Python can be said to be the sharpest weapon for machine learning; and machine learning for Python has the power to expand its influence and create glory. The two complement each other, so that when it comes to machine learning, people naturally think of Python. Although it is a bit narrow, there is also the inevitability of its existence behind it!
Today we will introduce the 10 most important third-party libraries related to Python machine learning in 2021, don’t miss it
TensorFlow
If you are currently using Python for machine learning projects, you must have heard of this popular open source library TensorFlow
This library is developed by Google in collaboration with the Brain Team. TensorFlow is a part of almost all Google machine learning applications.
TensorFlow is like a computing library for writing new algorithms that involve a lot of tensor operations, as neural networks Easily represented as computational graphs, they can be implemented using TensorFlow as a sequence of operations on tensors. In addition, a tensor is an N-dimensional matrix that represents data and is an important concept in machine learning.
TensorFlow is optimized for speed. It uses technologies such as XLA to perform fast linear algebra operations.
Using TensorFlow, we can easily visualize every part of the graph, which is unlikely when using Numpy or SciKit
One of the very important features of Tensorflow is that its operability is very flexible, which means that it is highly modular and also gives us the option to make certain functions independently
It is easy to train on CPU and GPU for distributed computing
In a sense, TensorFlow provides Pipeline, we can train multiple neural networks on multiple GPUs, which makes the model very efficient on large-scale systems
Because it is developed by Google, then There is already a large team of software engineers constantly working on stability improvements, and its developer community is very active. You are not fighting alone
The best thing about this machine learning library is It's open source, so anyone with an internet connection can use it
Scikit-Learn
It is a Python library associated with NumPy and SciPy. It is considered one of the best libraries for processing complex data
Many optimization changes have been made in this library , one of which is the cross-validation feature, which provides the ability to use multiple metrics. Many training methods, such as logistic regression and nearest neighbor, have received some small improvements and optimizations
There are multiple ways to do this Check the accuracy of supervised models on unseen data
There are many types of algorithms in the product, including clustering, factor analysis, principal component analysis, and unsupervised neural networks
Used to extract features (such as bag of words) from images and text
Numpy
Numpy is considered one of the most popular machine learning libraries in Python
TensorFlow and other libraries use Numpy internally to perform multiple operations on tensors , the array interface is the best and most important feature of Numpy
Numpy is interactive and very easy to use
can make complex mathematical implementations very simple
makes coding really easy, and easy to grasp concepts
Widely used, so there are many open source contributors
Keras
Keras is considered one of the coolest machine learning libraries in Python, providing a simpler mechanism to express neural networks. Keras also provides some of the best utilities for compiling models, processing datasets, graph visualization, etc.
In the backend, Keras uses Theano or TensorFlow internally. Some of the most popular neural networks, such as CNTK, can also be used. When we compare Keras to other machine learning libraries, it is relatively slow. Because it creates a computational graph using backend infrastructure and then leverages it to perform operations. All models in Keras are portable
It can run smoothly on CPU and GPU
Keras supports almost all models of neural networks - fully connected, convolution, pooling, loop, embedding, etc. Additionally, these models can be combined to build more complex models
Keras is modular in nature, allowing for incredible expressiveness, flexibility, and innovative research capabilities
Keras is a framework completely based on Python, easy to debug and explore
PyTorch
PyTorch is the largest machine learning library that allows developers to perform tensor calculations with GPU acceleration, create dynamic computational graphs, and automatically calculate gradients. In addition, PyTorch also provides a rich API to solve application problems related to neural networks
This machine learning library is based on Torch, which is an open source machine library implemented in C language and implemented in Lua Encapsulated in
This Python machine library was launched in 2017. Since its inception, the library has become increasingly popular and attracted more and more machine learning developers
The new hybrid front end provides ease of use and flexibility in Eager mode while seamlessly transitioning to graphical mode for speed, optimization and Features
Optimize performance in research and production by leveraging native support for asynchronous execution of collective operations and peer-to-peer communication accessible from Python and C
It is built to be deeply integrated into Python, so it can be used with popular libraries and packages such as Cython and Numba
An active community of researchers and developers has built a rich ecosystem of tools and libraries for extending PyTorch and supporting development in areas ranging from computer vision to reinforcement learning
LightGBM
Gradient Boosting is one of the best and most popular machine learning libraries, which works by using a redefined basic model (i.e. decision tree ) helps developers build new algorithms. Therefore, there are some special libraries that can be used to implement this method quickly and efficiently
These libraries are LightGBM, XGBoost and CatBoost. All these libraries are helpful in solving common problems and can be used in almost similar way
Very fast calculations ensure high productivity Efficiency
Intuitive and therefore very user friendly
Has faster training speed than many other deep learning libraries
No errors will be generated when considering NaN values and other canonical values
Eli5
Most of the time, machine learning models predict inaccurate results, and the Eli5 machine learning library built with Python helps overcome this problem. It combines visualization and debugging of all machine learning models and traces all working steps of the algorithm
Eli5 also supports many libraries such as XGBoost, lightning, scikit-learn and sklearn-crfsuite etc
SciPy
SciPy is a machine for application developers and engineers Learning library. The SciPy library contains modules for optimization, linear algebra, integration and statistics
The main feature of the SciPy library is that it is developed using NumPy and its arrays make maximum use of NumPy
Furthermore, SciPy provides all efficient numerical routines such as optimization, numerical integration and many other programs using its specific submodules
All functions in all submodules of SciPy are Well documented
Theano
Theano is a Python library for calculations A machine learning library for computing frameworks on multidimensional arrays. Theano works similarly to TensorFlow, but is not as efficient as TensorFlow, so it cannot be adapted to production environments
In addition, Theano can also be used in distributed or parallel environments similar to TensorFlow
Ability to use complete NumPy arrays in Theano compiled functions
Perform data-intensive calculations Much faster than on CPU
Theano can differentiate functions with one or more inputs
Even if x is very small, the correct answer of log(1 x) can be obtained. Of course this is just one example showing the stability of Theano
Evaluate expressions faster than ever, resulting in vastly improved efficiency
Detection and diagnostics in models Many types of errors and ambiguities
Pandas
Pandas is in Python A machine learning library that provides advanced data structures and various analysis tools. A great feature of this library is the ability to transform complex data operations using just one or two commands. Pandas has many built-in methods for grouping, combining data, and filtering, as well as time series functionality Support for operations such as iteration, sorting, aggregation, connection and visualization is one of the functional highlights of Pandas
The above is the detailed content of Top 10 Python Machine Learning Libraries of 2021. For more information, please follow other related articles on the PHP Chinese website!
![[Web front-end] Node.js quick start](https://img.php.cn/upload/course/000/000/067/662b5d34ba7c0227.png)
