#deep-learning

A beginner’s guide to Deep Learning Applications in Medical Imaging
▻https://hackernoon.com/a-beginners-guide-to-deep-learning-applications-in-medical-imaging-7aa3b

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Let us first understand what medical imaging is before we delve into how deep learning and other similar expert systems can help medical professional such as radiologists in diagnosing their patients.This is how Wikipedia defines Medical Imaging:Medical imaging is the technique and process of creating visual representations of the interior of a body for clinical analysis and medical intervention, as well as visual representation of the function of some organs or tissues (physiology). Medical imaging seeks to reveal internal structures hidden by the skin and bones, as well as to diagnose and treat disease. Medical imaging also establishes a database of normal anatomy and physiology to make it possible to identify abnormalities. Although imaging of removed organs and tissues can be (...)

#keras #deep-learning #artificial-intelligence #medicine #machine-learning

Build an Abstractive Text Summarizer in 94 Lines of #tensorflow !! (Tutorial 6)
▻https://hackernoon.com/build-an-abstractive-text-summarizer-in-94-lines-of-tensorflow-tutorial-

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Build an Abstractive Text Summarizer in 94 Lines of Tensorflow !! (Tutorial 6)This tutorial is the sixth one from a series of tutorials that would help you build an abstractive text summarizer using tensorflow , today we would build an abstractive text summarizer in tensorflow in an optimized way .Today we would go through one of the most optimized models that has been built for this task , this model has been written by dongjun-Lee , this is the link to his model , I have used his model model on different datasets (in different languages) and it resulted in truly amazing results , so I would truly like to thank him for his effortI have made multiple modifications to the model to enable it to enable it to run seamlessly on google colab (link to my model) , and i have hosted the data onto (...)

#machine-learning #nlp #ai #deep-learning

Top 5 Machine Learning Projects for Beginners
▻https://hackernoon.com/top-5-machine-learning-projects-for-beginners-47b184e7837f?source=rss---

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Purchased Image designed by PlargueDoctorAs a beginner, jumping into a new machine learning project can be overwhelming. The whole process starts with picking a data set, and second of all, study the data set in order to find out which machine learning algorithm class or type will fit best on the set of data.Here are some tips from experts on how to get started:Find a modestly sized data set which is relatively easy to analyze. Good places to search are the UCI ML Repository and Kaggle.Experiment with the data set. To get a good “feeling” with the data set, you can run several top machine learning algorithms on the data to see how it behaves and what performance each algorithm achieves.Pick the algorithm with the best performance and tune it accordingly.Ok, now we are packed with a couple (...)

#deep-learning #machine-learning #artificial-intelligence #ai #programming

Malicious Attacks to Neural Networks
▻https://hackernoon.com/malicious-attacks-to-neural-networks-8b966793dfe1?source=rss----3a8144ea

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Adversarial Examples for Humans — An IntroductionThis article is based on a twenty-minute talk I gave for TrendMicro Philippines Decode Event 2018. It’s about how malicious people can attack deep neural networks. A trained neural network is a model; I’ll be using the terms network (short for neural network) and model interchangeably throughout this article.Deep learning in a nutshellThe basic building block of any neural network is an artificial neuron.Essentially, a neuron takes a bunch of inputs and outputs a value. A neuron gets the weighted sum of the inputs (plus a number called a bias) and feeds it to a non-linear activation function. Then, the function outputs a value that can be used as one of the inputs to other neurons.You can connect neurons in various different (usually (...)

