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How Deep Learning Got Deeper: The Breakthrough of Residual Networks

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Training deeper neural networks has always been a challenge—until now. In this episode, we dive into the groundbreaking innovation behind Residual Networks, or ResNets, which revolutionized AI models. Learn how this simple yet powerful idea made it possible to train networks 8x deeper than before, winning top honors in global AI competitions. From improving image recognition to dominating object detection, discover why ResNets are the foundation of today's cutting-edge AI. Hear it all on our podcast!

Link to research paper- https://arxiv.org/pdf/1512.03385

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In this episode, we dive into BERT, a breakthrough model that's reshaping how machines understand language. Short for Bidirectional Encoder Representations from Transformers, BERT uses a clever technique to learn from text in both directions simultaneously, enabling unmatched performance on tasks like answering questions and language inference. With state-of-the-art results on 11 benchmarks, BERT has set a new standard for natural language processing. Tune in to learn how this simple yet powerful model works and why it’s a game-changer in AI!

Link to research paper- https://drive.google.com/file/d/1EBTbfiIO0D8fnQsd4UIz2HN31K-6Qz-m/view

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What makes word vectors so powerful in capturing meaning and structure? In this episode, we uncover the mystery behind their surprising regularities and introduce a groundbreaking model that redefines how we learn word representations. By blending the strengths of global and local methods, this innovative approach creates word vectors with rich substructures, achieving impressive results on analogy and recognition tasks. We’ll break down the key ideas and explain why this model is a leap forward for natural language understanding. Perfect for curious minds—no coding knowledge required.

Link to research paper- https://www-nlp.stanford.edu/pubs/glove.pdf

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