Introduction
Python is one of the most popular and widely used programming languages and has replaced many programming languages in the industry.
There are a lot of reasons why Python is popular among developers and one of them is that it has an amazingly large collection of libraries that users can work with. To learn more about Python, you can join our Python certification course today.
Here are a few important reasons why Python is popular:
- Python has a huge collection of libraries.
- Python is a beginner’s level programming language because of its simplicity and easiness.
- From developing to deploying and maintaining Python wants their developers to be more productive.
- Portability is another reason for the huge popularity of Python.
- Python programming syntax is simple to learn and is of high level when we compare it to C, Java, and C++.
Hence, only a few lines of code make new applications.
The simplicity of Python has attracted many developers to create new libraries for machine learning. Because of the huge collection of libraries Python is becoming hugely popular among machine learning experts.
TensorFlow
What Is TensorFlow?
If you are currently working on a machine learning project in Python, then you may have heard about this popular open-source library known as TensorFlow.
This library was developed by Google in collaboration with Brain Team. TensorFlow is a part of almost every Google application for machine learning.
TensorFlow works like a computational library for writing new algorithms that involve a large number of tensor operations, since neural networks can be easily expressed as computational graphs they can be implemented using TensorFlow as a series of operations on Tensors. Plus, tensors are N-dimensional matrices that represent your data.
Features of TensorFlow
TensorFlow is optimized for speed, it makes use of techniques like XLA for quick linear algebra operations.
1. Responsive Construct
With TensorFlow, we can easily visualize every part of the graph which is not an option while using Numpy or SciKit.
2. Flexible
One of the very important Tensorflow Features is that it is flexible in its operability, meaning it has modularity and the parts of it which you want to make standalone, offer that option.
3. Easily Trainable
It is easily trainable on CPU as well as GPU for distributed computing.
4. Parallel Neural Network Training
TensorFlow offers pipelining in the sense that you can train multiple neural networks and on multiple GPUs which makes the models very efficient on large-scale systems.
5. Large Community
Needless to say, if it has been developed by Google, there already is a large team of software engineers who work on stability improvements continuously.
6. Open Source
The best thing about this machine learning library is that it is open source so anyone can use it as long as they have internet connectivity.
Uses of TensorFlow?
You are using TensorFlow daily but indirectly with applications like Google Voice Search or Google Photos. These are the applications of TensorFlow.
All the libraries created in TensorFlow are written in C and C++. However, it has a complicated front end for Python. Your Python code will get compiled and then executed on TensorFlow distributed execution engine built using C and C++.
The number of applications of TensorFlow is unlimited and that is the beauty of TensorFlow.
So, next up on this ‘Top 10 Python Libraries’ blog we have Scikit-Learn!
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 working with complex data.
There are a lot of changes being made in this library. One modification is the cross-validation feature, providing the ability to use more than one metric. Lots of training methods like logistics regression and nearest neighbors have received some little improvements.
Features Of Scikit-Learn
1. Cross-validation: There are various methods to check the accuracy of supervised models on unseen data.
2. Unsupervised learning algorithms: Again there is a large spread of algorithms in the offering – starting from clustering, factor analysis, and principal component analysis to unsupervised neural networks.
3. Feature extraction: Useful for extracting features from images and text (e.g. Bag of words)
Where are we using Scikit-Learn?
It contains several algorithms for implementing standard machine learning and data mining tasks like reducing dimensionality, classification, regression, clustering, and model selection.
So, next up on this ‘Top 10 Python Libraries’ blog, we have Numpy!
Numpy
What Is Numpy?
Numpy is considered one of the most popular machine-learning libraries in Python.
TensorFlow and other libraries use Numpy internally for performing multiple operations on Tensors. The array interface is the best and the most important feature of Numpy.
Features Of Numpy
- Interactive: Numpy is very interactive and easy to use.
- Mathematics: Makes complex mathematical implementations very simple.
- Intuitive: Makes coding easy and grasping the concepts easily.
- Lot of Interaction: Widely used, hence a lot of open source contribution.
Uses of Numpy?
This interface can be utilized for expressing images, sound waves, and other binary raw streams as an array of real numbers in N-dimensional.
For implementing this library for machine learning knowing Numpy is important for full-stack developers.
So next up on this ‘Top 10 Python Libraries’ blog, we have Keras!
Keras
What Is Keras?
