10 Best Python Libraries for Deep Learning (2022)

Python has been steadily rising to become a top programming language. There are many reasons for this, including its high efficiency when compared to other mainstream languages. It also features English-like commands and syntax, which make it a top choice for beginner coders.

Perhaps the biggest selling point of Python is its vast amount of open-source libraries that enable the use of everything from language to data science to data manipulation.

Deep learning is a subfield of machine learning artificial neural networks, which are algorithms inspired by the structure of the human brain. Deep learning has many applications and is used in many today’s AI technologies, such as self-driving cars, news aggregation tools, natural language processing (NLP), virtual assistants, visual recognition, and much more.

In recent years, Python has proven to be an incredible tool for deep learning. Because the code is concise and readable, it makes deep learning applications for a perfect match. Its simple syntax also enables applications to be developed faster than other programming languages. Another major reason for deep learning for Python is that the language can be integrated with other systems in different programming languages. This makes it easier to blend it with AI projects in other languages.

Let’s take a look at the 10 best Python libraries for deep learning:

1. TensorFlow

TensorFlow is a widely considered one of the best Python libraries for deep learning applications. Developed by the Google Brain Team, it provides a wide range of flexible tools, libraries, and community resources. Beginners and professionals alike can use TensorFlow to construct deep learning models, as well as neural networks.

TensorFlow has an architecture and framework that is flexible, enabling it to run on various computational platforms like CPU and GPU. With that said, it performs best when operating on a tensor processing unit (TPU). The Python library is often used to implement reinforcement learning in deep learning models, and you can directly visualize the machine learning models.

Here are some of the main features of TensorFlow:

• Flexible architecture and framework.
• Computational platforms of a variety on Runs.
• Abstraction capabilities
• Manages deep neural networks.

2. Pytorch

Another one of the most popular Python libraries for deep learning is Pytorch, which is created by an open-source library on Facebook’s AI research team in 2016. The name of the library is derived from Torch, which is a deep learning framework written by Lua programming language.

PyTorch enables you to carry out many tasks, and it is particularly useful for deep learning applications such as NLP and computer vision.

Some of the best aspects of PyTorch include its high speed of execution, which can be achieved even when handling heavy graphs. It is also a flexible library, capable of operating on simplified processors or CPUs and GPUs. PyTorch has powerful APIs that enable you to expand the library, as well as a natural language toolkit.

Here are some of the main features of PyTorch:

• Statistical distribution and operations
• Datasets over Control
• Deep learning models of development
• Highly flexible

3. NumPy

One of the other well-known Python libraries, NumPy can be seamlessly utilized for large multi-dimensional array and matrix processing. It relies on a large set of high-level mathematical functions, which make it particularly useful for efficient fundamental scientific computations.

NumPy arrays require a lot less storage area than other Python lists, and they are faster and more convenient to use. The data can be manipulated in the library with matrices, transposed, and reshaped. NumPy is a great option and requires a lot of complex work without deep learning models.

Here are some of the main features of NumPy:

• Shape manipulation
• High-performance N-dimensional array object
• Data cleaning / manipulation
• Statistical operations and linear algebra

4. Scikit-Learn

Scikit-Learn was originally a third-party extension to the SciPy library, but it’s now a standalone Python library on Github. Scikit-Learn includes DBSCAN, gradient boosting, support vector machines, and classification, regression, and clustering methods within random forests.

One of the greatest aspects of Scikit-Learn is that it’s easily interoperable with other SciPy stacks. It is also user-friendly and consistent, making it easier to share and use data.

Here are some of the main features of Scikit-learn:

• Data classification and modeling
• End-to-end machine learning algorithms
• Pre-processing of data
• Model selection

5. SciPy

That brings us to Scipy, which is a free and open-source library based on Numpy. SciPy is one of the best Python libraries out there thanks to its ability to perform large datasets on scientific and technical computing. It is accompanied by array optimization and linear algebra for embedded modules.

The programming language includes all of NumPy’s functions, but it turns them into user-friendly, scientific tools. It is often used for high-level, non-scientific mathematical functions for image manipulation and basic processing features.

Here are some of the main features of SciPy:

• User-friendly
• Data visualization and manipulation
• Scientific and technical analysis
• Computes large data sets

6. Pandas

One of the open-source Python libraries mainly used in data science and deep learning is Pandas. The library provides data manipulation and analysis tools, which are used for analyzing data. The library relies on manipulating numerical tables and time series analysis for its powerful data structures.

The Pandas Library offers a fast and efficient way to manage and explore data provided by Series and DataFrames, which represent data in a variety of ways.

Here are some of the main features of Pandas:

• Indexing of data
• Data alignment
• Datasets of merging / joining
• Data manipulation and analysis

7. Microsoft CNTK

Another Python library for deep learning applications is the Microsoft CNTK (Cognitive Toolkit), formerly known as the Computational Network ToolKit. The open-source deep-learning library is used for distributed deep learning and machine learning tasks.

CNTK enables you to combine predictive models like convolutional neural networks (CNNs), feed-forward deep neural networks (DNNs), and recurrent neural networks (RNNs), with the CNTK framework. These enables the effective implementation of end-to-end deep learning tasks.

Here are some of the main features of CNTK:

• Open-source
• Implement distributed deep learning tasks
• CNTK framework with combine predictive models
• End-to-end deep learning tasks

8. Keras

Kears is yet another notable open-source Python library used for deep learning tasks, testing deep neural networks. Keras provides you with the tools you need to construct models, visualize graphs, and analyze datasets. On top of that, it also includes prelabeled datasets that can be directly imported and loaded.

The Keras Library is often preferable to being modular, extensible, and flexible. This makes it a very user-friendly option for beginners. It can also integrate objectives, layers, optimizers, and activation functions. Keras operates in various environments and can run on CPUs and GPUs. It also offers one of the widest ranges of data types.

Here are some of the main features of Keras:

• Developing neural layers
• Data pooling
• Builds deep learning and machine learning models
• Activation and cost functions

9. Theano

Nearing the end of our list is Theano, a numerical computation Python library developed for machine learning and deep libraries. With this tool, you will achieve efficient definition, optimization, and evaluation of mathematical expressions and matrix calculations. All of these enables construct deep learning models to employ the dimensional arrays of the Theano.

Theano is a highly specialized library thanks to a lot of deep learning developers and programmers. It can be used with a graphics processing unit (GPU) instead of a central processing unit (CPU).

Here are some of the main features of Theano:

• Built-in validation and unit testing tools
• High-performing mathematical computations
• Fast and stable evaluations
• Data-intensive calculations

10. MXNet

Closing out our list of the 10 best Python libraries for deep learning is MXNet, which is a highly scalable open-source deep learning framework. MXNet was designed to train and deploy deep neural networks, and it can train train models extremely fast.

MXNet supports many programming languages, such as Python, Julia, C, C ++, and more. One of the best aspects of MXNet is that it offers incredibly fast calculation speeds and resource utilization on the GPU.

Here are some of the main features of MXNet:

• Highly-scalable
• Open-source
• Train and deploy deep learning neural networks
• Trains models fast
• Fast calculation speeds