Popular Python Libraries for Machine Learning and Deep Learning
From solving complex problems about data to creating advanced AI technologies, whatever you have in mind for your deep learning and machine learning needs there is plenty to learn when getting started.
There are a lot of artificial intelligence trends predicted for 2021 but there are also a number of ways deep learning and machine learning can add value to your company or business. For all the projects you may be interested in (or projects you need to create) it's important to know which Python libraries are the most popular to use for deep learning and machine learning.
What’s So Great About Python?
Python is a robust programming language that can be simple to learn and use while also having tons of rich features for power-users. It has become the premier programming language to use for deep learning and machine learning because of its accessibility and flexibility.
Deep learning and machine learning projects differ from traditional projects. As the name implies, some of the major differences are centered around the need for deep research, and the raw technology needed for deep learning and machine learning.
These all require a programming language that is stable and flexible, providing simplicity and consistency for computing huge datasets and formats. Having tools and frameworks available is a necessity, too. Python offers everything you need, making it the go-to language for all deep learning and machine learning.
Python Libraries and Frameworks Get You Started Faster
Another reason Python is so popular for deep learning and machine learning is because of the ability to use Python libraries.
These are essentially Python programs already created with essential tools for different types of tasks that can be manipulated to suit your needs.
This saves you time and energy writing your own code and tools from scratch, allowing you to dig into your projects that much quicker.
When it comes to deep learning and machine learning you may need to be running several algorithms at once. You may even need to be running several Python libraries at once to perform your specific tasks.
This is when Python frameworks come into play.
A Python framework incorporates multiple Python libraries into one cohesive unit. This allows you to perform incredibly complex algorithms and computations without the need to manipulate massive amounts of data on your own.
Used effectively, these Python libraries can be used in conjunction with others to create a framework that allows you to input key data points individually in order to create the result or outcome you desire.
Utilizing Python libraries and frameworks is how many neural networks are created. These allow for a neural network to scan and "read" an entire series of novels and then produce one of its own. It also creates the possibility for a neural network to learn chess in four hours and beat the up-to-then-unbeatable computer program designed to always win at chess (read more here).
When first starting work on a deep learning or machine learning project, it's best to start with specific libraries and build out your datasets to ensure you have a full understanding of the processes each library uses and works through. Once you have mastered a couple of different libraries, then you can utilize frameworks to their maximum potential.
For deep learning and machine learning, there are many good Python libraries to choose from. Based on our research of Python libraries for deep learning and machine learning we highly recommend taking advantage of the choices below. Each one has its advantages and disadvantages, but they all offer something special for the tasks you want to perform.
The 4 Most Popular Python Libraries
1. NumPy
NumPy is first on our list because it is a well-known package for general-purpose array-processing. For starters, this provides a wealth of community knowledge to pull from when you use NumPy in conjunction with your deep learning or machine learning projects.
NumPy uses a collection of extensive, high-complexity mathematical functions for multi-dimensional array and matrix processing. This makes it perfect for scientific computing. It is also one of the most used Python libraries for AI research.
Using NumPy allows you to define data types and integrate this data with a large number of other databases. In fact, NumPy is used in many other libraries and frameworks for filtering large datasets and converting them into useful data points for other computing aspects.
Advantages:
- Easy to use and interactive
- Integrates well with other libraries, frameworks, and even other programming languages
- Versatile because it is used in so many other libraries and frameworks
- Great community of users to pull ideas and experience from
- Breaks down complex datasets into usable, manageable data
Disadvantages:
- Can be overkill if you have simple datasets that could be plugged directly into other libraries and frameworks
2. SciPy
SciPy was created alongside the growth of the deep learning and machine learning project base. So, it is heavily utilized in the data science field and comes with plenty of tools, community learning, and standardized practices.
The SciPy library uses NumPy for its underlying data structure to provide multi-dimensional array and matrix processing. SciPy also offers tools for linear algebra, image optimization, special functions, signal and image processing, Ordinary Differential Equation (ODE) solving, and many other computational tasks for deep learning and machine learning.
Advantages:
- Can take raw datasets and convert them into image visualizations
- Takes care of most of your mathematical computations
- Efficient numerical routines for integration and optimization
Disadvantages:
- Can be confusing for beginners because there is both a framework and a library named SciPy. Be careful and pay attention as you start working with SciPy.
3. Scikit-learn
Scikit-learn was built using two other Python libraries we have already discussed, NumPy and SciPy. By building on these two existing libraries, Scikit-learn has become the most popular Python library for deep learning and machine learning algorithms.
Scikit-learn comes packed with all the features of NumPy and SciPy while also adding tools and features for data analysis and data mining. Scikit-learn specializes in deep learning and machine learning using a range of supervised as well as unsupervised algorithms that work on a consistent interface within Python.
Scikit-learn can be further used for project tasks such as classification, dimensionality reduction, regression, model selection, preprocessing, and clustering. By using and adding to the benefits of NumPy and SciPy, Scikit-learn is a powerhouse for a wide range of AI projects.
Advantages:
- Simplicity and efficiency for all things deep learning and machine learning
- Constantly being improved on by an active community of users
- Wide variety of function and algorithms for tasks
- Has the ability to extract data from images
Disadvantages:
- While it has the ability to perform unsupervised algorithms, it functions best for deep learning while supervised.
4. Keras
Keras holds a special place in this list in that it can run independently of many other libraries and frameworks. While it does work well in conjunction with the other libraries on our list, it operates well on its own and for its own purposes to support deep learning and machine learning.
Keras is an open-source library used mainly in neural networks, but functions incredibly well in machine learning, too. It also takes into account something we haven't touched on in this list: Keras can run efficiently on CPU and GPU so it won't hog up too much of your processing power.
Rather than working with mathematical algorithms, Keras works using neural-network building blocks such as optimizers, layers, activation functions, and objectives. This gives Keras a bunch of features to work on images and text images, which is especially useful in deep learning and neural network coding.
All these features make Keras a popular Python library to run alongside some of the others on our list as well as functioning well with the use of certain frameworks too, like TensorFlow.
Advantages:
- Useful for experimentation
- Integrates well with other libraries and frameworks
- Comprehensive and simple expression of neural networks other libraries don't have
- Best for visualizing data
Disadvantages:
- While it doesn't take up too much of your processing power, Keras can be slow as it requires the creation of a computational graph before performing operations.
How These Libraries Can Boost Your Deep Learning and Machine Learning
These Python libraries are essential for getting started in deep learning and machine learning because they form the backbone of many popular projects today.Whether you are diving into AI research to create the next great photo app for your business or you are trying to create a better method of forecasting long-term stock market changes, these are all essential Python libraries for any project you may be starting.
To view additional articles related to deep learning, machine learning, or a variety of other topics, head over to our blog or contact us for any questions.
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