Top 10 Machine Learning Libraries to Bookmark

Source: Machine Learning Algorithms

Python is arguably the sharpest weapon for machine learning; conversely, machine learning provides the power to expand influence and recreate brilliance for Python. The two complement each other to such an extent that whenever machine learning is mentioned, people naturally think of Python. Although this perspective may be somewhat narrow, there is an inevitability behind its existence!

Today, we will introduce the 10 most important third-party libraries related to Python machine learning in 2021, so don’t miss out!

1. TensorFlow

1. What is TensorFlow?

If you are currently using Python for machine learning projects, you have certainly heard of this popular open-source library, TensorFlow.

This library was developed in collaboration with Google and the Brain Team, and TensorFlow is part of almost all Google machine learning applications.

TensorFlow functions like a computational library designed to write new algorithms involving numerous tensor operations, as neural networks can be easily represented as computational graphs, which can be implemented using TensorFlow as a series of operations on tensors. Additionally, tensors are N-dimensional matrices that represent data, which is a critical concept in machine learning.

2. Features of TensorFlow

TensorFlow is optimized for speed, utilizing technologies like XLA for fast linear algebra computations.

• Responsive Construction: With TensorFlow, we can easily visualize every part of the graph, which is unlikely when using Numpy or SciKit.

• Flexible: One of the most important features of TensorFlow is its operational flexibility, meaning it has a high degree of modularity and also provides us with the option to independently create certain functionalities.

• Easy Training: It is easy to train on both CPU and GPU for distributed computing.

• Parallel Neural Network Training: In a sense, TensorFlow provides a pipeline where we can train multiple neural networks on multiple GPUs, making the model highly efficient on large-scale systems.

• Large Active Community: Because it is developed by Google, there is already a large team of software engineers continuously working on stability improvements, and its developer community is very active, so you are not alone in this battle.

• Open Source: The best aspect of this machine learning library is that it is open-source, so anyone can use it as long as they have internet access.

2. Scikit-Learn

1. What is Scikit-learn?

It is a Python library associated with NumPy and SciPy, considered one of the best libraries for handling complex data.

Many optimizations have been made in this library, one of which is the cross-validation feature, providing the ability to use multiple metrics. Many training methods, such as logistic regression and nearest neighbors, have received some minor improvements and optimizations.

2. Features of Scikit-Learn

• Cross-Validation: There are various methods to check the accuracy of supervised models on unseen data.

• Unsupervised Learning Algorithms: The product includes a variety of algorithms, including clustering, factor analysis, principal component analysis, and unsupervised neural networks.

• Feature Extraction: Used to extract features from images and text (e.g., bag of words).

3. Numpy

1. What is Numpy?

Numpy is considered one of the most popular machine learning libraries in Python.

TensorFlow and other libraries internally use Numpy to perform multiple operations on tensors, and the array interface is the best and most important feature of Numpy.

2. Features of Numpy

• Interactive: Numpy is interactive and very easy to use.

• Mathematical Computation: It can simplify complex mathematical implementations significantly.

• Intuitive: It makes coding genuinely easy and helps grasp concepts easily.

• Open Source: It is widely used, thus has many open-source contributors.

4. Keras

1. What is Keras?

Keras is considered one of the coolest machine learning libraries in Python, providing a simpler mechanism for expressing neural networks. Keras also offers some best utilities for compiling models, handling datasets, and graphical visualization.

On the backend, Keras internally uses Theano or TensorFlow. It can also utilize some of the most popular neural networks, such as CNTK. When comparing Keras with other machine learning libraries, its speed is relatively slower because it creates computational graphs using backend infrastructure and then utilizes them to perform operations. All models in Keras are portable.

2. Features of Keras

• Supports CPU and GPU: It runs smoothly on both CPU and GPU.

• Comprehensive Models: Keras supports almost all models of neural networks—fully connected, convolutional, pooling, recurrent, embedding, etc. Moreover, these models can be combined to build more complex models.

