PyTorch is an open-source machine learning library for Python, widely used for its ease of use and flexibility in building and training deep learning models. It provides a dynamic computational graph, which allows for faster prototyping and more intuitive model building compared to traditional static computation graphs.
PyTorch also integrates seamlessly with the most popular Python libraries, making it a popular choice among researchers and practitioners alike. Its popularity and active community make PyTorch a go-to choice for many deep learning projects, and it is used in a variety of industries, including computer vision, natural language processing, and reinforcement learning.
PyTorch is a relatively new deep learning framework based on Torch. Developed by Facebook’s AI research group and open-sourced on GitHub in 2017, it’s used for natural language processing applications. PyTorch has a reputation for simplicity, ease of use, flexibility, efficient memory usage, and dynamic computational graphs. It also feels native, making coding more manageable and increasing processing speed.

PyTorch was created by Facebook (Meta) and open-sourced in 2016.
This deep learning framework allows developers and data scientists to use Python as the main programming language for their AI models.
PyTorch is the suitable option for prototyping for research or smaller-scale projects.
PyTorch uses immediate execution (i.e., eager mode), it is easier to debug. With PyTorch, you can use Python debugging tools such as PDB, ipdb, and PyCharm debugger.
You can use PyTorch for mobile applications with the release of PyTorch Live.
PyTorch is used to train networks to complete tasks for their computer vision applications, including object detection and depth modeling.
For example the company Tesla uses PyTorch for Autopilot, their self-driving technology.
Disney engineers uses PyTorch.
PyTorch has a low-level API that requires you to write more code and handle more details than Keras.
PyTorch also has a weaker support for distributed and parallel computing, which can affect your scalability and efficiency.
The NVIDIA PyTorch Container is optimized for use with NVIDIA GPUs, and contains the following software for GPU acceleration: CUDA. cuBLAS. NVIDIA cuDNN.
Which big companies use PyTorch?
Some of the top users of PyTorch include large tech companies like Facebook, OpenAI, and NVIDIA, as well as startups in a variety of industries. These organizations are using PyTorch to develop innovative products and services, from AI-powered language translation systems to computer vision systems for agriculture.

What is TensorFlow?
TensorFlow + TFLite is one preferred methodology for Deep Learning models.
TensorFlow was an AI library developed by the Google Brain team for internal Google use in research and production. The initial version was released under the Apache License 2.0 in 2015.
TensorFlow was the choice for big projects with significant deployment requirements. TensorFlow is widely used by professionals in the industry, including data scientists, machine learning engineers, and researchers.
TensorFlow offers visualization tools, which allows developers to debug better and track the training process.
TensorFlow is used to deploy trained models to production, thanks to the TensorFlow Serving framework.
TensorFlow is an end-to-end open-source deep learning framework developed by Google and released in 2015. It is known for documentation and training support, scalable production and deployment options, multiple abstraction levels, and support for different platforms, such as Android.
TensorFlow is a symbolic math library used for neural networks and is best suited for dataflow programming across a range of tasks. It offers multiple abstraction levels for building and training models.
The difference is that TensorFlow creates optimized static computational graphs, while PyTorch makes use of dynamic computational graphs.
Who use TensorFlow?
For example TensorFlow is the preferred deep learning library used at Uber.
In the world of deep learning, TensorFlow offers a flexible, comprehensive ecosystem of community resources, libraries, and tools that facilitate building and deploying machine learning apps. Also, as mentioned before, TensorFlow has adopted Keras, which makes comparing the two seem problematic. Nevertheless, we will still compare the two frameworks for the sake of completeness, especially since Keras users don’t necessarily have to use TensorFlow.
PyTorch is replacing TensorFlow in many applications.
TensorFlow market share is decreasing only 4% in 2023.

JAX (Just After eXecution) is an AI framework replacing TensorFlow (for internal use at Google).

What is Keras?
Keras is an effective high-level neural network Application Programming Interface (API) written in Python. This open-source neural network library is designed to provide fast experimentation with deep neural networks, and it can run on top of CNTK, TensorFlow, and Theano.
Keras is usually used for small datasets as it is comparitively slower.
Keras focuses on being modular, user-friendly, and extensible. It doesn’t handle low-level computations; instead, it hands them off to another library called the Backend.
Keras was adopted and integrated into TensorFlow in mid-2017. Users can access it via the tf.keras module. However, the Keras library can still operate separately and independently.
PyTorch is better for scientists who need fast, scalable operations over large data sets. Keras is better for programmers who need easy, rapid operations over small data sets.

OpenCV is the best library for real-time computer vision.
OpenCV is not a framework for framework for machine learning like Pytorch or TensorFlow.
OpenCV is used for more traditional image processing tasks, while Keras/TensorFlow is used for machine learning tasks that involve training models on data
OpenCV is an open-source library for the computer vision, machine learning, and image processing and now it plays a major role in real-time operation which is very important in today's systems.
OpenCV is written in the programming language C++ and run much more faster than python.

Theano used to be one of the more popular deep learning libraries, an open-source project that lets programmers define, evaluate, and optimize mathematical expressions, including multi-dimensional arrays and matrix-valued expressions.
Theano was developed by the Universite de Montreal in 2007 and is a key foundational library used for deep learning in Python.
It’s considered the grandfather of deep learning frameworks and has fallen out of favor by most researchers outside academia.

