As a library designed for production research, PyTorch Lightning streamlines hardware support and distributed training as well, and we’ll show how easy it is to move training to a GPU toward the end.
AutoML is a broad category of techniques and tools for applying automated search to your automated search and learning to your learning. In addition to Auto-Sklearn, the Freiburg-Hannover AutoML group has also developed an Auto-PyTorch library. We’ll use both of these as our entry point into AutoML in the following simple tutorial.
Ever larger models churning on increasingly faster machines suggest a potential path toward smarter AI, such as with the massive GPT-3 language model. However, new, more lean, approaches are being conceived and explored that may rival these super-models, which could lead to a future with more efficient implementations of advanced AI-driven systems.
Deep learning isn’t just for placing ads or identifying cats anymore. Instead, a slew of young startups have started to incorporate the advances in computer vision made possible through larger and larger neural networks to real working robots in the fields.
Deep Learning continues to evolve as one of the most powerful techniques in the AI toolbox. Many software packages exist today to support the development of models, and we highlight important options available with key qualities and differentiators to help you select the most appropriate for your needs.
Read on to learn more about what makes Copilot different from previous autocomplete tools (including TabNine), and why this particular tool has been generating so much controversy.
AutoML enables developers with limited ML expertise (and coding experience) to train high-quality models specific to their business needs. For this article, we will focus on AutoML systems which cater to everyday business and technology applications.
While the preprocessing pipeline we are focusing on in this post is mainly centered around Deep Learning, most of it will also be applicable to conventional machine learning models too.
Access to high-quality, noise-free, large-scale datasets is crucial for training complex deep neural network models for computer vision applications. Many open-source datasets are developed for use in image classification, pose estimation, image captioning, autonomous driving, and object segmentation. These datasets must be paired with the appropriate hardware and benchmarking strategies to optimize performance.
Kevin Vu manages Exxact Corp blog and works with many of its talented authors who write about different aspects of Deep Learning.
Introduction to PyTorch Lightning
Open Source Datasets for Computer Vision
