Machine Learning Tech Brief By HackerNoon

Machine Learning Tech Brief By HackerNoon

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Machine Learning Tech Brief By HackerNoon episodes

  • Understanding Stochastic Average Gradient

    This story was originally published on HackerNoon at: https://hackernoon.com/understanding-stochastic-average-gradient.


    Techniques like Stochastic Gradient Descent (SGD) are designed to improve the calculation performance but at the cost of convergence accuracy.
    Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.
    You can also check exclusive content about #ml, #machine-learning, #algorithms, #gradient-descent, #ai-optimization, #model-optimization, #loss-functions, #convergence-rates, and more.


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


    Gradient descent is a popular optimization used for locating global minima of the provided objective functions. The algorithm uses the gradient of the objective function to traverse the function slope until it reaches the lowest point.
    Full Gradient Descent (FG) and Stochastic Gradient Descent (SGD) are two popular variations of the algorithm. FG uses the entire dataset during each iteration and provides a high convergence rate at a high computation cost. At each iteration, SGD uses a subset of data to run the algorithm. It is far more efficient but with an uncertain convergence.
    Stochastic Average Gradient (SAG) is another variation that provides the benefits of both previous algorithms. It uses the average of past gradients and a subset of the dataset to provide a high convergence rate with low computation. The algorithm can be further modified to improve its efficiency using vectorization and mini-batches.

    6 min
  • Bulldozer Intelligence: Here's Why LLMs Won’t Be AGI

    This story was originally published on HackerNoon at: https://hackernoon.com/bulldozer-intelligence-heres-why-llms-wont-be-agi.


    LLMs are functional intelligence; they work to achieve an end goal. An LLM aims to generate as many words as efficiently as possible.
    Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.
    You can also check exclusive content about #llms, #agi, #bulldozer-intelligence, #the-truth-about-llms, #will-ai-replace-humans, #future-of-ai, #ai-and-llms, #llms-use-cases, and more.


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


    A bulldozer has just two functions, for moving or breaking things. So if it can’t break or move it, throw a bigger weight at it. They are functional tools, the same as LLM (Large Language Models) While LLMs create new things through synthesis, which mimics a sign of human intelligence, LLMs lack creativity.

    4 min
  • Effortless 2D-Guided, 3D Gaussian Segmentation: Related Work

    This story was originally published on HackerNoon at: https://hackernoon.com/effortless-2d-guided-3d-gaussian-segmentation-related-work.


    Efficient 3D Gaussian segmentation guided by 2D models achieves fast, accurate multi-object segmentation, advancing 3D scene understanding and editing.
    Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.
    You can also check exclusive content about #machine-learning, #gaussian-clustering, #3d-gaussian-segmentation, #3d-scene-understanding, #2d-to-3d-supervision, #ai-in-3d-graphics, #semantic-information-learning, #point-based-rendering, and more.


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


    3D Gaussian, a recently proposed explicit representation method, has attained remarkable achievements in three-dimensional scene reconstruction. Using a series of scene images and corresponding camera data, it employs 3D Gaussians to depict scene objects. Gaussian Splatting then utilizes point-based rendering for efficient 3D to 2D projection.

    6 min
  • Why the Book Publishing Industry Is Terrified of AI

    This story was originally published on HackerNoon at: https://hackernoon.com/why-the-book-publishing-industry-is-terrified-of-ai.


    AI isn't all bad, but it could potentially destroy the publishing industry. Are publisher fears of AI justified or unwarranted?
    Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.
    You can also check exclusive content about #artificial-intelligence, #publishing, #books, #large-language-models, #creative-writing, #ai-writing, #copyright, #hackernoon-top-story, and more.


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


    AI isn't the first threat to the book publishing industry, but it is the biggest (so far). For now, AI can't write, edit, or publish better than humans — but it may not stay that way forever. AI is stealing jobs and stealing authors' work, and there are no protections yet for writers. However, AI has a few benefits; it can take on mundane work and make human content stand out. It's too early to tell whether AI will transform, replace, or topple the industry.

    9 min
  • $10M for Founders, AI Agents, and More. Plus, Can AI Outperform Human Therapists?

    This story was originally published on HackerNoon at: https://hackernoon.com/$10m-for-founders-ai-agents-and-more-plus-can-ai-outperform-human-therapists.


    Multi-agent systems represent a significant leap in AI technology. These systems involve several AI entities working together to complete tasks more efficiently
    Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.
    You can also check exclusive content about #artificial-intelligence-trends, #future-of-ai, #autonomous-agents, #llms, #ai-therapy, #microsoft-and-nvidia, #multi-agent-collaboration, #ai-startups, and more.


