Lovers of vintage movies would have definitely heard of the Monty Python series. The programming language that it inspired continues to remain among the most popular languages. Guess why Python has consistently topped the charts of the most popular programming languages? Because of its rich environment of libraries and tools, its easy code readability and the fact that it is so easy to pick up. You name the domain, and you will get Python libraries available, to help you out in solving problems. Right from Artificial Intelligence, Data Science, Machine Learning, Image Processing, Speech Recognition, Computer Vision and more, Python has numerous uses. These libraries and frameworks are open source and can be easily integrated with the development environment that one has.
These software frameworks, the platforms which provides necessary libraries and code components, are backbones for devloping applications. Read on to see which are the top ML frameworks and libraries in Python.
As the name implies, this is the library which supports numerical calculations and tasks. It supports array operations and basic mathematical functions on the array and other data types of Python. The basic data type of this library is ndArray object.
Numpy has many advantages
- The base data structure is N –Dimensional array.
- Rich functions to handle the N-dimensional array effectively.
- Supports integration of C, C++ and other language code fragments.
- Supports many functions related to linear algebra, random numbers, transforms, statistics etc.
- No GPU and TPU support.
- Cannot automatically calculate the derivatives which is required in all ML algorithms.
- Numpy performance goes down when high complex calculations are required.
This is the most useful library for data preprocessing and preparing the data for the Machine Learning algorithms. The data from various files like CSV, Excel, Data etc. can be easily read using Pandas. The data is available in a spreadsheet like area, which makes processing easy. There are three basic data structures at the core of Pandas library:
- Series - One-dimensional array like object containing data and label (or index).
- Dataframe - Spreadsheet-like data structure containing an order collection of columns. It has both a row and column index.
- Panel – Collection of dataframes but rarely used data structure.
- Structured data can be read easily.
- Great tool for handling of data.
- Strong functions for manipulation and preprocessing of data.
- Data Exploration functions help in better understanding data.
- Data preprocessing capabilities help in making data ready for the application of ML algorithms.
- Basic Plotting functions are provided for visualization of data.
- Datasets can be easily joined or merged.
- The functions of Pandas are optimized for large datasets.
- Getting to know the Pandas functionalities is time consuming.
- The syntax is complex when multiple operations are required.
- Support for 3D metrics is poor.
- Proper documentation is not available for study.
Matplotlib is an important Python library which helps in data visualization. Understanding the data is very important for a data scientist before devising any machine learning based model. This library helps in understanding the data in a visual way. Data can be visualized using various graphical methods like line graph, bar graph, pie chart etc. This is a 2D visualization library with numerous ways of visualizing data.
- Simple and easy to learn for beginners.
- Integrated with Pandas for visualization of data in effective way.
- Various plots are provided for better understanding of data like Bar Chart, Stacked Bar chart, Pie chart, Scatter Plot etc.
- Forms a base for many advanced plotting libraries.
- Supports storing of the various graphs as images so that they can be integrated with other applications.
- Can plot timeseries data (with date) very easily.
- Complex Syntax for plotting simple graphs.
- The code becomes lengthy and complex for visualizations.
- Support for plotting of categorial data is not provided.
- It is a 2D visualization library.
- When multiple fields are required to be plotted and visualized effectively, matplotlib code can become lengthy.
- Managing multiple figures is difficult.
Visualizations are made simpler and more advanced with the help of Seaborn library. The base for Seaborn is Matplotlib. It is a boon for programmers as statistical visualizations are simplified.
- Best high-level interface for drawing statistical graphics.
- Provides support for plotting of categorial data effectively.
- The library provides default themes and many visualization patterns.
- Multiple figures are automatically created.
- The syntax is very simple and compact.
- There are many methods to integrate with Pandas dataframe, making this library most useful for visualization.
- Memory issues due to creation of multiple figures.
- Less customizable and flexible as compared to Matplotlib.
- Scalability issues.
Scipy is a Scientific Python library based on Numpy. It has functions which are best suitable for Mathematics, Science and Engineering. Many libraries are provided for Image and Signal Processing, Fourier Transform, Linear Algebra, Integration and Optimization. The functions are useful for ML algorithms and programs.
- The base library is Numpy.
- Many ML related functions are provided like Linear Algebra, Optimization, Compressed Sparce Data Structure etc.
- Useful Linear Algebra functions are available which are required for implementation of ML related algorithms.
- The functions can be applied with Pandas Dataframe directly.
- Complex functions are available and domain knowledge is needed to understand and implement these functions.
- There are performance issues when data size increases.
- Many other effective alternative libraries are available with the needed functionality.
Scikit-Learn is a useful open access library for use to Python developers. It is an extensive and popular library with many Machine Learning Supervised and Unsupervised algorithms implemented. These algorithms can be fine-tuned with the help of hyperparameters. This library contains many useful functions for preprocessing of data, useful metrics to measure performance of algorithms and optimization techniques.
- It is a general Machine Learning library built on top of Numpy, Pandas and Matplotlib.
- Simple to understand and use even for novice programmers.
- Useful Machine Learning Algorithms, both Supervised and Unsupervised, are implemented.
- Popular library for doing Machine Learning related tasks.
- Rich in Data Preprocessing and Data Sampling functions and techniques.
