Browse the State-of-the-Art in Machine Learning
▻http://paperswithcode.com/sota
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How I built my first Machine Learning Software-As-A-Service
▻https://hackernoon.com/how-i-built-my-first-machine-learning-software-as-a-service-a726080f566aHow I Built My First Machine Learning Software-As-A-ServiceAnd guess what? It didn’t need any funding. But it did need humans (and still does)My first payment from a real customer finally cleared. Product Pix just made $65.36. I feel like I can finally write a bit about my journey so far in building a #saas (software as a service). I can finally tell for sure that someone out there thinks my site is generating real value.And the site still doesn’t have any payment or subscription page! The transaction arose from the client reaching out to me and asking how much the service costs, because they needed to use my service at scale.That’s a good sign, right? So elegant. Here I am, a machine learning consultant, writing some code on my spare time, and I’m getting people to pay me for using it. Sheer (...)
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Deep Learning vs. Machine Learning: A Simple Explanation
▻https://hackernoon.com/deep-learning-vs-machine-learning-a-simple-explanation-47405b3eef08?sourMachine 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
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How to make a simple Machine Learning #website from scratch
▻https://hackernoon.com/how-to-make-a-simple-machine-learning-website-from-scratch-1ae4756c8b04?Learn to make a simple machine learning website which adapts text colour ? according to changeable background contrast.Are you enthusiastic about machine learning? Are you trying to implement a simple machine learning webpage from scratch?Do you want to make something cool with HTML/CSS and #javascript?If yes, then you’re at the perfect place.Machine Learning, AIHere we will be making a simple webpage that changes the text colour according to the background color, using a simple machine learning library brain.js.Let’s kick off our excitement with the result itself.Here is the link to the webpage.Go to the above link and try changing the RGB values of the slider, you’d notice the auto changing text colour with respect to the background contrast.This is what we would be building here.What? You (...)
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Thinking of Self-Studying Machine Learning? Remind yourself of these 6 things
▻https://hackernoon.com/thinking-of-self-studying-machine-learning-remind-yourself-of-these-6-thI’m a self-taught¹ Machine Learning Engineer, here’s what I’d tell myself if I started againWhere most of my self-study takes place. Photo from: Daniel Bourke on YouTube.We were hosting a Meetup on robotics in Australia and it was question time.Someone asked a question.“How do I get into artificial intelligence and machine learning from a different background?”Nick turned and called my name.“Where’s Dan Bourke?”I was backstage and talking to Alex. I walked over.“Here he is,” Nick continued, “Dan comes from a health science background, he studied nutrition, then drove Uber, learned machine learning online and has now been with Max Kelsen as a machine learning engineer for going on a year.”Nick is the CEO and Co-founder of Max Kelsen, a technology company in Brisbane.I stood and kept listening.“He has (...)
#data-science #machine-learning #online-learning #machine-learning-course #study-machine-learning
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Enterprise™ AF Solution for Text Classification (using BERT)
▻https://hackernoon.com/enterprise-af-solution-for-text-classification-using-bert-9fe2b7234c46?sWhat is BERT? How does one use BERT to solve problems? Google Colab, #tensorflow, #kubernetes on Google CloudOverviewThis for people who want to create a REST service using a model built with BERT, the best NLP base model available. I spent a lot of time figuring out how to put a solution together so I figured I would write up how to deploy a solution and share!Why should you read this?Today we have machine learning engineers, software engineers, and data scientists. The trend in deep learning is that models are getting so powerful that there is little need to know about the details of the specific algorithm and can be immediately applied to custom use cases. This trend will turn the job of machine learning engineers into a skill that software engineers have. There will still be data (...)
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Transfer Learning : Approaches and Empirical Observations
▻https://hackernoon.com/transfer-learning-approaches-and-empirical-observations-efeff9dfeca6?souTransfer 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
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Artificial Interior Designers: How #ai is helping scientists study cultures around the world
▻https://hackernoon.com/artificial-interior-designers-how-ai-is-helping-scientists-study-culturePeople trained computers using a programming technique called transfer learning. People first pick out objects in rooms — and then the computers could do the work on their own. Credit: Penn StateArtificial intelligence helped a team of researchers analyze about a million images of living rooms all over the world to better understand how different cultures decorate their homes.According to a Penn State news release, the researchers used a type of machine learning, called transfer learning, to pick out wall colors, wall #art, books, and other beautifying techniques to see how people personalized their living rooms — about 50,000 living rooms, as a matter of fact. The living room pictures were collected from the Airbnb website, a place where people market their rental homes and rooms.“We were (...)