PyTorch vs TensorFlow.
Understand the differences between Pytorch vs TensorFlow.
Here are some resources that help you expand your knowledge in this fascinating field: a deep learning tutorial, a spotlight on deep learning frameworks, and a discussion of deep learning algorithms.
Both TensorFlow and PyTorch offer useful abstractions that ease the development of models by reducing boilerplate code. They differ because PyTorch has a more "pythonic" approach and is object-oriented, while TensorFlow offers a variety of options.
PyTorch is used for many deep learning projects today, and its popularity is increasing among AI researchers, although of the three main frameworks, it is the least popular. Trends show that this may change soon.
When researchers want flexibility, debugging capabilities, and short training duration, they choose PyTorch. It runs on Linux, macOS, and Windows.
Thanks to its well-documented framework and abundance of trained models and tutorials, TensorFlow is the favorite tool of many industry professionals and researchers. TensorFlow offers better visualization, which allows developers to debug better and track the training process. PyTorch, however, provides only limited visualization.
TensorFlow also beats PyTorch in deploying trained models to production, thanks to the TensorFlow Serving framework. PyTorch offers no such framework, so developers need to use Django or Flask as a back-end server.
In the area of data parallelism, PyTorch gains optimal performance by relying on native support for asynchronous execution through Python. However, with TensorFlow, you must manually code and optimize every operation run on a specific device to allow distributed training. In summary, you can replicate everything from PyTorch in TensorFlow; you just need to work harder at it.
If you’re just starting to explore deep learning, you should learn PyTorch first due to its popularity in the research community. However, if you’re familiar with machine learning and deep learning and focused on getting a job in the industry as soon as possible, learn TensorFlow first.

PyTorch vs Keras choices and differences.
Both of these choices are good if you’re just starting to work with deep learning frameworks. Mathematicians and experienced researchers will find PyTorch more to their liking. Keras is better suited for developers who want a plug-and-play framework that lets them build, train, and evaluate their models quickly. Keras also offers more deployment options and easier model export.
However, remember that PyTorch is faster than Keras and has better debugging capabilities.
Both platforms enjoy sufficient levels of popularity that they offer plenty of learning resources. Keras has excellent access to reusable code and tutorials, while PyTorch has outstanding community support and active development.
Keras is the best when working with small datasets, rapid prototyping, and multiple back-end support. It’s the most popular framework thanks to its comparative simplicity. It runs on Linux, MacOS, and Windows.

Compare TensorFlow vs Keras.
TensorFlow is an open-sourced end-to-end platform, a library for multiple machine learning tasks, while Keras is a high-level neural network library that runs on top of TensorFlow. Both provide high-level APIs used for easily building and training models, but Keras is more user-friendly because it’s built-in Python.
Researchers turn to TensorFlow when working with large datasets and object detection and need excellent functionality and high performance. TensorFlow runs on Linux, MacOS, Windows, and Android. The framework was developed by Google Brain and currently used for Google’s research and production needs.
The reader should bear in mind that comparing TensorFlow and Keras isn’t the best way to approach the question since Keras functions as a wrapper to TensorFlow’s framework. Thus, you can define a model with Keras’ interface, which is easier to use, then drop down into TensorFlow when you need to use a feature that Keras doesn’t have, or you’re looking for specific TensorFlow functionality. Thus, you can place your TensorFlow code directly into the Keras training pipeline or model.
At the end of the day, use TensorFlow machine learning applications and Keras for deep neural networks.
Again, while the focus of this article is on Keras vs TensorFlow vs PyTorch, it makes sense to include Theano in the discussion. Theano brings fast computation to the table, and it specializes in training deep neural network algorithms. It’s cross-platform and can run on both Central Processing Units (CPU) and Graphics Processing Units (GPU).
TensorFlow also runs on CPU and GPU. It is based on graph computation, allowing the developer to visualize the neural network’s construction better using TensorBoard, making debugging easier.

Everyone’s situation and needs are different, so it boils down to which features matter the most for your AI project.
For easy reference, here’s a chart that breaks down the features of Keras vs PyTorch vs TensorFlow.

Feature	Keras	PyTorch	TensorFlow
API Level	High	Low	High and Low
Architecture	Simple, concise, readable	Complex, less readable	Not easy to use
Datasets	Smaller datasets	Large datasets, high performance Large datasets, high performance
Debugging	Simple network, so debugging is not often needed	Good debugging capabilities	Difficult to conduct debugging
Does It Have Trained Models?	Yes	Yes	Yes
Popularity	Most popular	Third most popular	Second most popular
Speed	Slow, low performance	Fast, high-performance	Fast, high-performance
Written In	Python	Lua	C++, CUDA, Python

Learn how to use the main frameworks : Pytorch, Tensorflow and Keras. Know how they overlap, and how they differ.
Is TensorFlow better than PyTorch? TensorFlow shines in deploying AI models for production, while PyTorch is the go-to for academic research purposes.
Is TensorFlow losing to PyTorch? The comparison between PyTorch and TensorFlow has typically been presented as TensorFlow excelling in production and PyTorch in research. Nevertheless, as of 2023, the situation is more nuanced, with both frameworks continually evolving.
Is PyTorch replacing TensorFlow? Although PyTorch now offers options for mobile applications through PyTorch Live, TensorFlow combined with TFLite remains the current favored approach. Choosing the best framework for learning Deep Learning depends on your background, especially if you're interested in studying Deep Learning itself.
Is TensorFlow faster than PyTorch? PyTorch enables faster prototyping, while TensorFlow might be more suitable when customized neural network features are required.
Should I learn PyTorch or TensorFlow? For extensive projects with significant deployment requirements, TensorFlow is the preferred choice. However, if you're focused on prototyping for research or smaller-scale endeavors, PyTorch is the suitable option.

You can create AI models and software with : TensorFlow, PyTorch, Keras, OpenCV for data science and machine learning.
Market share for : TensorFlow 37.31%, PyTorch 20.10%, OpenCV with 22.33%, Keras with 20.27%. (2022)