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This article mainly explains "what are the best Python machine learning libraries". The content in the article is simple and clear, and it is easy to learn and understand. Please follow the editor's train of thought to study and learn what the best Python machine learning libraries are.

Introduction

There is no doubt that neural network and machine learning have been one of the hottest topics in the high-tech field in the past few years. This is easy to see because they address many really interesting use cases, such as speech recognition, image recognition, and even music composition. Therefore, in this article, I decided to compile a list of some good Python machine learning libraries and post them below.

In my opinion, Python is one of the best languages for learning (and implementing) machine learning technology for the following reasons:

Language simplicity: today, Python is the language of choice for novice programmers mainly because of its simple syntax and large community.

Powerful: just because the grammar is simple doesn't mean it's weak. Python is also one of the most popular languages for data scientists and Web programmers. The libraries created by the Python community allow you to do whatever you want, including machine learning.

Rich ML libraries: there are a large number of Python-oriented machine learning libraries. You can choose the most appropriate one from hundreds of libraries according to your usage, technology, and requirements.

The last point above can be said to be the most important. The algorithms that drive machine learning are very complex, including a lot of mathematical knowledge, so it will be a difficult task to implement them yourself (and ensure their normal operation). Fortunately, there are a lot of smart, dedicated people who have done this difficult job for us, so we just need to focus on the application at hand.

This is not an exhaustive list. There is a lot of code not listed here, here I will only release some very relevant or well-known libraries. Next, let's take a look at this list.

The most popular library

I have given a brief description of some of the more popular libraries and what they are good at, and I will give a more complete list of projects in the next section.

Tensorflow

This is the latest neural network library in the list. Just released a few days ago, Tensorflow is an advanced neural network library that can help you design your network architecture and avoid low-level details. The point is that it allows you to represent the calculation as a data flow graph, which is more suitable for solving complex problems.

This library is mainly written in C++, including Python bindings, so you don't have to worry about its performance. One of my favorite features is its flexible architecture that allows you to deploy it to one or more CPU or GPU desktops, servers, or mobile devices using the same API. There are not many libraries with this feature, and if any, Tensorflow is one of them.

It was developed for the Google brain project and has been used by hundreds of engineers, so there is no doubt that it can create interesting solutions.

Although, like other libraries, you may have to spend some time learning its API, it should be worth the time. It only took me a few minutes to understand its core functions, and I already knew that Tensorflow was worth more of my time to implement my web design, not just through API.

Good at: neural network

Scikit-learn

Scikit-learn is definitely one of them, if not the most popular, then it is one of the popular machine learning libraries of all languages. It has a large number of data mining and data analysis functions, making it the preferred library for researchers and developers.

It has built-in popular NumPy and SciPy,matplotlib libraries, so it's familiar to many people who already use these libraries. Although this library is slightly lower-level than the other libraries listed below, it tends to serve as the basis for many other machine learning implementations.

Good at: a lot

Theano

Theano is a machine learning library that allows you to define, optimize, and evaluate mathematical expressions involving multidimensional arrays, which can be a setback for other library developers. Like scikit-learn, Theano integrates the NumPy library well. The transparent use of GPU allows Theano to be set up quickly and without error, which is very important for beginners. However, some people describe it more as a research tool than as a product, so it should be used on demand.

One of the best features of Theano is its excellent reference documentation and extensive tutorials. In fact, thanks to the popularity of this library, you won't have too much trouble finding resources, such as how to get your model and run it.

Good at: neural network and deep learning

Pylearn2

Most Pylearn2 functions are actually built on top of Theano, so it has a very solid foundation.

According to the Pylearn2 website:

Unlike scikit-learn,Pylearn2, Pylearn2 is designed to provide great flexibility so that researchers can do almost anything they want, while the purpose of scikit-learn is to work as a "black box" that can produce good results even if users do not understand the implementation.

Remember, Pylearn2 encapsulates other libraries, such as scikit-learn, when appropriate, so you won't get 100% user-written code here. However, this is really good, because most of the errors have been resolved. Wrapper libraries like Pylearn2 play an important role in this list.

Good at: neural network

Pyevolve

One of the more exciting and different areas of neural network research is genetic algorithms. Fundamentally speaking, genetic algorithm is only a heuristic search process that simulates natural selection. In essence, it tests the neural network on some data and gets the feedback of the network performance from a fitting function. Then iteratively make small, random changes to the network, and then test with the same data. Take the network with a high fitting score as the output, and then make it the parent node of the next network.

Pyevolve provides a great framework for building and executing such algorithms. The author has said that V0.6 also supports genetic programming, so in the near future, the framework will be more likely to be an evolutionary computing framework than a simple genetic algorithm framework.

Good at: neural network of genetic algorithm

NuPIC

Nupic is another library that provides some different functions compared to standard machine learning algorithms. It is based on a neocortical theory called hierarchical time memory (HTM). HTMs can be regarded as a kind of neural network, but it is different in some theories.

Fundamentally speaking, HTMs is a hierarchical, time-based memory system that can accept all kinds of data. This means that it will become a new computing framework to mimic how memory and computing in our brains are inextricably linked. For a detailed description of the theory and its application, please refer to the white paper.

Good at: HTMs

Pattern

This library is more like a "complete" library, because it not only provides some machine learning algorithms, but also provides tools to help you collect and analyze data. The data mining section can help you collect data from web services such as Google, Twitter and Wikipedia. It also has a Web crawler and a HTML DOM parser. "the advantage of introducing these tools is that it is easier to collect and train data in the same program.

A good example in the document is to use a bunch of tweets to train a classifier to distinguish whether a tweet is "win" or "fail".

From pattern.web import Twitter from pattern.en import tag from pattern.vector import KNN, counttwitter, knn = Twitter (), KNN () for i in range (1,3): for tweet in twitter.search ('# win OR # fail', start=i, count=100): s = tweet.text.lower () p ='# win' in s and 'WIN' or' FAIL' v = tag (s) v = [word for word Pos in v if pos = = 'JJ'] # JJ = adjective v = count (v) # {' sweet': 1} if v: knn.train (v, type=p) print knn.classify ('sweet potato burger') print knn.classify (' stupid autocorrect')

First use twitter.search () to collect tweet data through the tags'# win' and'# fail'. Then the adjectives extracted from tweets are used to train a K-nearest neighbor (KNN) model. After enough training, you will get a classifier. It only takes 15 lines of code, which is not bad.

Good at: natural language processing (NLP) and classification.

Caffe

Caffe is a machine learning library for vision applications. You may use it to create a deep neural network, identify entities in an image, and even recognize a visual style.

Caffe provides seamless integration of GPU training, which is highly recommended when you train images. Although Caffe seems to be primarily academic and research-oriented, it also has enough use for training models for production use.

Good at: neural network / visual deep learning

Thank you for your reading, the above is the content of "what are the best Python machine learning libraries". After the study of this article, I believe you have a deeper understanding of what the best Python machine learning libraries have, and the specific use needs to be verified in practice. Here is, the editor will push for you more related knowledge points of the article, welcome to follow!

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