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2018 Stealth Capital Report: Tracking Hidden Billions
▻https://hackernoon.com/2018-stealth-capital-report-tracking-hidden-billions-39744c1f1726?sourceIt should come as no surprise that investment in tech startups is booming. Early stage startups are on-pace to reach over $8 billion dollars of funding this year in 2018, while late stage startups are receiving over $70 billion.But what does this mean for startups in stealth? If you haven’t heard of a startup, does it even really exist? Are credible startups with real venture dollars remaining in stealth for longer as they build their technology?What we’ve seen at Brex is that the stealth startup economy is thriving. Brex has identified over $1.43B in stealth capital outstanding, with the vast majority raised in the last two years. These stealth startups make up 3–5% of the overall startup market. And some of the most high profile startups in Machine Learning, Autonomous Driving, and (...)
#stealth-capital #stealth-vc #venture-capital #stealth-capital-report #tracking-hidden-billions
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Some State of the Art Optimizers in Neural Networks
▻https://hackernoon.com/some-state-of-the-art-optimizers-in-neural-networks-a3c2ba5a5643?source=We are going to study Momentum, Nesterov Accelerated Momentum, AdaGrad, AdaDelta, RMSProp, Adam, AdaMax, AMSGrad.Optimization is the heart of Machine learningBefore begin on our topic, let’s understand..Why do we need Optimization?According to Merriam-Webster dictionary, meaning of the word optimize is “to make as perfect, effective, or functional as possible”. This is the definition to understand why we need Optimization in neural networks.So in machine learning, we perform optimization on the training data and check its performance on a new validation data.We already have a cost function which will tell us about the behavior of our model. Initially, our model contains arbitrary defined parameters like weights and biases and now we need to find the best possible state of those parameters (...)
#optimization-algorithms #data-science #gradient-descent #neural-networks #machine-learning
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New report exposes global reach of powerful governments who equip, finance and train other countries to spy on their populations
Privacy International has today released a report that looks at how powerful governments are financing, training and equipping countries — including authoritarian regimes — with surveillance capabilities. The report warns that rather than increasing security, this is entrenching authoritarianism.
Countries with powerful security agencies are spending literally billions to equip, finance, and train security and surveillance agencies around the world — including authoritarian regimes. This is resulting in entrenched authoritarianism, further facilitation of abuse against people, and diversion of resources from long-term development programmes.
The report, titled ‘Teach ’em to Phish: State Sponsors of Surveillance’ is available to download here.
Examples from the report include:
In 2001, the US spent $5.7 billion in security aid. In 2017 it spent over $20 billion [1]. In 2015, military and non-military security assistance in the US amounted to an estimated 35% of its entire foreign aid expenditure [2]. The report provides examples of how US Departments of State, Defense, and Justice all facilitate foreign countries’ surveillance capabilities, as well as an overview of how large arms companies have embedded themselves into such programmes, including at surveillance training bases in the US. Examples provided include how these agencies have provided communications intercept and other surveillance technology, how they fund wiretapping programmes, and how they train foreign spy agencies in surveillance techniques around the world.
The EU and individual European countries are sponsoring surveillance globally. The EU is already spending billions developing border control and surveillance capabilities in foreign countries to deter migration to Europe. For example, the EU is supporting Sudan’s leader with tens of millions of Euros aimed at capacity building for border management. The EU is now looking to massively increase its expenditure aimed at building border control and surveillance capabilities globally under the forthcoming Multiannual Financial Framework, which will determine its budget for 2021–2027. Other EU projects include developing the surveillance capabilities of security agencies in Tunisia, Burkina Faso, Somalia, Iraq and elsewhere. European countries such as France, Germany, and the UK are sponsoring surveillance worldwide, for example, providing training and equipment to “Cyber Police Officers” in Ukraine, as well as to agencies in Saudi Arabia, and across Africa.
