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What is ExtraTrees Classifier? When to use it? How to implement it? Photo by Eunice Lituañas on Unsplash Tree based models have increased in popularity over the last decade, primarily due to their robust nature. Tree-based models can be used on any type of data (categorical/continuous), can be used on data that is not normally distributed, and requ ...
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I want the topics to learn to start and progress in natural language processing. I prefer to not watch videos because I can learn faster by reading.
If there is a website where I can learn NLP please let me know! or just giving me the modular topic names is perfect too.
Thank you. ...
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Learn how the Boruta algorithm works for feature selection. Explanation + template Photo by Caroline on Unsplash The feature selection process is fundamental in any machine learning project. In this post we’ll go through the Boruta algorithm, which allows us to create a ranking of our features, from the most important to the least impacting for our ...
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5 minutes explanation of how anomaly detection can be improved by passing images with random patterns to the autoencoder Photo by Neven Krcmarek on Unsplash In this story, I am going to review the reconstruction-by-inpainting anomaly detection (RIAD) method [1], introduced by the University of Ljubljana. There are two main concepts in this paper: R ...
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Solving challenges with DataLoaders, Metalhead.jl, and Twin Network design Photo by Luca Bravo on Unsplash Earlier this year, I was working on a project in PyTorch to create a deep learning model that could detect disease in unseen species. Recently, I decided to rebuild the project in Julia, and use it as an exercise in learning Flux.jl [1], Julia ...
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Opinion Facebook Prophet is highly popular for time-series forecasting. Let me show you why I am not a big fan and what else you can use. Photo by mostafa meraji on Unsplash Introduction Facebook Prophet is arguably one of the most widely known tools for time-series forecasting and related tasks. Ask any data scientist who works with time-series da ...
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Posted by AJ Piergiovanni and Anelia Angelova, Research Scientists, Google Research, Brain Team Video is an ubiquitous source of media content that touches on many aspects of people’s day-to-day lives. Increasingly, real-world video applications, such as video captioning, video content analysis, and video question-answering (VideoQA), rely on model ...
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This week, I uploaded a newer version of the R package recogito to CRAN. The recogito R package provides tools to manipulate and annotate images and text in shiny. It is a htmlwidgets R wrapper around the excellent recogito-js and annotorious javascri... Continue reading: Image Annotation
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I recently encountered the PaLM (Scaling Language Modeling with Pathways) paper from Google Research and it opened up a can of worms of ideas I’ve felt I’ve intuitively had for a while, but have been unable to express – and I know I can’t be the only one. Sometimes I wonder what the original pioneers of AI – Turing, Neumann, McCarthy, etc. – would ...
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The biggest pain points when researching a product online are:
* Google results full of SEO spam and Ads* Fake reviews* Fragmented trusted sources* Inconsistent information across source
To get trustworthy reviews, many people are adding "reddit" to their search queries. The challenges here are:
* Many duplicate posts/requests* Bad search* ...
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How many papers I have read that have explicitly mentioned that their dataset and/or code is available for public use but in practice they rarely if ever actually are. Most of the time they don’t have a publicly available link and expect you to mail them, in which case too they reply maybe once for every ten papers.
It’s one thing to not want t ...
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Annotating images for machine learning ensures that AI can understand what it sees in them and use that information effectively and efficiently. The primary goal of picture annotation for ML is to provide context for the AI when it’s processing an image. The more specific you are, the better the AI will be able to understand the subject matter and
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Posted by Bingyi Cao, Software Engineer, Google Research, and Mário Lipovský, Software Engineer, Google Lens Computer vision models see daily application for a wide variety of tasks, ranging from object recognition to image-based 3D object reconstruction. One challenging type of computer vision problem is instance-level recognition (ILR) — given an ...
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Hi all, I've been writing a new textbook.  It's titled "Understanding Deep Learning" and will be published by MIT press. A partial draft is now available at:
https://udlbook.github.io/udlbook/
It's not the most applied book (it has no code) and it's not t ...
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Nearly all papers published do only include positive results but rarely conclude with statements like „we tried this but it didn’t work out“. ...
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Hi all, I've been writing a new textbook.  It's entitled "Understanding Deep Learning" and will be published by MIT press. A partial draft is now available at:
https://udlbook.github.io/udlbook/
It's not the most applied book (it has no code) and it's no ...
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Hey guys I’m looking for best universities around the world that teaches machine learning speciality (masters degree) ? and do self taught machine learning engineers find jobs easily? ...
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Posted by Qifei Wang, Senior Software Engineer, and Feng Yang, Senior Staff Software Engineer, Google Research Deep learning models for visual tasks (e.g., image classification) are usually trained end-to-end with data from a single visual domain (e.g., natural images or computer generated images). Typically, an application that completes visual ta ...
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