Keras is considered one of the coolest machine-learning libraries in Python. It provides an easier mechanism to express neural networks. Keras also provides some of the best utilities for compiling models, processing data sets, visualization of graphs, and much more.
In the backend, Keras uses either Theano or TensorFlow internally. Some of the most popular neural networks like CNTK can also be used. Keras is comparatively slow when we compare it with other machine-learning libraries. Because it creates a computational graph by using back-end infrastructure and then makes use of it to perform operations. All the models in Keras are portable.
Features Of Keras
- It runs smoothly on both CPU and GPU.
- Keras supports almost all the models of a neural network – fully connected, convolutional, pooling, recurrent, embedding, etc. Furthermore, these models can be combined to build more complex models.
- Keras, being modular, is incredibly expressive, flexible, and apt for innovative research.
- Keras is a completely Python-based framework, which makes it easy to debug and explore.
Where are we using Keras?
You are already constantly interacting with features built with Keras — it is in use at Netflix, Uber, Yelp, Instacart, Zocdoc, Square, and many others. It is especially popular among startups that place deep learning at the core of their products.
Keras contains numerous implementations of commonly used neural network building blocks such as layers, objectives, activation functions, optimizers, and a host of tools to make working with image and text data easier.
Plus, it provides many pre-processed data sets and pre-trained models like MNIST, VGG, Inception, SqueezeNet, ResNet, etc.
Keras is also a favorite among deep learning researchers, coming in at #2. Keras has also been adopted by researchers at large scientific organizations, in partic,ular CERN and NASA.
So, next up on this ‘Top 10 Python Libraries’ blog, we have PyTorch!
PyTorch
What Is PyTorch?
PyTorch is the largest machine learning library that allows developers to perform tensor computations wan with the acceleration of GPU, create dynamic computational graphs, and calculate gradients automatically. Other than this, PyTorch offers rich APIs for solving application issues related to neural networks.
This machine learning library is based on Torch, which is an open-source machine library implemented in C with a wrapper in Lua.
This machine library in Python was introduced in 2017, and since its inception, the library is gaining popularity and attracting an increasing number of machine learning developers.
Features Of PyTorch
Hybrid Front-End
A new hybrid front-end provides ease of use and flexibility in eager mode, while seamlessly transitioning to graph mode for speed, optimization, and functionality in C++ runtime environments.
Distributed Training
Optimize performance in both research and production by taking advantage of native support for asynchronous execution of collective operations and peer-to-peer communication that is accessible from Python and C++.
Python First
PyTorch is not a Python binding into a monolithic C++ framework. It’s built to be deeply integrated into Python so it can be used with popular libraries and packages such as Cython and Numba.
Libraries And Tools
An active community of researchers and developers has built a rich ecosystem of tools and libraries for extending PyTorch and supporting development in areas from computer vision to reinforcement learning.
Applications of PyTorch?
PyTorch is primarily used for applications such as natural language processing.
It is primarily developed by Facebook’s artificial intelligence research group and Uber’s “Pyro” software for probabilistic programming is built on it.
PyTorch is outperforming TensorFlow in multiple ways and it is gaining a lot of attention in recent days.
You can check out this PyTorch or TensorFlow blog to find out which is better for you.
So, next up on this ‘Top 10 Python Libraries’ blog, we have LightGBM!
LightGBM
What Is LightGBM?
Gradient Boosting is one of the best and most popular machine learning libraries, which helps developers in building new algorithms by using redefined elementary models and namely decision trees. Therefore, there are special libraries that are available for fast and efficient implementation of this method.
These libraries are LightGBM, XGBoost, and CatBoost. All these libraries are competitors that help in solving a common problem and can be utilized in almost a similar manner.
Features of LightGBM
Very fast computation ensures high production efficiency.
Intuitive, hence making it user-friendly.
Faster training than many other deep learning libraries.
Will not produce errors when you consider NaN values and other canonical values.
What are the applications of LightGBM?
This library provides provide highly scalable, optimized, and fast implementations of gradient boosting, which makes them popular among machine learning developers. Because most of the machine learning full stack developers won machine learning competitions by using these algorithms.
So, next up on this ‘Top 10 Python Libraries’ blog, we have Eli5!
Eli5
What Is Eli5?
Most often the results of machine learning model predictions are not accurate, and the Eli5 machine learning library built in Python helps in overcoming this challenge. It is a combination of visualization and debugging all the machine learning models and tracking all working steps of an algorithm.
