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Last updated: 24th August, 2024 Model parallelism and data parallelism are two strategies used to distribute the training of large…
Last updated: 20th August, 2024 Self-supervised learning is an approach to training machine learning models primarily for large corpus of…
Last updated: 12th May, 2024 In the world of generative AI models, autoencoders (AE) and variational autoencoders (VAEs) have emerged…
Last updated: 23rd Jan, 2024 Two NLP concepts that are fundamental to large language models (LLMs) are transfer learning and…
The Transformer model architecture, introduced by Vaswani et al. in 2017, is a deep learning model that has revolutionized the…
Are you fascinated by the power of deep learning large language models that can generate creative writing, answer complex questions,…
NLP has been around for decades, but it has recently seen an explosion in popularity due to pre-trained models (PTMs),…
Have you been wondering what sets apart two of the most prominent transformer-based machine learning models in the field of…
In the field of AI / machine learning, the encoder-decoder architecture is a widely-used framework for developing neural networks that…
Have you ever marveled at how typing a few words into a search engine yields exactly the information you're looking…
Last updated: 4th Dec, 2023. In the fast-paced world of computer vision and image processing, the problem of image classification…
Last updated: 24th Nov, 2023 The activation functions are critical to understanding neural networks. There are many activation functions available…
Are you intrigued by the revolutionary world of transformer architectures? Have you ever wondered how encoder-only transformer models like BERT,…
How can machines accurately classify text into categories? What enables them to recognize specific entities like names, locations, or dates…