AutoML: An Introduction Using Auto-Sklearn and Auto-PyTorch - 11 Oct 2021
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.
Introduction to PyTorch Lightning - 04 Oct 2021
PyTorch Lightning is a high-level programming layer built on top of PyTorch. It makes building and training models faster, easier, and more reliable.
Surpassing Trillion Parameters and GPT-3 with Switch Transformers – a path to AGI? - 01 Oct 2021
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.
Computer Vision in Agriculture - 27 Sep 2021
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.
A Breakdown of Deep Learning Frameworks - 23 Sep 2021
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.
GitHub Copilot and the Rise of AI Language Models in Programming Automation - 22 Sep 2021
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.
Introduction to Automated Machine Learning - 15 Sep 2021
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.
Text Preprocessing Methods for Deep Learning - 10 Sep 2021
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.
Open Source Datasets for Computer Vision - 18 Aug 2021
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.
Not Only for Deep Learning: How GPUs Accelerate Data Science & Data Analytics - 26 Jul 2021
Modern AI/ML systems’ success has been critically dependent on their ability to process massive amounts of raw data in a parallel fashion using task-optimized hardware. Can we leverage the power of GPU and distributed computing for regular data processing jobs too?
Kevin Vu manages Exxact Corp blog and works with many of its talented authors who write about different aspects of Deep Learning. 