#artificial-intelligence #neural-networks #deep-learning #machine-learning

Beam Search & Attention for text summarization made easy (Tutorial 5)
▻https://hackernoon.com/beam-search-attention-for-text-summarization-made-easy-tutorial-5-3b7186

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This tutorial is the fifth one from a series of tutorials that would help you build an abstractive text summarizer using tensorflow , today we would discuss some useful modification to the core RNN seq2seq model we have covered in the last tutorialThese Modifications areBeam SearchAttention ModelAbout SeriesThis is a series of tutorials that would help you build an abstractive text summarizer using tensorflow using multiple approaches , you don’t need to download the data nor do you need to run the code locally on your device , as data is found on google drive , (you can simply copy it to your google drive , learn more here) , and the code for this series is written in Jupyter notebooks to run on google colab can be found hereWe have covered so far (code for this series can be found here)0. (...)

#nlp #ai #technology #machine-learning #deep-learning

Top 5 Trends of Artificial Intelligence (AI) 2019
▻https://hackernoon.com/top-5-trends-of-artificial-intelligence-ai-2019-693f7a5a0f7b?source=rss-

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To estimate the trends of Artificial Intelligence (AI) 2019, we need to remember that 2018 witnessed a multitude of platforms, applications, and tools which are based on artificial intelligence and machine learning.Such technology trends laid huge implications on software and the Internet industry. Furthermore, its effects on fields like healthcare, manufacturing, agriculture, and automobile are worth-noticing.The advancement of ML and AI-related technologies will have a long journey in 2019, or even further. Future of AI seems bright and it is supported by the fact mentioned further:Well-reputed companies like Apple, Amazon, Google, Facebook, IBM, Microsoft and the like are investing a lot in the research and development of AI, which will definitely bring consumers and AI (...)

#technology-trends #ai-future #machine-learning-ai #artificial-intelligence #deep-learning

Interview with #kaggle Grandmaster, Data Scientist at Point API: Pavel Pleskov
▻https://hackernoon.com/interview-with-kaggle-grandmaster-data-scientist-at-point-api-pavel-ples

Interview with Kaggle Grandmaster, Data Scientist at Point API (NLP startup): Pavel PleskovPart 25 of The series where I interview my heroes.Index to “Interviews with ML Heroes”Today I’m honored to be interviewing a Kaggle Grandmaster from the ods.ai community.I’m excited to be talking to Competitions GrandMaster (Ranked #4, Kaggle: @ppleskov) and Discussions Expert: (Ranked #29): Pavel PleskovPavel has a background in Math and Economics. Currently, he is working as a Data Scientist at Point API (NLP startup). He has worked as a Financial Consultant and as a Quant Researcher earlier.Sanyam:​ Hello Grandmaster, Thank you for taking the time to do this.Pavel Pleskov: My pleasure!Sanyam:​ Currently, you’re one of the Top 5 ranked Comp GrandMasters and are actively working on Data Science (...)

#data-science #deep-learning #artificial-intelligence #machine-learning

A Quick Introduction to Artificial Intelligence, Machine Learning, Deep Learning and #tensorflow
▻https://hackernoon.com/a-quick-introduction-to-artificial-intelligence-machine-learning-deep-le

What used to be just a pipe dream in the realms of science fiction, artificial intelligence (AI) is now mainstream technology in our everyday lives with applications in image and voice recognition, language translations, chatbots, and predictive data analysis.In this article, we’ll introduce AI along with its related terms machine learning and deep learning. By the end of the article you should understand these terms, how things generally work and be more familiar with terms like Inception and YOLO (and no, we’re not talking about the Leonardo DiCaprio movie or some internet meme).Artificial intelligence (AI) is the simulation of human intelligence by computers. Machine learning is a branch of AI where algorithms are used to learn from data to make future decisions or predictions. Deep (...)