• Modular: Keras is inherently modular, with incredible expressiveness, flexibility, and innovative research capabilities.

• Completely Python-Based: Keras is a fully Python-based framework, making it easy to debug and explore.

5. PyTorch

1. 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. Additionally, PyTorch provides a rich API to address application problems related to neural networks.

This machine learning library is based on Torch, an open-source machine library implemented in C and encapsulated in Lua.

This Python machine library was launched in 2017, and since its establishment, it has become increasingly popular and attracted more and more machine learning developers.

2. Features of PyTorch

• Hybrid Front-End: The new hybrid front-end provides usability and flexibility in Eager mode while seamlessly transitioning to graph mode for speed, optimization, and functionality in C++ runtime environments.

• Distributed Training: Optimizes performance in research and production by leveraging native support for asynchronous collective operations and point-to-point communications accessible from both Python and C++.

• Python First: It is built to be deeply integrated into Python, allowing it to be used with popular libraries and packages (such as Cython and Numba).

• A Wealth of 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 across fields from computer vision to reinforcement learning.

6. LightGBM

1. What is LightGBM?

Gradient Boosting is one of the best and most popular machine learning libraries, helping developers build new algorithms by using redefined base models (i.e., decision trees). Therefore, there are some special libraries available to implement this method quickly and efficiently.

These libraries include LightGBM, XGBoost, and CatBoost. All of these libraries help solve common problems and can be used in almost similar ways.

2. Features of LightGBM

• Fast: Extremely fast computations ensure high production efficiency.

• Intuitive: Intuitive, making it very user-friendly.

• Faster Training: It has faster training speeds than many other deep learning libraries.

• Tolerant: It does not produce errors when considering NaN values and other normative values.

7. Eli5

1. What is Eli5?

In most cases, the results predicted by machine learning models are not accurate, and the Eli5 machine learning library built with Python helps overcome this issue. It combines visualization and debugging of all machine learning models and tracks all working steps of the algorithms.

2. Features of Eli5

Eli5 also supports many libraries, such as XGBoost, lightning, scikit-learn, and sklearn-crfsuite.

8. SciPy

1. What is SciPy?

SciPy is a machine learning library aimed at application developers and engineers. The SciPy library contains modules for optimization, linear algebra, integration, and statistics.

2. Features of SciPy

The main feature of the SciPy library is that it is developed using NumPy, maximizing the use of NumPy’s arrays.

Additionally, SciPy provides all efficient numerical routines using its specific submodules, such as optimization, numerical integration, and many other programs.

All functions in all submodules of SciPy are well documented.

9. Theano

1. What is Theano?

Theano is a computational framework library for computing multidimensional arrays in Python. Theano works similarly to TensorFlow but is not as efficient, thus it is not suitable for production environments.

Moreover, Theano can also be used in distributed or parallel environments similar to TensorFlow.

2. Features of Theano

• Tightly Integrated with NumPy: It can use full NumPy arrays in functions compiled by Theano.

• Efficient GPU Usage: Performs data-intensive computations significantly faster than on CPU.

• Efficient Symbolic Differentiation: Theano can differentiate functions with one or more inputs.

• Speed and Stability Optimization: Even when x is very small, it can yield the correct answer for log(1+x). Of course, this is just one example demonstrating Theano’s stability.

• Dynamic C Code Generation: Evaluates expressions faster than ever, significantly improving efficiency.

• Extensive Unit Testing and Self-Validation: Detects and diagnoses various types of errors and ambiguities in models.

10. Pandas

1. What is Pandas?

Pandas is a machine learning library in Python that provides advanced data structures and various analysis tools. One of the library’s significant features is its ability to transform complex data operations with just one or two commands. Pandas has many built-in methods for grouping, aggregating data, and filtering, as well as time series capabilities.

2. Features of Pandas

Pandas makes the entire process of manipulating data easier, with support for operations such as re-indexing, iterating, sorting, aggregating, concatenating, and visualizing being one of the highlights of Pandas’ capabilities.