There is a saying that "it's not Python that's powerful, it's the library"!
Yes, Python itself is indeed much more elegant and concise than other languages, but its ease of use is mainly due to its very large and powerful library. These libraries can be used for data cleaning, data manipulation, visualization, building models and even model deployment, so this is why Python is so popular among developers. So today, let’s take stock of some of the more useful libraries:
1、TensorFlow
What is TensorFlow?
This library was developed by Google in collaboration with Brain Team. TensorFlow is used for machine learning in almost every Google application.
TensorFlow works like a computing library for writing new algorithms involving a large number of tensor operations. Since neural networks can be easily represented as computational graphs, they can be implemented using TensorFlow as a sequence of operations on Tensors. Furthermore, tensors are N-dimensional matrices that represent data.
Features of TensorFlow
TensorFlow is optimized for speed, leveraging technologies such as XLA to enable fast linear algebra operations.
Using TensorFlow we can easily visualize every part of the graph, which is not an option when using Numpy or SciKit. One of the very important features of Tensorflow is that its operability is very flexible, meaning it is modular and for the parts you want to be independent of, it gives you that option.
It can be easily trained on CPU and GPU for distributed computing. TensorFlow provides pipelining, in the sense that you can train multiple neural networks and multiple GPUs, which makes the model very efficient on large-scale systems. There are already a large number of software engineers working continuously on stability improvements. And it's open source, so anyone with an internet connection can use it.
2、NumPy
What is 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 Numpy's best and most important feature.
Features of Numpy
Numpy is very interactive and easy to use; makes implementing complex mathematics very simple; makes coding straightforward and understanding concepts easy; is widely used and therefore has many open source contributions.
3、Scikit-Learn
What is Scikit-Learn?
It is a Python library associated with NumPy and SciPy. It is considered one of the best libraries for handling complex data. There are many variations in this library. One modification is the cross-validation feature, which allows the use of multiple metrics. Many training methods such as logistic regression and nearest neighbor have received some improvements.
Features of Scikit-Learn
There are various ways to check the accuracy of supervised models on unseen data;
Unsupervised Learning Algorithms:
Likewise, there are a large number of algorithms on offer - from clustering, factor analysis and principal component analysis to unsupervised neural networks;
for Extract features from images and text.
4、Pandas
What is Pandas?
When it comes to data manipulation and data analysis, Pandas is unmatched. The name comes from the term "panel data," an econometric term that refers to a data set containing observations of the same individuals over multiple time periods.
Features of Pandas:
1. Connect and merge data sets
2. Delete and insert data structure columns
3. Data filtering
4. Reshape data sets
5. Use DataFrame objects to manipulate data, etc.
5. Seaborn
What is Seaborn?
Seaborn is a module based on matplotlib. It specializes in statistical visualization and can be seamlessly connected with pandas, making it easier for beginners to get started. Compared with matplotlib, Seaborn's syntax is more concise, and the relationship between the two is similar to the relationship between numpy and pandas.
Seaborn Features:
1. As a dataset-oriented API that can be used to examine relationships between multiple variables
2. Facilitate viewing of the overall structure of complex datasets
3. Tools for selecting color palettes that display patterns in the data
6、HoloViews
What are HoloViews
HoloViews can complete data analysis and visualization with very little code. In addition to the default matplotlib backend, a Bokeh backend has been added. Bokeh provides a powerful platform. By combining the interactive widgets provided by Bokeh, you can use HTML5 canvas and WebGL to quickly generate interactive and high-dimensional visualizations, which is very suitable for interactive exploration of data.
7、PyTorch
What is 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 for solving application problems related to neural networks. This machine learning library is based on Torch, an open source machine library implemented in C with wrappers in Lua. It was launched in 2017, and since its inception, the library has grown in popularity and attracted more and more machine learning developers.
Features of PyTorch
The new hybrid front end provides ease of use and flexibility in eager mode while seamlessly transitioning to graphics mode for speed, optimization and functionality in the C++ runtime environment. Optimize performance in research and production by leveraging native support for asynchronous execution of collection operations and peer-to-peer communication accessible to Python and C++.
PyTorch is not a Python binding to a monolithic C++ framework. It is built for deep integration into Python, so it can be used with popular libraries and packages like 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.
8、Hard
What is Keras?
Keras is considered one of the coolest machine learning libraries in Python. It provides a mechanism for expressing neural networks more easily. Keras also provides some of the best tools for compiling models, working with datasets, graph visualization, and more. On 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 it with other machine learning libraries, Keras is relatively slow because it creates a computational graph by using backend infrastructure and then leverages it to perform operations.
Features of Keras
It runs smoothly on both CPU and GPU. Keras supports almost all models of neural networks - fully connected, convolutional, pooling, recurrent, embedding, etc.
Additionally, these models can be combined to build more complex models. Keras' modular nature makes it incredibly expressive, flexible, and suitable for innovative research.
There are really too many free and open source libraries for Python. I will introduce them here today, and I will share them with you later ~
Technical reserves about Python
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