Machine Learning Tech Brief By HackerNoon

Machine Learning Tech Brief By HackerNoon

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Machine Learning Tech Brief By HackerNoon episodes

  • Effective Anomaly Detection Pipeline for Amazon Reviews: References & Appendix

    This story was originally published on HackerNoon at: https://hackernoon.com/effective-anomaly-detection-pipeline-for-amazon-reviews-references-and-appendix.


    Explore findings from a study on an anomaly detection pipeline for Amazon reviews using MPNet embeddings.
    Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.
    You can also check exclusive content about #transformers, #anomaly-detection, #nlp-for-anomaly-detection, #explainability-in-ml, #machine-learning-classifiers, #text-specific-ad-models, #text-encoding-techniques, #explainable-ai, and more.


    This story was written by: @textmodels. Learn more about this writer by checking @textmodels's about page,
    and for more stories, please visit hackernoon.com.


    This study introduces an effective pipeline for detecting anomalous Amazon reviews using MPNet embeddings. It evaluates SHAP, term frequency, and GPT-3 for explainability, revealing user preferences and computational challenges. Future research may explore broader surveys and integrating GPT-3 throughout the pipeline for enhanced performance.

    19 min
  • Breaking down GPU VRAM consumption

    This story was originally published on HackerNoon at: https://hackernoon.com/breaking-down-gpu-vram-consumption.


    What factors influence VRAM consumption? How does it vary with different model settings? I dug into the topic and conducted my measurements.
    Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.
    You can also check exclusive content about #llms, #vram, #machine-learning, #deep-learning, #gpus, #gpu-vram, #gpus-for-machine-learning, #gpu-optimization, and more.


    This story was written by: @furiousteabag. Learn more about this writer by checking @furiousteabag's about page,
    and for more stories, please visit hackernoon.com.


    I’ve always been curious about the GPU VRAM required for training and fine-tuning transformer-based language models. What factors influence VRAM consumption? How does it vary with different model settings? I dug into the topic and conducted my measurements.

    9 min
  • Effective Anomaly Detection Pipeline for Amazon Reviews: References & Appendix

    This story was originally published on HackerNoon at: https://hackernoon.com/effective-anomaly-detection-pipeline-for-amazon-reviews-references-and-appendix.


    Explore findings from a study on an anomaly detection pipeline for Amazon reviews using MPNet embeddings.
    Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.
    You can also check exclusive content about #transformers, #anomaly-detection, #nlp-for-anomaly-detection, #explainability-in-ml, #machine-learning-classifiers, #text-specific-ad-models, #text-encoding-techniques, #explainable-ai, and more.


    This story was written by: @textmodels. Learn more about this writer by checking @textmodels's about page,
    and for more stories, please visit hackernoon.com.


    This study introduces an effective pipeline for detecting anomalous Amazon reviews using MPNet embeddings. It evaluates SHAP, term frequency, and GPT-3 for explainability, revealing user preferences and computational challenges. Future research may explore broader surveys and integrating GPT-3 throughout the pipeline for enhanced performance.

    19 min
  • Breaking down GPU VRAM consumption

    This story was originally published on HackerNoon at: https://hackernoon.com/breaking-down-gpu-vram-consumption.


    What factors influence VRAM consumption? How does it vary with different model settings? I dug into the topic and conducted my measurements.
    Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.
    You can also check exclusive content about #llms, #vram, #machine-learning, #deep-learning, #gpus, #gpu-vram, #gpus-for-machine-learning, #gpu-optimization, and more.


    This story was written by: @furiousteabag. Learn more about this writer by checking @furiousteabag's about page,
    and for more stories, please visit hackernoon.com.


    I’ve always been curious about the GPU VRAM required for training and fine-tuning transformer-based language models. What factors influence VRAM consumption? How does it vary with different model settings? I dug into the topic and conducted my measurements.

    9 min
  • Building Chatbots from Scratch: Understanding and Harnessing Large Language Models (LLMs)

    This story was originally published on HackerNoon at: https://hackernoon.com/building-chatbots-from-scratch-understanding-and-harnessing-large-language-models-llms.


    Imagine having a super smart friend who has read every book, article, and blog post on the internet.
    Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.
    You can also check exclusive content about #large-language-models, #prompt-engineering, #langchain, #node.js, #ai-technologies, #chatbot-development, #openai-api-integration, #natural-language-processing, and more.


    This story was written by: @nassermaronie. Learn more about this writer by checking @nassermaronie's about page,
    and for more stories, please visit hackernoon.com.


    Large Language Models (LLMs) like OpenAI’s GPT are revolutionizing how we interact with technology. LLMs are trained on vast amounts of text data, making them ideal for applications such as chatbots. Prompt Engineering is the art of designing prompts from specific responses from an AI.