Surveillance capabilities are also being supported by China’s government under the ‘Belt and Road Initiative’ and other efforts to expand into international markets. Chinese companies have reportedly supplied surveillance capabilities to Bolivia, Venezuela, and Ecuador [3]. In Ecuador, China Electronics Corporation supplied a network of cameras — including some fitted with facial recognition capabilities — to the country’s 24 provinces, as well as a system to locate and identify mobile phones.Edin Omanovic, Privacy International’s Surveillance Programme Lead, said
“The global rush to make sure that surveillance is as universal and pervasive as possible is as astonishing as it is disturbing. The breadth of institutions, countries, agencies, and arms companies that are involved shows how there is no real long-term policy or strategic thinking driving any of this. It’s a free-for-all, where capabilities developed by some of the world’s most powerful spy agencies are being thrown at anyone willing to serve their interests, including dictators and killers whose only goal is to cling to power.
“If these ‘benefactor’ countries truly want to assist other countries to be secure and stable, they should build schools, hospitals, and other infrastructure, and promote democracy and human rights. This is what communities need for safety, security, and prosperity. What we don’t need is powerful and wealthy countries giving money to arms companies to build border control and surveillance infrastructure. This only serves the interests of those powerful, wealthy countries. As our report shows, instead of putting resources into long-term development solutions, such programmes further entrench authoritarianism and spur abuses around the world — the very things which cause insecurity in the first place.”
▻https://privacyinternational.org/press-release/2161/press-release-new-report-exposes-global-reach-powerful-governm
#surveillance #surveillance_de_masse #rapport
Pour télécharger le rapport “Teach ’em to Phish: State Sponsors of Surveillance”:
▻https://privacyinternational.org/sites/default/files/2018-07/Teach-em-to-Phish-report.pdfping @fil
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China Uses DNA to Track Its People, With the Help of American Expertise
The Chinese authorities turned to a Massachusetts company and a prominent Yale researcher as they built an enormous system of surveillance and control.
The authorities called it a free health check. Tahir Imin had his doubts.
They drew blood from the 38-year-old Muslim, scanned his face, recorded his voice and took his fingerprints. They didn’t bother to check his heart or kidneys, and they rebuffed his request to see the results.
“They said, ‘You don’t have the right to ask about this,’” Mr. Imin said. “‘If you want to ask more,’ they said, ‘you can go to the police.’”
Mr. Imin was one of millions of people caught up in a vast Chinese campaign of surveillance and oppression. To give it teeth, the Chinese authorities are collecting DNA — and they got unlikely corporate and academic help from the United States to do it.
China wants to make the country’s Uighurs, a predominantly Muslim ethnic group, more subservient to the Communist Party. It has detained up to a million people in what China calls “re-education” camps, drawing condemnation from human rights groups and a threat of sanctions from the Trump administration.
Collecting genetic material is a key part of China’s campaign, according to human rights groups and Uighur activists. They say a comprehensive DNA database could be used to chase down any Uighurs who resist conforming to the campaign.
Police forces in the United States and elsewhere use genetic material from family members to find suspects and solve crimes. Chinese officials, who are building a broad nationwide database of DNA samples, have cited the crime-fighting benefits of China’s own genetic studies.
To bolster their DNA capabilities, scientists affiliated with China’s police used equipment made by Thermo Fisher, a Massachusetts company. For comparison with Uighur DNA, they also relied on genetic material from people around the world that was provided by #Kenneth_Kidd, a prominent #Yale_University geneticist.
On Wednesday, #Thermo_Fisher said it would no longer sell its equipment in Xinjiang, the part of China where the campaign to track Uighurs is mostly taking place. The company said separately in an earlier statement to The New York Times that it was working with American officials to figure out how its technology was being used.
Dr. Kidd said he had been unaware of how his material and know-how were being used. He said he believed Chinese scientists were acting within scientific norms that require informed consent by DNA donors.
China’s campaign poses a direct challenge to the scientific community and the way it makes cutting-edge knowledge publicly available. The campaign relies in part on public DNA databases and commercial technology, much of it made or managed in the United States. In turn, Chinese scientists have contributed Uighur DNA samples to a global database, potentially violating scientific norms of consent.
Cooperation from the global scientific community “legitimizes this type of genetic surveillance,” said Mark Munsterhjelm, an assistant professor at the University of Windsor in Ontario who has closely tracked the use of American technology in Xinjiang.
Swabbing Millions
In Xinjiang, in northwestern China, the program was known as “#Physicals_for_All.”
From 2016 to 2017, nearly 36 million people took part in it, according to Xinhua, China’s official news agency. The authorities collected DNA samples, images of irises and other personal data, according to Uighurs and human rights groups. It is unclear whether some residents participated more than once — Xinjiang has a population of about 24.5 million.