#open-source #artificial-intelligence #deep-learning #machine-learning

Interview with Kaggle Grandmaster, Senior CV Engineer at Lyft: Dr. Vladimir I. Iglovikov
▻https://hackernoon.com/interview-with-kaggle-grandmaster-senior-cv-engineer-at-lyft-dr-vladimir

Interview with Kaggle Grandmaster, Senior Computer Vision Engineer at Lyft: Dr. Vladimir I. IglovikovPart 24 of The series where I interview my heroes.Today, I’m honored to be talking to another great kaggler from the ODS community: (kaggle: iglovikov) Competitions Grandmaster (Ranked #97), Discussions Expert (Ranked #30): Dr. Vladimir I. IglovikovVladimir is currently working as the Senior Computer Vision Engineer at Level5, Self-Driving Division, Lyft Inc.Prior to Lyft, Vladimir was working as a Senior Data Scientist at TrueAccord. He has a background in Physics and holds a Ph.D. in Physics from UC Davis.About the Series:I have very recently started making some progress with my Self-Taught Machine Learning Journey. But to be honest, it wouldn’t be possible at all without the amazing (...)

#artificial-intelligence #machine-learning #data-science #deep-learning #computer-vision

How to Understand Machine Learning with simple Code Examples
▻https://hackernoon.com/how-to-understand-machine-learning-with-simple-code-examples-a0508dae212

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Understanding machine learning using simple code examples.“Machine Learning, Artificial Intelligence, Deep Learning, Data Science, Neural Networks”You must’ve surely read somewhere about how these things are gonna take away future jobs, overthrow us as dominant species on earth and how we’d have to find Arnold Schwarzenegger and John Connor to save humanity.With current hype, there is no surprise you might have.But, what is Machine Learning and what is Artificial Intelligence?Machine learning is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence.And what is Neural Network?Artificial neural (...)

#deep-learning #artificial-intelligence #neural-networks #machine-learning #javascript

Interview with Senior #research Scientist at the US Naval Research Laboratory: Dr. Leslie Smith
▻https://hackernoon.com/interview-with-senior-research-scientist-at-the-us-naval-research-labora

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Interview with Senior Research Scientist at the United States Naval Research Laboratory: Dr. Leslie SmithPart 23 of The series where I interview my heroes.Today, I’m super excited to be talking to Dr. Leslie Smith.Dr. Leslie SmithI’m sure Leslie needs no introduction to our friends from the fast.ai community. For our readers from outside of fast.ai:Leslie is currently working as a Senior Research Scientist at the Naval Center for Applied Research in AI, United States Naval Research Laboratory.His past research works include includes deep neural networks and reinforcement learning applied to robotics research. Prior to that, he has worked in the Maritime Surveillance Section.He has a background in Chemistry, he has done his Ph.D. in the Quantum Chemistry domain.His Research Objectives are to (...)

#machine-learning #fastai #deep-learning #artificial-intelligence

XDL Framework: Delivering powerful Performance for Large-scale Deep Learning Applications
▻https://hackernoon.com/xdl-framework-delivering-powerful-performance-for-large-scale-deep-learn

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The Alibaba tech team open sourced its self-developed deep learning framework that goes where others have failedDeep learning AI technologies have brought remarkable breakthroughs to fields including speech recognition, computer vision, and natural language processing, with many of these developments benefiting from the prevalence of open source deep learning frameworks like TensorFlow, PyTorch, and MxNet. Nevertheless, efforts to bring deep learning to large-scale, industry-level scenarios like advertising, online recommendation, and search scenarios have largely failed due to the inadequacy of available frameworks.Whereas most open source frameworks are designed for low-dimensional, continuous data such as in images and speech, a majority of Internet applications deal with (...)

#artificial-intelligence #data-analysis #machine-learning #deep-learning #hackernoon-top-story

Chars2vec: character-based language model for handling real world texts with spelling errors and…
▻https://hackernoon.com/chars2vec-character-based-language-model-for-handling-real-world-texts-w

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Chars2vec: character-based language model for handling real world texts with spelling errors and human slangThis paper describes our open source character-based language model chars2vec. This model was developed with Keras library (TensorFlow backend) and now is available for Python 2.7 and 3.0+.IntroductionCreating and using word embeddings is the mainstream approach for handling most of the #nlp tasks. Each word is matched with a numeric vector which is then used in some way if the word appears in text. Some simple models use one-hot word embeddings or initialise words with random vectors or with integer numbers. The drawback of such models is obvious – such word vectorisation methods do not represent any semantic connections between words.There are other language models, called (...)