Features of Eli5
Moreover, Eli5 supports other libraries XGBoost, lightning, sci-kit-learn, and sklearn-crfsuite libraries.
What are the applications of Eli5?
Mathematical applications require a lot of computation in a short time.
Eli5 plays a vital role where there are dependencies with other Python packages.
Legacy applications and implementing newer methodologies in various fields.
So, next up on this ‘Top 10 Python Libraries’ blog, we have SciPy!
SciPy
What Is SciPy?
SciPy is a machine learning library for application developers and engineers. However, you still need to know the difference between the SciPy library and the SciPy stack. SciPy library contains modules for optimization, linear algebra, integration, and statistics.
Features Of SciPy
The main feature of the SciPy library is that it is developed using NumPy, and its array makes the most use of NumPy.
In addition, SciPy provides all the efficient numerical routines like optimization, numerical integration, and many others using its specific submodules.
All the functions in all submodules of SciPy are well documented.
Applications of SciPy?
SciPy is a library that uses NumPy to solve mathematical functions. SciPy uses NumPy arrays as the basic data structure and comes with modules for various commonly used tasks in scientific programming.
Tasks including linear algebra, integration (calculus), ordinary differential equation solving and signal processing execute easily by SciPy.
So, next up on this ‘Top 10 Python Libraries’ blog, we have Theano!
Theano
What Is Theano?
Theano is a computational framework machine learning library in Python for computing multidimensional arrays. Theano works similarly to TensorFlow, but it is not as efficient as TensorFlow. Because of its inability to fit into production environments.
Moreover, Theano can also be used in distributed or parallel environments just similar to TensorFlow.
Features Of Theano
- Tight integration with NumPy – Ability to use completely NumPy arrays in Theano-compiled functions.
- Transparent use of a GPU – Perform data-intensive computations much faster than on a CPU.
- Efficient symbolic differentiation – Theano does your derivatives for functions with one or many inputs.
- Speed and stability optimizations – Get the right answer for
log(1+x)even whenxis very tiny. This is just one of the examples to show the stability of Theano. - Dynamic C code generation – Evaluate expressions faster than ever before, thereby, increasing efficiency by a lot.
- Extensive unit-testing and self-verification – Detect and diagnose multiple types of errors and ambiguities in the model.
Where are we using Theano?
The actual syntax of Theano expressions is symbolic, which can be off-putting to beginners used to normal software development. Specifically, expressions are defined in the abstract sense, compiled, and later actually used to make calculations.
It specifically handles the types of computation for large neural network algorithms in Deep Learning. It was one of the first libraries of its kind (development started in 2007) and is an industry-standard for Deep Learning research and development.
Theano is the strength of multiple neural network projects today and the popularity of Theano is only growing with time.
And, lastly, on this ‘Top 10 Python Libraries’ blog, we have Pandas!
Pandas
What Is Pandas?
Pandas is a machine learning library in Python that provides data structures of high-level and a wide variety of tools for analysis. One of the great features of this library is the ability to translate complex operations with data using one or two commands. Pandas have so many inbuilt methods for grouping, combining data, and filtering, as well as time-series functionality.
Features Of Pandas
Pandas make sure that the entire process of manipulating data will be easier. Support for operations such as Re-indexing, Iteration, Sorting, Aggregations, Concatenations, and Visualizations are among the feature highlights of Pandas.
Applications of Pandas?
Currently, there are fewer releases of the Panda's library which includes hundreds of new features, bug fixes, enhancements, and changes in API. The improvements in pandas regard its ability to group and sort data, select the best-suited output for the applied method, and provide support for performing custom types operations.
Data Analysis among everything else takes the highlight when it comes to the usage of Pandas. But, Pandas when used with other libraries and tools ensure high functionality and a good amount of flexibility.
Conclusion
I hope this Top 10 Python Libraries blog helped you to kick start your learning on the libraries available in Python. After knowing about the top 10 Python libraries, I am pretty sure you want to know more about Python. To know more about Python you can refer to the following blogs:
- Python Tutorial – Python Programming for Beginners
- Top 10 Reasons why you should learn Python
I think the following blogs on Python concepts will interest you as well. Check it out:
- Python Pandas Tutorial
- Numpy Tutorial
- Exceptions in Python Tutorial
- Python Matplotlib Tutorial
If you have any questions regarding this tutorial, please let me know in the comments.
Do develop something from the libraries and let me know in the comments section below, I’d love to be a part of that conversation!
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