrix calculations. With Theano, you can perform data-intensive computations that are up to 140x faster. It also has built-in tools for validation and unit testing, making it easier to avoid any problems or bugs. 

4- PyTorch

PyTorch is a deep learning library which is used by applications like natural language processing and computer vision. Developed by Facebook, it is open-source, free, and released under the modified BSD license. The Python library for AI and ML is based on Torch library, and that’s how it gets its name. PyTorch can easily be integrated with other Python data science stacks and it also helps developers in performing computations on tensors. 

The robust and seamless framework of PyTorch can create accurate computational graphs which can be changed even during runtime. The library also offers support for simplified preprocessors, numerous GPUs, and custom data loaders. 

5- Scikit-learn

Scikit-learn is a free machine learning library based on Python learning language which features a wide range of unsupervised and supervised learning algorithms. It is built on two of the basic Python libraries — SciPy and NumPy. It has numerous classification, clustering, and regression algorithms available in it like random forests, k-means, and gradient boosting. The library can also help with dimensionality reduction, preprocessing, and model selection. Developers mainly deploy the Scikit-learn library for data mining and analysis.

6- Python Libraries: Pandas

Pandas is a Python library which is consistently becoming more popular. It helps developers build high-level data structures that are intuitive and seamless. There are inbuilt methods available in Pandas for data filtering, combining, and grouping. It is highly stable and it can be used to perform time-series analysis. With Pandas, you can easily fetch and manipulate data from different sources like Excel, CSV, and JSON file. There are two main data structures available in Pandas — Series (One dimensional) and Data Frame (Two Dimensional).  

Python libraries allow developers to leverage the power of machine learning and artificial intelligence in the easiest way possible. If you are planning to learn and implement Python libraries for AI and ML, then it’s best to start by strengthening your machine learning basics. 

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