For machine learning, Python is arguably the sharpest weapon; and for machine learning, for Python, it has the power to expand its influence and recreate its 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
what TensorFlow
If you are currently working on a machine learning project with Python, you must have heard of this popular open source library, TensorFlow
Developed by Google in collaboration with the Brain Team, TensorFlow is part of almost all Google machine learning applications
TensorFlow is like a computational library for writing new algorithms involving large numbers of tensor operations, and because neural networks can be easily represented as computational graphs, they can be implemented using TensorFlow as a sequence of operations on tensors. In addition, tensors are N-dimensional matrices that represent data and are an important concept in machine learning
Features of TensorFlow
TensorFlow is optimized for speed and leverages techniques like XLA for fast linear algebra operations
responsive construction
Using TensorFlow, we can easily visualize every part of the graph, which is not possible when using Numpy or SciKit
flexible
One of the very important features of Tensorflow is that its operability is very flexible, which means it is highly modular and also gives us the option to make some functions independently
easy to train
It is easy to train on CPU and GPU for distributed computing
Parallel Neural Network Training
In a sense, TensorFlow provides pipelines where we can train multiple neural networks on multiple GPUs, which makes the model very efficient on large scale systems
Huge active community
Because it's developed by Google, there's already a huge team of software engineers constantly working on stability improvements, and its developer community is very active, you're not fighting alone
open source
The best thing about this machine learning library is that it is open source, so anyone with internet access can use it
Scikit-Learn
What is Scikit-learn
It is a Python library associated with NumPy and SciPy, and it is considered one of the best libraries for working with complex data
A number of 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 neighbors, have received some small improvements and optimizations
Features of Scikit-Learn
Cross-validation
There are various ways to check the accuracy of supervised models on unseen data
Unsupervised Learning Algorithms
A wide variety of algorithms are available in the product, including clustering, factor analysis, principal component analysis, unsupervised neural networks
Feature extraction
For extracting features from images and text (e.g. bag of words)
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, and the array interface is the best and most important feature of Numpy
Features of Numpy
interactive
Numpy is interactive and very easy to use
Mathematical calculation
Can make complex math implementation very simple
intuitive
Makes coding really easy and easy to grasp concepts
open source
Widely used, so there are many open source contributors
Hard
What is Keras
Considered to be one of the coolest machine learning libraries in Python, Keras provides an easier mechanism to express neural networks. Keras also provides some of the best utilities 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 Keras to other machine learning libraries, it is relatively slow. Because it creates a computational graph by using the backend infrastructure and then leverages it to perform operations. All models in Keras are portable
Features of Keras
Supports CPU and GPU
It runs smoothly on CPU and GPU
The model is comprehensive
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
modular
Modular in nature, Keras is incredibly expressive, flexible, and capable of innovative research
Completely based on Python
Keras is a completely Python-based framework that is easy to debug and explore
PyTorch
What is PyTorch
PyTorch is the largest machine learning library that allows developers to perform tensor computations with GPU acceleration, create dynamic computational graphs, and automatically compute gradients. In addition to this, PyTorch also provides a rich API to solve application problems related to neural networks
This machine learning library is based on Torch, an open source machine library implemented in C and packaged in Lua
Launched in 2017, this Python machine library has grown in popularity and attracted more and more machine learning developers since its inception
Features of PyTorch
Hybrid front end
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 a C++ runtime environment
Distributed training
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++
Python first
It is built to be deeply integrated into Python, so it can be used with popular libraries and packages such as Cython and Numba
Numerous libraries and tools
An active community of researchers and developers has built a rich ecosystem of tools and libraries to extend PyTorch and support development in areas ranging from computer vision to reinforcement learning
LightGBM
What is LightGBM
Gradient Boosting is one of the best and most popular machine learning libraries that helps developers build new algorithms by using redefined base models (i.e. decision trees). So there are special libraries that can be used to implement this method quickly and efficiently
These libraries are LightGBM, XGBoost and CatBoost. All of these libraries are helpful in solving common problems and can be used in an almost similar way
Features of LightGBM
fast
Very fast calculations ensure high productivity
intuitive
Intuitive and therefore very user friendly
train faster
Has faster training speed than many other deep learning libraries
fault tolerance
No errors are generated when considering NaN values and other canonical values
Eli5
What is Eli5
Most of the time, machine learning models predict inaccurate results, and the Eli5 machine learning library built in Python helps overcome this problem. It combines visualization and debugging of all machine learning models and traces all working steps of the algorithm
Features of Eli5
Eli5 also supports many libraries, such as XGBoost, lightning, scikit-learn and sklearn-crfsuite, etc.
SciPy
What is SciPy
SciPy is a machine learning library for application developers and engineers. The SciPy library contains modules for optimization, linear algebra, integration, and statistics
Features of SciPy
The main feature of the SciPy library is that it was developed using NumPy and its arrays make maximum use of NumPy
Additionally, SciPy provides all efficient numerical routines such as optimization, numerical integration and many others using its specific submodules
All functions in all submodules of SciPy are well documented
Theano
What is Theano
Theano is a computational framework machine learning library for computing multidimensional arrays in Python. Theano works similar to TensorFlow, but is not as efficient as TensorFlow, so it is not suitable for production environments
Additionally, Theano can also be used in a distributed or parallel environment similar to TensorFlow
Features of Theano
Tight integration with NumPy
Ability to use full NumPy arrays in Theano-compiled functions
Efficient use of GPU
Perform data-intensive computations much faster than on a CPU
Efficient symbolic differentiation
Theano can take derivatives of functions with one or more inputs
Speed and stability optimizations
The correct answer of log(1+x) can be obtained even if x is very small. Of course this is just one of the examples showing the stability of Theano
Dynamic C code generation
Evaluate expressions faster than ever, resulting in vastly improved efficiency
Extensive unit testing and self-verification
Detect and diagnose many types of errors and ambiguities in models
Pandas
What is Pandas
Pandas is a machine learning library in Python that provides advanced data structures and various analytical tools. A great feature of this library is the ability to use a command or two to transform complex data manipulations. Pandas has many built-in methods for grouping, combining data, and filtering, as well as time series capabilities
Features of Pandas
Pandas makes the whole process of manipulating data easier, and support for operations such as re-indexing, iteration, sorting, aggregation, joining, and visualization is one of Pandas's functional highlights
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