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image classification


A Simple Convolutional Neural Network Summary for Binary Image Classification With Keras.

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Convolutional neural networks (CNN's) are the main deep learning tool to use for image processing. I recently used a CNN for my latest student project here at Flatiron and got to have a look at how they work and how they differ from dense neural networks, in addition to how they perform better when working with images and python. In my project, I was able to classify patient x-ray images to determine whether they had pneumonia or not. There are also many other uses for image processing in the medical field and in other fields of work and study. Next, we'll try to show the simplest, most basic breakdown of some of these steps so that you can get on your way to building a CNN for image classification with Keras.


Video classification with FastAI and Deep Learning

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In this tutorial, you will learn how to perform video classification using FastAI, Python, and Deep Learning. FastAI is a Deep Learning library that is built on the top of Pytorch. There are freely available tutorials/courses for FastAI. I am also currently enrolled in Practical Deep Learning for Coders course.


10 Papers You Should Read to Understand Image Classification in the Deep Learning Era

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Computer vision is a subject to convert images and videos into machine-understandable signals. With these signals, programmers can further control the behavior of the machine based on this high-level understanding. Among many computer vision tasks, image classification is one of the most fundamental ones. It not only can be used in lots of real products like Google Photo's tagging and AI content moderation but also opens a door for lots of more advanced vision tasks, such as object detection and video understanding. Due to the rapid changes in this field since the breakthrough of Deep Learning, beginners often find it too overwhelming to learn. Unlike typical software engineering subjects, there are not many great books about image classification using DCNN, and the best way to understand this field is though reading academic papers. But what papers to read? In this article, I'm going to introduce 10 best papers for beginners to read.


A more parameter-efficient SOTA bottleneck! (2020/07)

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CNN are great blablabla… Let's get to the point. SOTA for image classification on Imagenet is EfficientNet with 88.5% top 1 accuracy in 2020. In this article, I introduce a combination of EfficientNet and Efficient Channel Attention (ECA) to highlight the results of the ECA paper from Tianjin/Dalian/Harbin universities. MobileNetV2 is composed of multiple blocks which are called linear bottlenecks or inverted residuals (they're almost the same). Linear Bottleneck is a residual layer composed of one 1x1 convolution, followed by a 3x3 depthwise convolution, then finally a 1x1 convolution.


Deep-belief networks detect glioblastoma tumors from MRI scans

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Scientists from South Ural State University, in collaboration with foreign colleagues, have proposed a new model for the classification of MRI images based on a deep-belief network that will help to detect malignant brain tumors faster and more accurately. The research study was published in the Journal of Big Data, indexed in the scientometric Scopus database. Glioblastoma (GBM) is a stage 4 malignant brain tumor in which a large proportion of tumor cells are reproducing at any given moment. Such tumors are life-threatening and can lead to partial or complete mental and physical disability. The study was carried out by an international group of scientists from Indian universities and South Ural State University.


Convolutional Neural Networks for Dummies

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A notification pops on your Social media handle saying, somebody uploaded a picture that might have you in it. This is the magic of Image Classification. Convolution Neural Networks(CNN) lies under the umbrella of Deep Learning. They are utilized in operations involving Computer Vision. Nowadays since the range of AI is expanding enormously, we can easily locate Convolution operation going around us.


Intro To Computer Vision - Classification

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Thanks to advancements in deep learning & artificial neural networks, computer vision is increasingly capable of mimicking human vision & is paving the way for self-driving cars, medical diagnosis, scanning recorded surveillance, manufacturing & much more. In this introductory workshop, Sage Elliot will give an overview of deep learning as it related to computer vision with a focused discussion around image classification. You will also learn about careers in computer vision & who are some of the biggest users of this technology. About Your Instructor: Sage Elliott is a Machine Learning Developer Evangelist for Sixgill with about 10 years of experience in the engineering space. He has passion for exploring new technologies & building communities.


FrugalML switches between APIs to improve image classification and cut costs

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Stanford University researchers developed a framework that enables developers to intelligently switch between multiple cloud AI APIs (including those from Google and Microsoft) within a budget constraint. In preliminary experiments, they claim their system -- FrugalML -- typically leads to a more than 50% cost reduction while matching the accuracy of the best single API. Third-party machine learning APIs come with several challenges. One is that companies don't price workloads the same. Moreover, different APIs perform either better or worse on different types of data.


A beginner's guide to how machines learn

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Once you get into artificial intelligence and machine learning, there's no way to avoid three terms: These are the three most common ways of how machines can learn, therefore understanding their meaning and differences is important to know when getting started with artificial intelligence. If you are new to the field, we recommend that you first read about the different disciplines of artificial intelligence. Note: There are also other ways for machines to learn but this would break the format. Also, it is not necessary when starting out. Think of it like this: When you need them, you will know.


A beginner's guide to how machines learn

#artificialintelligence

Once you get into artificial intelligence and machine learning, there's no way to avoid three terms: These are the three most common ways of how machines can learn, therefore understanding their meaning and differences is important to know when getting started with artificial intelligence. If you are new to the field, we recommend that you first read about the different disciplines of artificial intelligence. Note: There are also other ways for machines to learn but this would break the format. Also, it is not necessary when starting out. Think of it like this: When you need them, you will know.