- Plethora of evaluation measures implemented to track the performance of algorithms.
- Very effective for quick coding and building Machine Learning Models.
- Scikit learn, as is based on Numpy, requires additional support to run on GTP and TPU
- Performance is an issue with size of data.
- Best suitable for basic Machine Learning applications.
- This library may be useful if one wants to write easy code, but it’s not the best choice for more detailed learning.
Natural Language processing is a great field of study for developers who like to research and challenge themselves. This library provides a base for Natural Language processing by providing simple functionalities to work with and understand languages.
- Very simple to use for processing natural language data.
- Many basic functionalities like tokenizing the words, removal of stop words, conversion to word vectors etc. are provided which forms the basis to start with natural language processing models.
- It is an amazing library to play with natural language using Python.
- It has more than 50 trained models and lexical resources like wordnet available for use.
- Rich discussion forums and many examples are available to discuss how to use this library effectively.
- It is based on string processing, which itself has many limitations.
- Slower as compared to other Natural Language processing libraries like Spacy.
Keras is a library written in Python for Neural Network programming. It offers a very simple interface to code the neural network and related algorithms. It is an incredibly popular library for Deep Learning algorithms, models and applications and can also be combined with various deep learning frameworks. It provides support for GPU and TPU computation of algorithms.
- The API provided is simple, same as Scikit-learn.
- Keras is totally based on Models and Graphs. A model has Input, output and intermediate layers to perform the various tasks as per requirement.
- Effective functionalities and models provided to code deep learning algorithms like Neural Network, Recurrent Neural Network, Long Short-Term memory, Autoencoders etc.
- Allows to create products easily supporting multiple backends
- Supports multi-platform use.
- Can be used with TensorFlow, can be used in browser using web based keras and provides native ML support for iPhone app development.
TensorFlow is the talk of the town because of its capabilities suitable for Machine Learning and Deep Learning models. It is one of the best, and most popular frameworks, adopted by companies around the world for Machine Learning and Deep Learning. Its support for Web as well as Mobile application coupled with Deep Learning models has made it popular among engineers and researchers. Many giants like IBM, Dropbox, Nvidia etc. use TensorFlow for creating and deploying Machine Learning Models.
This library has many applications like image recognition, video analysis, speech recognition, Natural Language Processing, Recommendation System etc. TensorFlow lite and TensorFlow JS has made it more popular for web applications and Mobile Applications.
- Developed by Google, it is one of the best deep learning frameworks.
- Simple Machine Learning tasks are also supported in TensorFlow.
- Supports many famous libraries like scikit learn, Keras etc. which are part of TensorFlow.
- The basic unit is Tensor which is an n-dimensional array.
- The basic derivatives are inherently computed which helps in developing many Machine learning Models easily.
- The models developed are supported on CPT, TPU and GPU.
- Tensorboard is the effective tool for data visualization.
- Many other supported tools are available to facilitate Web Development, App Development and IoT Applications using Machine Learning.
- Understanding Tensor and computational graphs is tedious.
- Computational graphs make the code complex and sometimes face performance problems.
A popular Python framework, Pytorch supports machine learning and deep learning algorithms and is a scientific computing framework. This is a framework which is widely used by Twitter, Google and Facebook. The library supports complex Tensor computations and is used to construct deep neural networks.
- The power of Pytorch lies in construction of Deep Neural Networks.
- Rich functions and utilities are provided to construct and use Neural Networks.
- Powerful when it comes to creation of production ready models.
- It supports GPU operations with rich math-based library functions.
- Unlike Numpy, it provides the functions which calculates gradient of the function, useful for the construction of the neural network.
- Provides support for Gradient based optimization which helps in scaling up the models easily to large data.
- It is a complex framework, so learning is difficult.
- Documentation support for learning is not readily available.
- Scalability may be an issue as compared to TensorFlow.
Theano is a library for evaluating and optimizing the mathematical computations. It is based on NumPy but provides support for both the GPU and CPU.
- It is a fast computation library in Python.
- Uses native libraries like BIAS to turn the code in faster computation.
- Best suited to handle computations in Deep Learning algorithms.
- Industry standard for Deep Learning research and development.
- It is not very popular among researchers as it is one of the older frameworks.
- It is not as easy to use as TensorFlow.
CNTK is Microsoft’s Cognitive Toolkit for the development of Deep Learning based models. It is a commercial distributed deep learning tool.
- It is a distributed open-source deep learning framework.
- Popular models like Deep Neural Network, Convolutional Neural Network models can be combined easily to form new models.
- Provides interface with C, C++ and Java to include Machine Learning models.
- Can be used to build reinforcement learning models as wide functions are available.
- Can be used to develop GAN (Generative Adversarial Networks).
- Provides various ways to measure the performance of the models built.
- High accuracy parallel computation on Multiple GPU is provided.
- Proper documentation is not available.
- There is inadequate community support.
Python, being one of the most popular languages for the development of Machine Learning models, has a plethora of tools and frameworks available for use. The choice of tool depends on the developer’s experience as well as the type of application to be developed. Every tool has some strong points and some weaknesses, so one has to carefully choose the tool or framework for the development of Machine Learning based applications. The documentation and support available are also important criteria to be kept in mind while choosing the most appropriate tool.