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dagshub.com | ||
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neptune.ai
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| | | | | Industry expert shares strategies for improving the performance of ML/DL systems, covering optimization, model selection, and data handling. | |
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scorpil.com
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| | | | | In Part One of the "Understanding Generative AI" series, we delved into Tokenization - the process of dividing text into tokens, which serve as the fundamental units of information for neural networks. These tokens are crucial in shaping how AI interprets and processes language. Building upon this foundational knowledge, we are now ready to explore Neural Networks - the cornerstone technology underpinning all Artificial Intelligence research. A Short Look into the History Neural Networks, as a technology, have their roots in the 1940s and 1950s. | |
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blog.fastforwardlabs.com
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| | | | | We discussed this research as part of our virtual event on Wednesday, July 24th; you can watch the replay here! Convolutional Neural Networks (CNNs or ConvNets) excel at learning meaningful representations of features and concepts within images. These capabilities make CNNs extremely valuable for solving problems in the image analysis domain. We can automatically identify defects in manufactured items, reducing costs associated with quality assurance processes; we can now infer depth information and reconstruct 3D maps from 2D images without additional metadata, giving new potential to urban planning as well as entertainment experiences; we can perform pixel level separation of objects in images and video with applications ranging from public safety to medical robotics; and we can perform "super resolution" on photo images (upscale an image up to 10x) with a trained deep-learning system reconstructing, filling in, and thus sharpening images with information that would be omitted and lost using a standard digital zoom. | |
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www.superannotate.com
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| | | Explore RLHF's transformative role in making LLMs more attuned to human preferences, enhancing AI interactions for a more intuitive future. | ||
