AI Education

Ray is an open-source framework designed to address the needs of scalable distributed computing, especially in the context of machine learning tasks. It was developed at the University of California, Berkeley’s RISELab to create a simple yet powerful infrastructure for parallel and distributed applications. The main goal of Ray is to enable developers to efficiently […]

Artificial Intelligence (AI) is transforming industries, with computer vision (CV) playing a pivotal role in sectors like healthcare, manufacturing, and autonomous driving. However, the effectiveness of CV models relies heavily on the quality of the data they are trained on. Poor data can lead to flawed CV models, resulting in inaccurate object detection, misclassification, and […]

Artificial intelligence and its subset, deep learning, have significantly changed how technology interacts with various industries. From healthcare to finance, deep learning to learn and create complex data sets has become invaluable. One of the key tools contributing to this revolution is Keras, a high-level API for building deep-learning training models. Keras has grown rapidly […]

Distributed deep learning has become a key solution for accelerating the training of large-scale models by leveraging the parallel processing capabilities of multiple GPUs. Two of the most popular deep learning frameworks, TensorFlow and PyTorch, have revolutionized the way models are developed and deployed. However, distributing the learning process across multiple GPUs and nodes presents […]

Chainer is an open-source deep learning platform built on Python, originally developed by the Japanese company Preferred Networks. It was first introduced to the public in June 2015 and has since gained recognition, particularly in the research community, for its innovative approach to building neural networks. Unlike some other frameworks that use static computational graphs […]

TensorFlow Extended (TFX) is a comprehensive end-to-end platform that streamlines the deployment and management of machine learning models in real-world production environments. Developed by Google, it leverages the reliability and scalability of TensorFlow, going beyond simple model training for the full lifecycle of a machine learning project. TFX was open-sourced so that developers and companies […]

Caffe’s genesis can be traced back to the bustling corridors of UC Berkeley, where it grew out of an academic need to accelerate deep learning research and application development. Developed by the Berkeley Vision and Learning Center (BVLC) and a thriving community of contributors, Caffe was designed with the vision of creating an infrastructure that […]

MXNet is a key development among deep learning frameworks. Built to provide unparalleled flexibility and scalability, it quickly became popular among developers, data scientists, and AI researchers. At its core, MXNet aims to facilitate the seamless creation, training, and deployment of deep learning models across a wide range of computing settings, from single CPUs to […]

The Microsoft Cognitive Toolkit, also known as CNTK, is free, open-source software that anyone can use to make computers smarter at tasks like viewing images, understanding language, or making decisions. It’s a tool created by Microsoft to help computers learn from data, much like a child learns from experience. With CNTK, programmers and developers can […]

Building custom models with PyTorch Lightning offers a simplified and efficient approach to deep learning development, allowing developers to focus more on the architecture and logic of their models, rather than the boilerplate code often associated with such tasks.    Getting started with PyTorch Lightning is the first step to making your deep learning projects […]