A significant obstacle to developing sustainable, responsible, and reliable AI is the lack of explainability. Several libraries on GitHub are dedicated to explaining black-box models, auditing model data, and creating transparent models.

Furthermore, developers rely on open-source AI libraries to hasten application development and enable more effective and efficient practical usage. The top six machine learning libraries on GitHub are listed below.

Detectron2

The 2018 update to Facebook AI's object detection library, Detectron, is called Detectron2. Detectron, which uses Caffe, was challenging to set up and operate. In response to helpful feedback from the open-source community, Facebook thus released Detectron 2.

Modern object identification algorithms are used by Facebook AI Research's next-generation software system, Detectron2. It provides several ways to put complex algorithms for DensePose, panoptic feature pyramid networks, and different iterations of FAIR's groundbreaking Mask R-CNN model family into practice. Like Detectron, it supports object detection using boxes, instance segmentation masks, and human pose prediction. In addition, semantic segmentation and panoptic segmentation, which combines semantic and instance segmentation, are also supported by Detectron2.

Fastai

The well-known open-source AI project Fastai uses machine learning and deep learning methods. The library has APIs for collaborative filtering, text, tabular, and time-series analysis. When implementing deep learning frameworks, Fastai v2, released in August 2020, asserts to be noticeably faster and more flexible.

Fastai increases the general public's access to deep learning. It combines PyTorch's customizability with Keras' clarity and rapid development. Fastai is renowned for being easily accessible, quick to produce, incredibly flexible, and having a layered architecture. Furthermore, different levels of Fastai's API are available to meet other model building requirements. While the high-level API is at solution developers, the mid-level API offers the fundamental deep learning and data-processing techniques for each application. Finally, the mid-level APIs can be developed and customized thanks to the low-level APIs, which provide a library for optimized primitives and functional and object-oriented foundations.

Keras

Keras is a high-level neural network framework built on TensorFlow, CNTK, and Theano. For example, assume you need a deep learning framework that supports convolutional and recurrent networks and runs efficiently on CPUs and GPUs. In that case, this library is ideal for carrying out open-source AI projects.

Unlike other independent alternatives, this open-source AI project does not deal with simple low-level operations. Instead, it employs backend engines from related deep learning frameworks such as Tensorflow or Theano to perform all low-level computations such as tensor products, convolutions, and many other things. Furthermore, Keras also provides a High-Level API for creating models, specifying layers, and configuring various models. Moreover, this API aids in developing models with loss and optimizer functions and the training process with the fit function.

PyTorch

Researchers built this Python-based framework upon a C++ backend API in 2017. PyTorch is a Python-based replacement for the Lua Torch framework, focusing solely on research applications. Currently, a diverse community of academic and industrial researchers, application developers, and deep learning specialists have contributed projects, tools, models, and libraries to the PyTorch ecosystem.

PyTorch uses dynamic computing, unlike most well-known deep learning frameworks like TensorFlow, which gives it more flexibility when building complex networks. Furthermore, PyTorch is much simpler to understand because it utilizes fundamental and well-known Python and has a more readable syntax. Additionally, PyTorch enhances the optimization of AI models by using Python's inherent abilities for asynchronous execution. Finally, you can expand projects by running models across numerous computers using its Distributed Data Parallelism feature.

TensorFlow

The most popular open-source deep learning project is TensorFlow. Initially, Google Brain Team developed this, a division of Google's Machine Intelligence research group, for machine learning and deep neural network research. TensorFlow is one of the best tools for creating machine learning and deep learning applications. It is by professionals all over the world to create text, audio, and image recognition algorithms. Like any other platform, it has competed with alternatives like PyTorch and Keras. But it has continued to be well-liked and has become a pioneer in the AI sector.

In addition, to speed up the creation of deep learning models, We can also use TensorFlow with Google's exclusive TensorFlow Processing Unit (TPU) hardware. Furthermore, you can train and run deep neural networks using 

• handwritten digit classification, 
• visual recognition, 
• word embeddings, 
• recurrent neural networks, 
• sequence-to-sequence models for machine translation, 
• natural language processing, and 
• PDE-based simulations TensorFlow.

Theano

Theano is an open-source artificial intelligence (AI) project developed by the MILA group at the University of Montreal in Montreal, Quebec, Canada. It is a Python library that assists in performing mathematical operations on multidimensional arrays using NumPy or SciPy. Theano can use GPUs to accelerate processing and automatically generate symbolic graphs to compute gradients.

Theano implements cutting-edge deep learning algorithms and is now widely used in deep learning research and development. However, while its computational performance is impressive, customers have complained about an inaccessible user interface and unhelpful error messages. As a result, Theano is in tandem with more user-friendly wrappers such as Keras, Lasagne (which provides convenience classes for creating deep learning models), and Blocks — three high-level frameworks for rapid prototyping and model testing. Nevertheless, there are still several advantages to using Theano that many data scientists find compelling, such as its simplicity and maturity.