In a statement, the Xinjiang government denied that it collects DNA samples as part of the free medical checkups. It said the DNA machines that were bought by the Xinjiang authorities were for “internal use.”
China has for decades maintained an iron grip in Xinjiang. In recent years, it has blamed Uighurs for a series of terrorist attacks in Xinjiang and elsewhere in China, including a 2013 incident in which a driver struck two people in Tiananmen Square in Beijing.
In late 2016, the Communist Party embarked on a campaign to turn the Uighurs and other largely Muslim minority groups into loyal supporters. The government locked up hundreds of thousands of them in what it called job training camps, touted as a way to escape poverty, backwardness and radical Islam. It also began to take DNA samples.
In at least some of the cases, people didn’t give up their genetic material voluntarily. To mobilize Uighurs for the free medical checkups, police and local cadres called or sent them text messages, telling them the checkups were required, according to Uighurs interviewed by The Times.
“There was a pretty strong coercive element to it,” said Darren Byler, an anthropologist at the University of Washington who studies the plight of the Uighurs. “They had no choice.”
Calling Dr. Kidd
Kenneth Kidd first visited China in 1981 and remained curious about the country. So when he received an invitation in 2010 for an expenses-paid trip to visit Beijing, he said yes.
Dr. Kidd is a major figure in the genetics field. The 77-year-old Yale professor has helped to make DNA evidence more acceptable in American courts.
His Chinese hosts had their own background in law enforcement. They were scientists from the Ministry of Public Security — essentially, China’s police.
During that trip, Dr. Kidd met Li Caixia, the chief forensic physician of the ministry’s Institute of Forensic Science. The relationship deepened. In December 2014, Dr. Li arrived at Dr. Kidd’s lab for an 11-month stint. She took some DNA samples back to China.
“I had thought we were sharing samples for collaborative research,” said Dr. Kidd.
Dr. Kidd is not the only prominent foreign geneticist to have worked with the Chinese authorities. Bruce Budowle, a professor at the University of North Texas, says in his online biography that he “has served or is serving” as a member of an academic committee at the ministry’s Institute of Forensic Science.
Jeff Carlton, a university spokesman, said in a statement that Professor Budowle’s role with the ministry was “only symbolic in nature” and that he had “done no work on its behalf.”
“Dr. Budowle and his team abhor the use of DNA technology to persecute ethnic or religious groups,” Mr. Carlton said in the statement. “Their work focuses on criminal investigations and combating human trafficking to serve humanity.”
Dr. Kidd’s data became part of China’s DNA drive.
In 2014, ministry researchers published a paper describing a way for scientists to tell one ethnic group from another. It cited, as an example, the ability to distinguish Uighurs from Indians. The authors said they used 40 DNA samples taken from Uighurs in China and samples from other ethnic groups from Dr. Kidd’s Yale lab.
In patent applications filed in China in 2013 and 2017, ministry researchers described ways to sort people by ethnicity by screening their genetic makeup. They took genetic material from Uighurs and compared it with DNA from other ethnic groups. In the 2017 filing, researchers explained that their system would help in “inferring the geographical origin from the DNA of suspects at crime scenes.”
For outside comparisons, they used DNA samples provided by Dr. Kidd’s lab, the 2017 filing said. They also used samples from the 1000 Genomes Project, a public catalog of genes from around the world.
Paul Flicek, member of the steering committee of the 1000 Genomes Project, said that its data was unrestricted and that “there is no obvious problem” if it was being used as a way to determine where a DNA sample came from.
The data flow also went the other way.
Chinese government researchers contributed the data of 2,143 Uighurs to the Allele Frequency Database, an online search platform run by Dr. Kidd that was partly funded by the United States Department of Justice until last year. The database, known as Alfred, contains DNA data from more than 700 populations around the world.
This sharing of data could violate scientific norms of informed consent because it is not clear whether the Uighurs volunteered their DNA samples to the Chinese authorities, said Arthur Caplan, the founding head of the division of medical ethics at New York University’s School of Medicine. He said that “no one should be in a database without express consent.”
“Honestly, there’s been a kind of naïveté on the part of American scientists presuming that other people will follow the same rules and standards wherever they come from,” Dr. Caplan said.
Dr. Kidd said he was “not particularly happy” that the ministry had cited him in its patents, saying his data shouldn’t be used in ways that could allow people or institutions to potentially profit from it. If the Chinese authorities used data they got from their earlier collaborations with him, he added, there is little he can do to stop them.