#machine-learning #artificial-intelligence #deep-learning #neural-networks

Idiot’s Guide to Precision, Recall and Confusion Matrix
▻https://hackernoon.com/idiots-guide-to-precision-recall-and-confusion-matrix-b32d36463556?sourc

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Evaluation metrics for classification modelsBuilding Machine Learning models is fun, making sure we build the best ones is what makes a difference!Evaluating ML modelsRegression modelsRMSE is a good measure to evaluate how a machine learning model is performing.If RMSE is significantly higher in test set than training-set — There is a good chance model is overfitting.(Make sure train and test set are from same/similar distribution)What about Classification models?Guess what, evaluating a Classification model is not that simpleBut why?You must be wondering ‘Can’t we just use accuracy of the model as the holy grail metric?’Accuracy is very important, but it might not be the best metric all the time. Let’s look at why with an example -:Let’s say we are building a model which predicts if a bank (...)

#artificial-intelligence #machine-learning #ai #deep-learning #technology

My Hackathon Experiences
▻https://hackernoon.com/my-hackathon-experiences-73b0b6191409?source=rss----3a8144eabfe3---4

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My experiments in the world of hackathon started out of my boredom in office work and have turned out to be a rich collection of experiences. You can find the solutions at my github.RBL bank hackathon(3 days)Problem statement — Make a data science solution with customer dataOffline | Prize pool — 2 lakhsWhat I likedGreat food arrangementComfy workplaceCould have been betterForced to use API provided for fetching dataThe API didn’t work for 1.5 daysAPI have transaction data of only 1 user of just a few months. No machine learning was possible over it.No guidance provided on what to do with so less dataTeam presentations were private with the JuryCoinberg hackathonCoinberg hackathon(2 days)Problem statement — Cryptocurrency — Arbitrage trading | Sentiment analysis | Portfolio management | Trend (...)

#data-science #deep-learning #hackathons #machine-learning #nlp

Enriching Word Vectors with Subword Information [PAPER SUMMARY]
▻https://hackernoon.com/enriching-word-vectors-with-subword-information-paper-summary-fa66d50ea0

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Enriching Word Vectors with Subword Information [Google Colab Implementation & Paper Summary]About the Authors:This paper was published by a group of researchers from FAIR (Facebook AI research). The original authors are Piotr Bojanowski, Edouard Grave, Armand Joulin and Tomas Mikolov.The ready-to-run code for this paper is available here on Google Colab.The Basic Idea behind Word Vectors:For most of the Natural Language Processing related tasks like text classification, text summarization, text generation etc, we need to perform various computations in order to achieve maximum precision on these tasks. In order to perform these computations, we need a numerical based representation for various components of Language like words, sentences and syllables.We assign multi-dimensional (...)

#deep-learning #artificial-intelligence #nlp #machine-learning #data-science

The Awesome Duo: 6 Cases of How #fintech Benefits From AI
▻https://hackernoon.com/the-awesome-duo-6-cases-of-how-fintech-benefits-from-ai-bb408242a1c5?sou

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Photo by Alice Pasqual on UnsplashIf you’ve ever used the Internet to transfer money between accounts or apply for a bank loan or trade, you’re probably aware of how deeply rooted fintech has become in our day-to-day lives. In 2018, about 61% of Americans used digital banking services and this number is set to exceed 65% in 2022. One of the newly-emerged traits of the 4th Industrial Era, fintech is an application of fast-evolving digital technologies to improve and facilitate financial services.Companies are rapidly adopting fintech to keep abreast of the competition. The investments into this industry are also impressive: in 2018, it attracted over $16 billion investment in the UK alone, according to KMPG.On the other hand, entire countries are rapidly adopting AI technologies to compete (...)

