How To: Using Loggly Alerts to Find Issues Before Your Customers Do | Raise your hand if you prefer to find out about your critical operational issues: on Twitter?on HackerNews?from an angry customer email? Built in and simple-to-use alerting is one of the most compelling features of Loggly’s paid plans (and part of our full-featured trial). With Loggly, you can specify the exact conditions when you want the alert to fire, with all of the capabilities of our search function at your disposal. That’s what I call Responsive Log Management! Watch how simple this is to do now, or follow the instructions below and set it up for yourself. Decide what conditions should be monitored and what number of events (or lack of events) should trigger an alert. Create a Loggly saved search that describes each condition you want to monitor. If you can search for it, you can alert off it.
Quickml - An easy-to-use but powerful and fast machine learning library for Java Mathematics for Machine Learning | Companion webpage to the book “Mathematics for Machine Learning”. Copyright 2020 by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. Published by Cambridge University Press. Top 10 Machine Learning Projects on Github Open source software is an important piece of the data science puzzle. According to the most recent KDnuggets data science software poll results, 73% of data scientists used free software in the previous 12 months. While there are many sources of such tools on the internet, Github has become a de facto clearinghouse for all types of open source software, including tools used in the data science community. The importance, and central position, of machine learning to the field of data science does not need to be pointed out. The following is an overview of the top 10 machine learning projects on Github. 1. Machine learning in Python. The top project is, unsurprisingly, the go-to machine learning library for Pythonistas the world over, from industry to academia. 2. A curated list of awesome Machine Learning frameworks, libraries and software. This is a curated list of machine learning libraries, frameworks, and software. 3. PredictionIO is a general purpose framework. 4.
jackschaedler/goya “I Mapped the Invisible”: American High School Student Groundbreaking AI Reveals 1.5 Million Space Objects Previously Hidden from Astronomers In a remarkable turn of events that challenges the way we think about space exploration, a high school student in California has made a discovery that could reshape our understanding of the cosmos. Matteo Paz, a teenager with a sharp aptitude for computer science, has developed an artificial intelligence model that uncovered 1.5 million space objects previously unknown to astronomers. The objects were hidden in plain sight within a vast dataset collected by NASA’s NEOWISE mission, a project designed to track near-Earth asteroids. The Genesis of an Unexpected Discovery Matteo Paz’s journey into deep space began during the summer of 2022 when he participated in Caltech’s Planet Finder Academy. The program, led by Professor Andrew Howard, aims to provide high school students with direct exposure to advanced astronomical research. Initially designed to observe and track asteroids near Earth, NEOWISE had spent more than a decade gathering infrared data that covered the entire sky.
tensorflow/tensorflow: Computation using data flow graphs for scalable machine learning Setting up Subversion and websvn on Debian | HowtoForge - Linux Setting up Subversion and websvn on Debian Purpose of this howto This howto will illustrate a way to install and configure Subversion and websvn on a Debian server with the following features: multiple repository Subversion access to the repositories via WebDAV (http, https) and ssh Linux system account access control and/or Apache level access control a secured websvn (php web application for easy code browsing) configured syntax coloring in websvn with gnu enscript I will not specifically configure inetd with svnserve in this howto. Packages that are assumed to already be installed This howto assumes PHP and apache2 are installed and configured. Setting up Subversion Subversion packages As root you can enter the following commands to install the packages required for our Subversion setup: # apt-get update # apt-get install subversion # apt-get install libapache2-svn The package libapache2-svn will install the subversion WebDAV apache module. Creating and populating repositories Configuration
Scientists just developed a new AI modeled on the human brain — it's outperforming LLMs like ChatGPT at reasoning tasks Scientists have developed a new type of artificial intelligence (AI) model that can reason differently from most large language models (LLMs) like ChatGPT, resulting in much better performance in key benchmarks. The new reasoning AI, called a hierarchical reasoning model (HRM), is inspired by the hierarchical and multi-timescale processing in the human brain — the way different brain regions integrate information over varying durations (from milliseconds to minutes). Scientists at Sapient, an AI company in Singapore, say this reasoning model can achieve better performance and can work more efficiently. The HRM model has 27 million parameters while using 1,000 training samples, the scientists said in a study uploaded June 26 to the preprint arXiv database (which has yet to be peer-reviewed). HRM scored 40.3% in ARC-AGI-1, compared with 34.5% for OpenAI's o3-mini-high, 21.2% for Anthropic's Claude 3.7 and 15.8% for Deepseek R1. Related: AI is entering an 'unprecedented regime.'
First Steps with TensorFlow: Toolkit | Machine Learning Crash Course | Google Developers Tensorflow is a computational framework for building machine learning models. TensorFlow provides a variety of different toolkits that allow you to construct models at your preferred level of abstraction. You can use lower-level APIs to build models by defining a series of mathematical operations. The following figure shows the current hierarchy of TensorFlow toolkits: Figure 1. The following table summarizes the purposes of the different layers: TensorFlow consists of the following two components: These two components are analogous to Python code and the Python interpreter. Which API(s) should you use? We'll use tf.estimator for the majority of exercises in Machine Learning Crash Course. tf.estimator is compatible with the scikit-learn API. Very broadly speaking, here's the pseudocode for a linear classification program implemented in tf.estimator:
Deep learning, transformers and graph neural networks: a linear algebra perspective | Numerical Algorithms Graphs serve as powerful tools for representing relationships and interactions in various domains such as social networks (e.g. identify fake news, predict future friends, learn multi-faceted interactions among users) [50, 51], chemistry (generate new drugs and materials, predict chemical properties) [52, 53], recommender systems (e.g., leverage consumer-product choices) [54, 55], knowledge graphs (reasoning with entity relationships) [56, 57], natural language processing (e.g. large language models) [17], physics (e.g. learn from interactions of particles in systems, detect particles, accelerate physics research) [58, 59], neuroscience (e.g., learn functions of brain regions through connectivity, understand brain mechanisms and neuro-degenerative diseases) [60], transportation (e.g., learn traffic behavior across road networks, predict time estimates across multilayered networks) and more. 5.1 Graph neural networks (GNNs) 5.2 Graph convolutional networks (GCNs) 1. 1. Positional encoding
DeepMind AI One-Ups Mathematicians at a Calculation Crucial to Computing DeepMind has done it again. After solving a fundamental challenge in biology—predicting protein structure—and untangling the mathematics of knot theory, it’s taken aim at a fundamental computing process embedded inside thousands of everyday applications. From parsing images to modeling weather or even probing the inner workings of artificial neural networks, the AI could theoretically speed up calculations across a range of fields, increasing efficiency while cutting energy use and costs. But more impressive is how they did it. “Algorithms have been used throughout the world’s civilizations to perform fundamental operations for thousands of years,” wrote co-authors Drs. AlphaTensor blazes a trail to a new world where AI designs programs that outperform anything humans engineer, while simultaneously improving its own machine “brain.” Enter the Matrix Multiplication The problem AlphaTensor confronts is matrix multiplication. But what if there are even more efficient methods?