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This article introduces the relevant knowledge of "what Python libraries are there". In the operation process of actual cases, many people will encounter such difficulties. Next, let Xiaobian lead you to learn how to deal with these situations! I hope you can read carefully and learn something!

1.Keras

Keras is a machine learning API written in python that runs on TensorFlow, an excellent platform for machine learning. The advantage is that you can quickly build the network model, data input and output is also very convenient, so that you can focus on the network model itself, suitable for beginners. The biggest drawback is slow! As a high-level API, reasoning speed and so on are definitely not as fast as tf and mxnet.

2. PyTorch

It handles N-dimensional tensors, similar to numpy, but runs on GPUs. Support automatic differentiation to build and train large neural networks.

3. fastai

Fastai simplifies the training process and speeds up the neural network very quickly by leveraging current best practices. One API contains almost all common deep learning applications.

4. JAX

Jax is a combination of Autograd and XLA to provide high-performance machine learning research. As an updated version of Autograd, JAX can automatically differentiate native Python and Numpy functions. It can be differentiated by loops, branches, recursion, and closures, and it is possible to derive derivatives of derivatives. Supports reverse mode differentiation via grad.

5. FastText

It is a library that allows you to efficiently learn word ideation and sentence classification.

6. spaCy

SpaCy v3.0 features all the new transformer-based pipes, bringing spaCy's accuracy to SOTA. You can use any pre-trained transformer to train your own pipeline, or you can share transformers between multiple components and tasks. SpaCy's transformer supports integration with PyTorch and HuggingFace transformers libraries, and many pre-trained models are accessible in the pipeline.

7. gensim

It uses large corpora for topic modeling, document indexing and similarity retrieval. The target audience is the Natural Language Processing (NLP) and Information Retrieval (IR) communities.

8. NLTK

It is a natural language toolkit, a complete set of open-source Python modules, datasets, and tutorials for natural language processing research and development.

9. TextBlob

Simple, Python-style, is a library for processing text data. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more.

10. Pillow

It is a very user-friendly PIL fork. PIL is a Python image library.

11. OpenCV

Open source computer vision library.

12.LightGBM

It helps developers build new algorithms using a redefined base model, the decision tree. LightGBM features: fast calculation speed, high productivity, intuitive, easy to use. This library provides highly scalable, optimized, and fast gradient-enhancement implementations, which makes it popular among machine learning developers.

13.Pandas

Pandas is a machine learning library in Python that provides advanced data structures and a variety of analysis tools. It is primarily the ability to transform complex data operations using one or two commands. It also has many built-in functions for grouping, data combining, filtering, and time series functions. Pandas guarantees high performance and flexibility when used with other libraries.

14.. Numpy

Array interfaces are Numpy's best feature. It is very easy to understand and use, making complex mathematical implementations very simple. Widely used, so there are a lot of open source contributors. This interface can be used to represent images, sounds, and other binary raw streams as n-dimensional real arrays.

15.Click

Click was originally developed to create a beautiful command-line interface in a composable way with minimal code. It is designed to make the process of writing command-line tools quick and fun while preventing any problems due to not implementing the expected CLI API. There are three main features: arbitrary nesting of commands, automatic help page generation, and support for delayed loading of subcommands at runtime.

"What Python libraries are there" is introduced here, thank you for reading. If you want to know more about industry-related knowledge, you can pay attention to the website. Xiaobian will output more high-quality practical articles for everyone!

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