#artificial-intelligence #chatbots #machine-learning #deep-learning

A Brief History of Computer Vision (and Convolutional Neural Networks)
▻https://hackernoon.com/a-brief-history-of-computer-vision-and-convolutional-neural-networks-8fe

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Although Computer Vision (CV) has only exploded recently (the breakthrough moment happened in 2012 when AlexNet won ImageNet), it certainly isn’t a new scientific field.Computer scientists around the world have been trying to find ways to make machines extract meaning from visual data for about 60 years now, and the history of Computer Vision, which most people don’t know much about, is deeply fascinating.In this article, I’ll try to shed some light on how modern CV systems, powered primarily by convolutional neural networks, came to be.I’ll start with a work that came out in the late 1950s and has nothing to do with software engineering.One of the most influential papers in Computer Vision was published by two neurophysiologists — David Hubel and Torsten Wiesel — in 1959. Their publication, (...)

#machine-learning #ai #computer-vision #deep-learning #hackernoon-top-story

Deep Learning vs. Machine Learning: A Simple Explanation
▻https://hackernoon.com/deep-learning-vs-machine-learning-a-simple-explanation-47405b3eef08?sour

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Machine learning and deep learning are two subsets of artificial intelligence which have garnered a lot of attention over the past two years. If you’re here looking to understand both the terms in the simplest way possible, there’s no better place to be.So if you’ll stick with me for some time, I’ll try to explain what really is the difference between deep learning vs machine learning, and how can you leverage these two subsets of AI for new and exciting business opportunities.Deep learning vs Machine learningBefore I start, I hope you would be familiar with a basic understanding of what both the terms deep learning and machine learning mean. If you don’t, here are a couple of simple definitions of deep learning and machine learning for dummies:Machine Learning for dummies:A subset of (...)

#dl-vs-ml #deep-learning #ml-algorithm #artificial-neural-network #machine-learning

Dueling Neural Networks
▻https://hackernoon.com/dueling-neural-networks-a063af14f62e?source=rss----3a8144eabfe3---4

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“What I cannot create, I do not understand.”— Richard FeynmanGANs generated by a computerThe above images look real, but more than that, they look familiar. They resemble a famous actress that you may have seen on television or in the movies. They are not real, however. A new type of neural network created them.Generative Adversarial Networks (GANs), sometimes called generative networks, generated these fake images. The NVIDIA research team used this new technique by feeding thousands of photos of celebrities to a neural network. The neural network produced thousands of pictures, like the ones above, that resembled the famous faces. They look real, but machines created them. #gans allow researchers to build images that look like the real ones that share many of the features the neural (...)

#birthday-paradox #deep-learning #generative-adversarial #machine-learning

#perceptron — Deep Learning Basics
▻https://hackernoon.com/perceptron-deep-learning-basics-3a938c5f84b6?source=rss----3a8144eabfe3-

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Perceptron — Deep Learning BasicsAn upgrade to McCulloch-Pitts Neuron.Perceptron is a fundamental unit of the neural network which takes weighted inputs, process it and capable of performing binary classifications. In this post, we will discuss the working of the Perceptron Model. This is a follow-up blog post to my previous post on McCulloch-Pitts Neuron.In 1958 Frank Rosenblatt proposed the perceptron, a more generalized computational model than the McCulloch-Pitts Neuron. The important feature in the Rosenblatt proposed perceptron was the introduction of weights for the inputs. Later in 1960s Rosenblatt’s Model was refined and perfected by Minsky and Papert. Rosenblatt’s model is called as classical perceptron and the model analyzed by Minsky and Papert is called perceptron.Disclaimer: (...)