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


    This edition is packed with insights and updates on AI developments that are essential for startup founders and business leaders. Multi-agent systems represent a significant leap in AI technology. These systems involve several AI entities working together to complete tasks more efficiently than a single AI system. Hugging Face’s $10 million GPU Boost for AI Startups is democratizing AI by offering free shared GPU access.

    14 min
  • How To Use Target Encoding in Machine Learning Credit Risk Models – Part 1

    This story was originally published on HackerNoon at: https://hackernoon.com/how-to-use-target-encoding-in-machine-learning-credit-risk-models-part-1.


    Discover how to use target encoding and weight of evidence for transforming categorical variables in supervised learning, enhancing model performance.
    Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.
    You can also check exclusive content about #ml-credit-risk-models, #target-encoding, #ml-models, #output-encoding, #logistic-regression, #piecewise-constant-model, #predictive-ml-modelling, #ml-model-optimization, and more.


    This story was written by: @varunnakra1. Learn more about this writer by checking @varunnakra1's about page,
    and for more stories, please visit hackernoon.com.


    Target encoding transforms categorical variables into numerical values based on the target variable, while Weight of Evidence (WoE) applies this concept to continuous variables for binary classification. WoE calculates log-odds differences between specific regions and overall averages, offering a powerful tool for credit risk modeling and other applications.

    7 min
  • AI Boom Spurs US-China Chip Race

    This story was originally published on HackerNoon at: https://hackernoon.com/ai-boom-spurs-us-china-chip-race.


    Explore the intense US-China rivalry in the semiconductor industry, driven by the AI boom and geopolitical tensions, reshaping the future of global tech.
    Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.
    You can also check exclusive content about #ai-boom, #ai-chip-market, #us-china-chip-war, #ai-and-semiconductors, #nvidia-ai-chips, #us-ai-chip-market, #china-ai-chip-market, #hackernoon-top-story, and more.


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


    The semiconductor chip tug-of-war between the US and China has chip companies scrambling to keep business going as usual. Apple’s M4 chip can conduct 38 trillion operations per second, making it much better for AI tasks and enhancing features like Live Captions and Visual Look Up on the iPad Pro.

    8 min
  • Why Quadratic Cost Functions Are Ineffective in Neural Network Training

    This story was originally published on HackerNoon at: https://hackernoon.com/why-quadratic-cost-functions-are-ineffective-in-neural-network-training.


    Explore why quadratic cost functions hinder neural network training and how cross-entropy improves learning efficiency in deep learning models.
    Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.
    You can also check exclusive content about #deep-learning, #neural-networks, #what-is-cross-entropy, #sigmoid-activation-function, #neural-network-training, #quadratic-cost-function, #cross-entropy-cost-function, #hackernoon-top-story, and more.


    This story was written by: @varunnakra1. Learn more about this writer by checking @varunnakra1's about page,
    and for more stories, please visit hackernoon.com.


    One of the most common question asked during deep learning knowledge interviews is - “Why can’t we use a quadratic cost function to train a Neural Network?**” We will delve deep into the answer for that. There will be a lot of Math involved but nothing crazy! and I will keep things simple yet precise.

    9 min
  • Will You Really Let AI Do the Thinking For You?

    This story was originally published on HackerNoon at: https://hackernoon.com/will-you-really-let-ai-do-the-thinking-for-you.


    Learn why active creation is crucial for innovation and personal fulfillment.
    Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.
    You can also check exclusive content about #artificial-intelligence, #creativity, #human-creativity, #creative-process, #creativity-neuroscience, #active-creation, #technology-and-society, #hackernoon-top-story, and more.


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


    The digital landscape is becoming a uniform echo chamber. It’s all driven by AI that replicates but never truly creates.

    10 min
  • Syntax Error-Free and Generalizable Tool Use for LLMs: Abstract and Intro

    This story was originally published on HackerNoon at: https://hackernoon.com/syntax-error-free-and-generalizable-tool-use-for-llms-abstract-and-intro.


    Researchers propose TOOLDEC, a finite-state machine-guided decoding for LLMs, reducing errors and improving tool use.
    Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.
    You can also check exclusive content about #llms, #tool-augmentation, #syntax-errors, #decoding-algorithm, #finite-state-machine, #tooldec, #tool-selection, #syntax-error-free, 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.


    Researchers propose TOOLDEC, a finite-state machine-guided decoding for LLMs, reducing errors and improving tool use.

    8 min

About Machine Learning Tech Brief By HackerNoon

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