9 Best Python Automatic Differentiation Libraries in 2023

Python Automatic Differentiation libraries will provide features for use with different applications. It offers features for applications like scientific computing and machine learning applications. It will allow users to compute mathematical function derivatives without computing the gradients. 

Many of these libraries use optimized algorithms for computing gradients efficiently. It will make them suitable for large-scale machine-learning applications. These support various differentiation modes and levels of control over the computation graph. It supports modes like reverse mode and forward mode. It will provide users with flexibility in how they use the libraries. Many libraries are designed to work seamlessly with other popular scientific libraries in Python. It can handle complex functions and make them useful for tasks like optimization.  

Here are the 9 best Python Automatic Differentiation Libraries that are handpicked for developers: 

tangent: 

• Is a Python library for automatic differentiation. 
• Is specifically designed for deep learning applications. 
• Offers a higher-level, user-friendly API for training and defining deep neural networks. 
• Offers support for dynamic computation graphs. 
• Allows the graph to be constructed dynamically as the program runs. 

pennylane: 

• Is a Python library for differentiable programming designed for quantum computing applications. 
• Offers a user-friendly API to define and optimize quantum circuits. 
• Supports various simulators and quantum hardware. 
• Allows users to compute gradients of quantum circuits with different parameters. 
• These parameters are essential for optimizing these circuits for specific tasks. 

aesara: 

• Is a Python library for symbolic mathematical computation and optimization. 
• Allows users to define mathematical expressions and optimize those expressions for specific tasks. 
• Allows users to compute gradients of mathematical expressions easily. 
• Is essential for optimizing complex models used in scientific computing and deep learning. 

uncertainties: 

• Is a Python library for performing calculations with error propagation. 
• Allows users to propagate errors through mathematical expressions easily.  
• Offers various tools for working with uncertain numbers.  
• Offers trigonometric, logarithmic functions, and arithmetic operations.  
• Includes tools for fitting models to uncertain data and generating random numbers. 

deep-learning-from-scratch-3: 

• Is a Python library for implementing deep learning models without relying on pre-built libraries. 
• Is designed to offer a comprehensive introduction to deep learning methods and concepts. 
• Offers various features which will make it well-suited for learning about deep learning. 
• Is defined to be modular with separate modules for various types of layers. 
• Includes loss functions, optimization algorithms, and activation functions. 

assignment1: 

• Is an educational library designed to help students learn about deep learning and distributed systems. 
• Offers various utilities and tools for implementing and training deep learning models. 
• Includes implementations of various optimization algorithms by SGD and momentum and tools. 
• Offers students hands-on learning experience in building and training distributed deep learning systems. 

ott: 

• Is a Python package for performing optimal transport computations using the JAX library. 
• Is high-performance numerical computing focusing on scientific computing and machine learning.  
• Offers utilities and tools for computing optimal transport maps between probability measures. 
• Is a valuable tool for anyone working with optimal transport problems.  

torchopt: 

• Is a Python package for optimization in PyTorch, a popular deep-learning framework.  
• Offers various optimization algorithms for training neural networks.  
• Provides first optimization methods like SGD, Adam, momentum, and L-BFGS. 
• Integrates seamlessly with PyTorch making it easy to use with PyTorch code and models.  

betty: 

• Offers various NLP utilities and tools for processing text data. 
• Includes text classification, summarization, sentiment analysis, and named entity recognition. 
• Includes various post-processing and pre-processing tools for working with text data. 
• Offers tools for data analysis and visualization. 
• By making exploring and understanding text data easier.