Data Science Tech Brief By HackerNoon

HackerNoon
Data Science Tech Brief By HackerNoon

Learn the latest data science updates in the tech world.

  1. How To Measure The Results Of In-App Events When Onelinks Don’t Work

    2024. 07. 30.

    How To Measure The Results Of In-App Events When Onelinks Don’t Work

    This story was originally published on HackerNoon at: https://hackernoon.com/how-to-measure-the-results-of-in-app-events-when-onelinks-dont-work. How To Measure The Results Of In-App Events When Onelinks Don’t Work Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #analytics, #onelink, #inapp-events, #marketing, #app-store, #mobile-apps, #digital-marketing, #good-company, and more. This story was written by: @socialdiscoverygroup. Learn more about this writer by checking @socialdiscoverygroup's about page, and for more stories, please visit hackernoon.com. Many app developers and marketing managers face the challenge of accurately measuring the impact of In-App Events (IAEs) on the App Store. While IAEs have proven effective for re-engaging users, attracting new downloads, and increasing revenue, traditional tracking methods like OneLink don’t actually include IAEs. Major mobile attribution platforms confirm that currently there is no way to track IAEs properly. At Social Discovery Group, our portfolio of 60+ dating and entertainment brands is supported by a team of over 100 marketers dedicated to app growth and development. We’re used to measuring all our marketing efforts in terms of financial value. Eventually, we’ve managed to develop our own composite way to evaluate IAEs, and are going to share it with you.

    6분
  2. Decoding Transformers' Superiority over RNNs in NLP Tasks

    2024. 07. 19.

    Decoding Transformers' Superiority over RNNs in NLP Tasks

    This story was originally published on HackerNoon at: https://hackernoon.com/decoding-transformers-superiority-over-rnns-in-nlp-tasks. Explore the intriguing journey from Recurrent Neural Networks (RNNs) to Transformers in the world of Natural Language Processing in our latest piece: 'The Trans Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #nlp, #transformers, #llms, #natural-language-processing, #large-language-models, #rnn, #machine-learning, #neural-networks, and more. This story was written by: @artemborin. Learn more about this writer by checking @artemborin's about page, and for more stories, please visit hackernoon.com. Despite Recurrent Neural Networks (RNNs) designed to mirror certain aspects of human cognition, they've been surpassed by Transformers in Natural Language Processing tasks. The primary reasons include RNNs' issues with the vanishing gradient problem, difficulty in capturing long-range dependencies, and training inefficiencies. The hypothesis that larger RNNs could mitigate these issues falls short in practice due to computational inefficiencies and memory constraints. On the other hand, Transformers leverage their parallel processing ability and self-attention mechanism to efficiently handle sequences and train larger models. Thus, the evolution of AI architectures is driven not only by biological plausibility but also by practical considerations such as computational efficiency and scalability.

    10분

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