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With the development and popularity of artificial intelligence technology, Python has surpassed many other programming languages and has become one of the most popular and most commonly used programming languages in the field of machine learning. There are many reasons why Python is so sought after among many developers, one of which is that it has a large number of open source frameworks and tool libraries related to machine learning. According to builtwith.com, 45% of technology companies tend to use Python as a programming language in the field of artificial intelligence and machine learning.
Making Python so popular is mainly due to:
Python was designed from the beginning to be efficient, so that the project can maintain high productivity from development to deployment and operation and maintenance;
There are a large number of open source frameworks and tool libraries based on Python;
Python is easy to use, it can be said to be the gospel of programming white;
Compared to C, Java, and C++, Python's syntax is simpler and more advanced. It requires only a small number of lines of code to implement the same functions as other programming languages.
Python's cross-platform capabilities;
Because of its ease of use and high development efficiency, Python has attracted a large number of developers to create more new machine learning tool libraries. And because of the emergence of a large number of machine learning tool libraries, Python has become a machine learning field. So popular.
Let's explore the top ten most popular frameworks or tool libraries in machine learning:
If you are using Python for machine learning projects, then you must have heard of one of the famous frameworks - Tensorflow. The Tensorflow framework was developed primarily by the Google Brain team and is primarily used for deep learning calculations. Almost all Google machine learning apps use it. For example, when using Google Voice Search or Google Photos, you are actually indirectly using the model built by Tensorflow.
Tensorflow abstracts neural network operations into graphs, and an graph contains a large number of Tensor operations. The tensor is actually a collection of N-dimensional data. The essence of neural network computing is to fit the mapping between input tensor and output tensor by tensor operation.
Parallel computing is one of Tensorflow's main strengths. In other words, you can allocate your CPU and GPU computing resources through code settings to achieve parallelized graph operations.
All tool libraries in the Tensorflow framework are written in C or C++, but it provides an interface wrapper written in Python. In fact, your neural network model written in Python will eventually call the Tensorflow kernel written in C and C++ to perform the operation.
Tensorflow uses techniques such as XLA (Accelerated Linear Algebra) to optimize the computational process to ensure that it can flexibly call computational resources while maintaining efficient computational speed.
Keras is considered one of the coolest Python deep learning libraries. If you are new to deep learning development, then it is highly recommended that you use it. It provides a very concise mechanism to express neural network structures. It also provides a number of great tools for compiling neural network models, processing data, and visualizing network structures.
Keras essentially encapsulates the underlying frameworks such as Tensorflow and Theano to provide a unified API to simplify the construction and training of neural networks. If you plan to use Tensorflow as your back-end infrastructure, you must follow the diagram below:
Furthermore, Keras provides a number of preprocessed data sets, such as MNIST, and pre-trained models such as VGG, Inception, ResNet, and more.
Theano is a Python framework for multidimensional array calculations. Theano works like Tensorflow but is less efficient than Tensorflow. Therefore it does not apply to production environments.
In addition, Theano can be used in distributed or parallel environments similar to Tensorflow.
PyTorch is the largest deep learning library that allows developers to perform tensor calculations by accelerating GPUs, create dynamic calculation graphs, and automatically calculate gradients. In addition, PyTorch also provides a rich API for solving application problems related to neural networks.
This deep learning library is based on Torch, an open source machine library implemented in C, packaged in Lua language. The difference with Tensorflow is that Tensorflow uses the concept of "static calculation graph", while PyTorch uses the concept of "dynamic calculation graph". The most intuitive feeling is that the neural network model code written in PyTorch is more like the usual Python code. PyTorch was launched in 2017 and since its inception, the library has become increasingly popular and has attracted more and more machine learning developers.
Gradient Boosting is one of the best and most popular machine learning libraries, helping developers build new algorithms by using redefined basic models and decision trees. Therefore, specialized libraries are designed to implement this method quickly and efficiently. These libraries include LightGBM, XGBoost, and CatBoost. These libraries are competitors and use almost the same ideas to solve a common problem. These libraries all offer highly scalable, optimized, and fast gradient enhancement implementations that make them popular among machine learning developers. Because most machine learning developers have won machine learning competitions by using these algorithms.
Numpy is recognized as one of the most popular Python machine learning libraries. Tensorflow and some other frameworks use Numpy internally to perform multiple operations on tensors. The array interface is the best and most important feature of Numpy. This interface can be used to represent images, audio, and other binary stream data as a multidimensional real array. In order to apply this library to machine learning, mastering Numpy's operations is of great significance to developers.
Pandas is a Python machine learning library that provides a variety of advanced tools for data analysis. One of the great features is that it can perform complex data operations in one or two lines of code. Pandas has many built-in methods for grouping statistics, merging data, data filtering, and time series operations. All of these operations have excellent performance. Therefore, using Pandas is often used for data mining tasks.
SciPy is a machine learning library used by application developers and engineers. However, what you need to know is the difference between the SciPy library and SciPy-Stack. The SciPy library is a subset of SciPy-Stack. The SciPy library contains sub-modules such as optimizer, linear algebra, integration, interpolation, fast Fourier transform, signal and image processing, and statistics. The functions in all submodules are fully documented and easy to use.
The main function of the SciPy library is based on Numpy, and its array operations use Numpy's array operations.
Scikits-learn, also known as sk-learn, is a Python library based on Numpy and SciPy. Sk-learn is considered one of the best machine learning libraries for handling complex data. It contains a number of algorithms for implementing traditional machine learning and data mining tasks, such as data dimensionality reduction, classification, regression, clustering, and model selection.
As time progresses, sk-learn continues to evolve. This includes the addition of cross-validation capabilities that provide the ability to use multiple metrics. Many training methods have been improved, such as logistic regression, nearest neighbor algorithm (KNN) and so on.
Often, the challenge in machine learning tasks is that the model's predictions are inaccurate. The Eli5 machine learning library built in Python can help overcome this problem. It provides several built-in support for existing machine learning frameworks, such as model data visualization, model debugging, algorithm tracking, etc., making the machine learning model no longer a black box for developers.
Eli5 supports machine learning frameworks or machine learning libraries such as sk-learn, XGBoost, LightGBM, lightning, sklearn-crfsuite.
These frameworks and libraries can achieve the above mentioned tasks of visualization, model debugging, algorithm tracking and so on.
These are the top ten machine learning frameworks or tool libraries that machine learning experts and data scientists generally recognize. All of these frameworks and libraries are worth a look and try.
Of course, in addition to the frameworks and tool libraries mentioned above, there are many other machine learning libraries that are equally worthy of attention. For example, Scikit-image is another tool library that belongs to the Scikit series and focuses on the image field.
I hope this article will help you choose the right machine learning framework or tool library for your project.
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