    6 min
  • How Technology Can Make Stress-Relief More Accessible in the Near Future

    This story was originally published on HackerNoon at: https://hackernoon.com/how-technology-can-make-stress-relief-more-accessible-in-the-near-future.


    Discover how AI and robotics are making massage therapy more accessible to combat stress and heart disease.
    Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.
    You can also check exclusive content about #ai-in-healthcare, #healthtech, #accessible-massage-therapy, #mental-well-being, #stress-relief, #stress-management-tech, #ai-for-healthcare-education, #smart-healthcare-solutions, and more.


    This story was written by: @dennisledenkof. Learn more about this writer by checking @dennisledenkof's about page,
    and for more stories, please visit hackernoon.com.


    AI in healthcareAI and robotic technologies are poised to make massage therapy more accessible, addressing stress—a major contributor to heart disease—by bridging gaps in availability and cost.

    6 min
  • Video Scene Location Recognition Using AI: Methodology

    This story was originally published on HackerNoon at: https://hackernoon.com/video-scene-location-recognition-using-ai-methodology.


    This study explores scene recognition in TV series using neural networks, tested on The Big Bang Theory, with various layers like LSTM and pooling methods.
    Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.
    You can also check exclusive content about #neural-networks, #scene-recognition, #tv-series-analysis, #convolutional-networks, #lstm-layers, #video-classification, #machine-learning, #big-bang-theory-dataset, and more.


    This story was written by: @rendering. Learn more about this writer by checking @rendering's about page,
    and for more stories, please visit hackernoon.com.


    The input consists of video files and a text file. The video files are divided into independent episodes. The textfile is contains manually created metainformation about every scene. The scene is understand as sequence of frames, that are not interrupted by another frame with different scene location label.

    12 min
  • Nucleoid: Neuro-Symbolic AI With Declarative Logic - What You Need to Know

    This story was originally published on HackerNoon at: https://hackernoon.com/nucleoid-neuro-symbolic-ai-with-declarative-logic-what-you-need-to-know.


    Nucleoid is Declarative (Logic) Runtime Environment, which is a type of Symbolic AI used for reasoning engine in Neuro-Symbolic AI.
    Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.
    You can also check exclusive content about #artificial-intelligence, #javascript, #future-of-ai, #software-development, #nodejs, #nucleoid, #neuro-symbolic-ai, #neural-networks, and more.


    This story was written by: @canmingir. Learn more about this writer by checking @canmingir's about page,
    and for more stories, please visit hackernoon.com.


    Nucleoid is Declarative (Logic) Runtime Environment, which is a type of Symbolic AI used for reasoning engine in Neuro-Symbolic AI. Nucleoid runtime tracks given statements in JavaScript syntax and creates relationships between variables, objects, and functions etc. in the logic graph.

    7 min
  • AI Regulations and Standards - ISO/IEC 42001

    This story was originally published on HackerNoon at: https://hackernoon.com/ai-regulations-and-standards-isoiec-42001.


    Learn how ISO 42001 AI standards and regulations ensure fairness, transparency, accountability, robustness, and privacy in global AI governance.
    Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.
    You can also check exclusive content about #ai, #ai-regulations, #ai-models, #future-of-ai, #eu-ai-act, #ai-regulation-in-the-usa, #isoiec-42001, #the-isoiec-42001-framework, and more.


    This story was written by: @patriciadehemricourt. Learn more about this writer by checking @patriciadehemricourt's about page,
    and for more stories, please visit hackernoon.com.


    Artificial Intelligence is here. ISO/IEC 42001 is the world's first international standard for AI management systems. Learn what are its main components and how to implement it

    13 min
  • On-Device AI Models and Core ML Tools: Insights From WWDC 2024

    This story was originally published on HackerNoon at: https://hackernoon.com/on-device-ai-models-and-core-ml-tools-insights-from-wwdc-2024.


    Enhance your AI model deployment on Apple devices with the latest updates from WWDC 2024. Discuss improvements in Core ML tools
    Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.
    You can also check exclusive content about #on-device-ai, #apple-wwdc, #core-ml, #on-device-language-models, #palettization-explained, #what-is-quantization, #what-are-core-ml-tools, #new-apple-updates, and more.


    This story was written by: @mniagolov. Learn more about this writer by checking @mniagolov's about page,
    and for more stories, please visit hackernoon.com.


    Apple has released updates to its Core ML tools. The updates are aimed at improving the efficiency and effectiveness of deploying machine learning (ML) models on Apple devices. Here is the breakdown of these innovations, how they affect developers, and the advantages for the end users. Core ML Tools (*coremltools*) is a Python package for converting third-party models to format suitable for Core ML.

    10 min

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