#neurons #artificial-intelligence #deep-learning #deep-learning-basics

McCulloch Pitts Neuron — Deep Learning Building Blocks
▻https://hackernoon.com/mcculloch-pitts-neuron-deep-learning-building-blocks-7928f4e0504d?source

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McCulloch Pitts Neuron — Deep Learning Building BlockThe fundamental block of deep learning is artificial neuron i.e.. it takes a weighted aggregate of inputs, applies a function and gives an output. The very first step towards the artificial neuron was taken by Warren McCulloch and Walter Pitts in 1943 inspired by neurobiology, created a model known as McCulloch-Pitts Neuron.Disclaimer: The content and the structure of this article is based on the deep learning lectures from One-Fourth Labs — Padhai.Motivation — Biological NeuronThe inspiration for the creation of an artificial neuron comes from the biological neuron.Fig — 1 Biological Neuron — Padhai Deep LearningIn a simplistic view, #neurons receive signals and produce a response. The general structure of a neuron is shown in the Fig-1. Dendrites (...)

#artificial-intelligence #mcculloch-pitts-neuron #deep-learning #machine-learning

Interview with #kaggle Grandmaster, Lead Data Scientist at Dbrain: Artur Kunzin
▻https://hackernoon.com/interview-with-kaggle-grandmaster-lead-data-scientist-at-dbrain-artur-ku

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Interview with Kaggle Grandmaster, Head of Computer Vision at X5 Retail Group: Artur KuzinPart 22 of The series where I interview my heroes.Today I’m honored to be interviewing a Kaggle Grandmaster from the ods.ai community.I’m excited to be talking to Competitions GrandMaster (Ranked #29, kaggle: @n01z3) and Kernels (Ranked #159), Discussions Expert: (Ranked #58),: Artur KuzinArtur has a background in Physics and Applied Math with a Masters Degree. Currently, he is working as the Head of Computer Vision at X5 Retail Group (Largest multi-format retailer in Russia), before X5 Group- he has worked as Lead Data Scientist at Dbrain (Dbrain.io), and as a Data Scientist at Avito (the second largest classifieds site in the world, part OLX group).About the Series:I have very recently started making (...)

#artificial-intelligence #data-science #deep-learning #machine-learning

Can #blockchain with Artificial Intelligence Fight Deep Fake?
▻https://hackernoon.com/can-blockchain-with-artificial-intelligence-fight-deep-fake-9b899b4d45e7

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Truth has been the subject of discussion in its own rights, objectively and independently of the ways we think about it or describe it, for many ages. Philosophical theories about truth may have many relative grounds but in mathematics there exist absolute truth.Can truth shapeshift? In an emotion based market, truth is subjective to the intellectual spectrum of people’s belief and opinion. The deepfake video of Barack Obama’s speech created by BuzzFeed using power face swapping neural networking technology is one such example.▻https://medium.com/media/d1eb9049368b3a8bf7a4dd9b5a92a8c2/hrefSo what is a deep fake?“Deepfake, a portmanteau of “deep learning” and “fake”,[1] is an artificial intelligence-based human image synthesis technique. It is used to combine and superimpose existing images and (...)

#machine-learning #artificial-intelligence #deep-learning #venture-capital

Transfer Learning : Approaches and Empirical Observations
▻https://hackernoon.com/transfer-learning-approaches-and-empirical-observations-efeff9dfeca6?sou

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Transfer Learning : Approaches and Empirical InsightsIf data is currency, then transfer learning is a messiah for the poors▻https://medium.com/media/868569aa242986fcdf8e6551d15f791e/hrefWhile there is no dearth of learning resources on this topic, only a few of them could couple the theoretical and empirical parts together and be intuitive enough. The reason ?? I guess we don’t transfer the knowledge in the exact way we store it in our minds. I believe that presenting complex topics in simple ways is an art, so lets master it.Lets begin a series of blogs where we will try to discuss why the things are the way they are and the intuitions behind them in the domain of machine learning. The first in the line is Transfer Learning (TL). We will begin with a crisp intro about TL followed by (...)

#deep-learning #data-science #transfer-learning #tensorflow #machine-learning