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Today, I would like to share with you which libraries in Python can be used to deal with AI and ML tasks, detailed content, clear logic, I believe most people still know too much about this knowledge, so share this article for your reference, I hope you can get something after reading this article, let's take a look at it.

NLTK

NLTK (Natural Language Toolkit) provides many software packages that deal with natural language processing (NLP). NLP is a branch of artificial intelligence related to computational linguistics that focuses on understanding and interpreting written human language. With NLTK, you can analyze sentences, classify words, perform emotional analysis, and so on.

OpenCV

OpenCV (Open computer Vision) is a library for optimizing real-time computer vision and machine learning. It can use filter and conversion to process the image, detect the features in the image and extract data from it; it is used in optical character recognition (OCR), face detection, object tracking and other applications.

Keras

Keras is a high-level library for deep learning and neural networks in Python. It runs on top of TensorFlow, Theano, or CNTK deep learning frameworks. Keras is more popular than using TensorFlow alone or using the PyTorch framework because it is the easiest rapid prototyping deep learning project of the three.

TensorFlow

TensorFlow is an open source library for developing and training machine learning models. It provides high-level and low-level API. It has higher performance than Keras, so it is more often used in large data sets. Keras is now included in the TensorFlow toolkit as an advanced API for TensorFlow to build and train prototypes of deep learning models.

PyTorch

PyTorch is a deep learning framework. It focuses on tensor computing (such as NumPy, but using GPU acceleration) and deep neural networks.

Scikit-learn

Scikit-learn is a library that provides a variety of "traditional" machine learning methods (linear models, support vector machines, decision trees, etc.). However, it does not include deep learning, because deep learning is quite professional, while machine learning libraries usually focus on deep learning or traditional machine learning methods. This makes scikit-learn a completely different beast from Keras, TensorFlow and PyTorch.

NumPy and SciPy

NumPy and SciPy are not libraries dedicated to machine learning, but they are an important part of this overview because they provide basic tools for scientific computing.

NumPy provides powerful N-dimensional arrays, linear algebra, Fourier transforms, and works well with other libraries such as OpenCV (which is not surprising, as they are all built on NumPy).

SciPy works on top of NumPy's array and provides functions for numerical analysis, such as interpolation, optimization, integration, differential equation solvers, statistics, and so on.

Matplotlib

Matplotlib is a library for creating visualization in Python. You can use it to create static, animated, or interactive charts and graphics in 2D or 3D format.

Pandas

Pandas is a library for data analysis and manipulation. It can read / write files in many different formats and provides many features to manage your data: indexing, selecting, merging, reshaping, and so on.

Choose what?

As you can see, you can make a lot of choices.

If you are an enterprise developer, you may want to choose an advanced library, such as NLTK, OpenCV, or Keras. This will get you back on track as soon as possible.

Libraries such as TensorFlow or the advanced parts of PyTorch can help you build neural networks without worrying about low-level complexity.

Low-level solutions, such as some parts of TensorFlow, or doing everything with NumPy, usually require more work, so it takes longer to deliver solutions, and these solutions are more difficult to maintain.

That's all of the article "which libraries in Python can be used to handle AI and ML tasks". Thank you for reading! I believe you will gain a lot after reading this article. The editor will update different knowledge for you every day. If you want to learn more knowledge, please pay attention to the industry information channel.

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