This article explores the complexities of counting fish passing through large hydroelectric dams and the challenges of coordinating human-in-the-loop dataset production. It highlights the importance of complying with regulations set by the Federal Energy Regulatory Commission and the potential impact of hydroelectric dams on fish populations.
This article explores outlier detection algorithms in machine learning and their application to Major League Baseball's 2023 batting statistics. The four algorithms compared are Elliptic Envelope, Local Outlier Factor, One-Class Support Vector Machine with Stochastic Gradient Descent, and Isolation Forest. The goal is to gain insight into their behavior and limitations in order to determine whi...
Amazon announces the integration of Amazon DocumentDB with Amazon SageMaker Canvas, enabling users to build ML models without coding. This integration allows businesses to analyze unstructured data stored in Amazon DocumentDB and generate predictions without relying on data engineering and data science teams.
Boosting data ingestion in the range-set-blaze Crate by 7x by delegating calculations to little crabs. Rule 7: Use Criterion benchmarking to pick an algorithm and discover that LANES should (almost) always be 32 or 64.
This article explores the logic behind the fundamental algorithm used in gradient descent, focusing on the exponential moving average. It discusses the motivation behind the method, its formula, and provides a mathematical interpretation of its weight distribution.
This article explains how to benchmark using the criterion crate and how to benchmark across different compiler settings, providing insights on performance effects and comparisons across CPUs. The range-set-blaze crate is used as an example to measure SIMD settings, optimization levels, and various input lengths.
ICL, a multinational manufacturing and mining corporation, developed in-house capabilities using machine learning and computer vision to automatically monitor their mining equipment. With support from the AWS Prototyping program, they were able to build a framework on AWS using Amazon SageMaker to extract vision from 30 cameras, with the potential to scale to thousands.
Amazon Comprehend offers pre-trained and custom APIs for natural-language processing. They have developed a pre-labeling tool that automatically annotates documents using existing tabular entity data, reducing the manual work needed to train accurate custom entity recognition models.
Dive into the world of artificial intelligence â build a deep reinforcement learning gym from scratch. Gain hands-on experience and develop your own gym to train an agent to solve a simple problem, setting the foundation for more complex environments and systems.
Text-to-image generation is a rapidly growing field of AI, with Stable Diffusion allowing users to create high-quality images in seconds. The use of Retrieval Augmented Generation (RAG) enhances prompts for Stable Diffusion models, enabling users to create their own AI assistant for prompt generation.
OpenAI's ChatGPT, a groundbreaking AI language model, sparked excitement with its impressive abilities, including excelling in exams and playing chess. However, skeptics argue that true intelligence should not be confused with memorization, leading to scientific studies exploring the distinction and making the case against AGI.
Talent.com collaborates with AWS to develop a job recommendation engine using deep learning, processing 5 million daily records in less than 1 hour. The system includes feature engineering, deep learning model architecture design, hyperparameter optimization, and model evaluation, all run using Python.
Amazon SageMaker Studio now offers a fully managed Code Editor based on Code-OSS, along with JupyterLab and RStudio, allowing ML developers to customize and scale their IDEs using flexible workspaces called Spaces. These Spaces provide persistent storage and runtime configurations, improving workflow efficiency and allowing for seamless integration of generative AI tools.
Getir, the ultrafast grocery delivery pioneer, has implemented an end-to-end workforce management system using Amazon Forecast and AWS Step Functions, resulting in a 70% reduction in modelling time and a 90% improvement in prediction accuracy. This comprehensive project calculates courier requirements and solves the shift assignment problem, optimizing shift schedules and minimizing missed orders.
Large language models (LLMs) like GPT NeoX and Pythia are gaining popularity, with billions of parameters and impressive performance. Training these models on AWS Trainium is cost-effective and efficient, thanks to optimizations like rotational positional embedding (ROPE) and partial rotation techniques.