He said he was unaware of the filings until he was contacted by The Times.
Dr. Kidd also said he considered his collaboration with the ministry to be no different from his work with police and forensics labs elsewhere. He said governments should have access to data about minorities, not just the dominant ethnic group, in order to have an accurate picture of the whole population.
As for the consent issue, he said the burden of meeting that standard lay with the Chinese researchers, though he said reports about what Uighurs are subjected to in China raised some difficult questions.
“I would assume they had appropriate informed consent on the samples,” he said, “though I must say what I’ve been hearing in the news recently about the treatment of the Uighurs raises concerns.”
Machine LearningIn 2015, Dr. Kidd and Dr. Budowle spoke at a genomics conference in the Chinese city of Xi’an. It was underwritten in part by Thermo Fisher, a company that has come under intense criticism for its equipment sales in China, and Illumina, a San Diego company that makes gene sequencing instruments. Illumina did not respond to requests for comment.
China is ramping up spending on health care and research. The Chinese market for gene-sequencing equipment and other technologies was worth $1 billion in 2017 and could more than double in five years, according to CCID Consulting, a research firm. But the Chinese market is loosely regulated, and it isn’t always clear where the equipment goes or to what uses it is put.
Thermo Fisher sells everything from lab instruments to forensic DNA testing kits to DNA mapping machines, which help scientists decipher a person’s ethnicity and identify diseases to which he or she is particularly vulnerable. China accounted for 10 percent of Thermo Fisher’s $20.9 billion in revenue, according to the company’s 2017 annual report, and it employs nearly 5,000 people there.
“Our greatest success story in emerging markets continues to be China,” it said in the report.
China used Thermo Fisher’s equipment to map the genes of its people, according to five Ministry of Public Security patent filings.
The company has also sold equipment directly to the authorities in Xinjiang, where the campaign to control the Uighurs has been most intense. At least some of the equipment was intended for use by the police, according to procurement documents. The authorities there said in the documents that the machines were important for DNA inspections in criminal cases and had “no substitutes in China.”
In February 2013, six ministry researchers credited Thermo Fisher’s Applied Biosystems brand, as well as other companies, with helping to analyze the DNA samples of Han, Uighur and Tibetan people in China, according to a patent filing. The researchers said understanding how to differentiate between such DNA samples was necessary for fighting terrorism “because these cases were becoming more difficult to crack.”
The researchers said they had obtained 95 Uighur DNA samples, some of which were given to them by the police. Other samples were provided by Uighurs voluntarily, they said.
Thermo Fisher was criticized by Senator Marco Rubio, Republican of Florida, and others who asked the Commerce Department to prohibit American companies from selling technology to China that could be used for purposes of surveillance and tracking.
On Wednesday, Thermo Fisher said it would stop selling its equipment in Xinjiang, a decision it said was “consistent with Thermo Fisher’s values, ethics code and policies.”
“As the world leader in serving science, we recognize the importance of considering how our products and services are used — or may be used — by our customers,” it said.
Human rights groups praised Thermo Fisher’s move. Still, they said, equipment and information flows into China should be better monitored, to make sure the authorities elsewhere don’t send them to Xinjiang.
“It’s an important step, and one hopes that they apply the language in their own statement to commercial activity across China, and that other companies are assessing their sales and operations, especially in Xinjiang,” said Sophie Richardson, the China director of Human Rights Watch.
American lawmakers and officials are taking a hard look at the situation in Xinjiang. The Trump administration is considering sanctions against Chinese officials and companies over China’s treatment of the Uighurs.
China’s tracking campaign unnerved people like Tahir Hamut. In May 2017, the police in the city of Urumqi in Xinjiang drew the 49-year-old Uighur’s blood, took his fingerprints, recorded his voice and took a scan of his face. He was called back a month later for what he was told was a free health check at a local clinic.
Mr. Hamut, a filmmaker who is now living in Virginia, said he saw between 20 to 40 Uighurs in line. He said it was absurd to think that such frightened people had consented to submit their DNA.
“No one in this situation, not under this much pressure and facing such personal danger, would agree to give their blood samples for research,” Mr. Hamut said. “It’s just inconceivable.”
▻https://www.nytimes.com/2019/02/21/business/china-xinjiang-uighur-dna-thermo-fisher.html?action=click&module=MoreInSect
#USA #Etats-Unis #ADN #DNA #Ouïghours #université #science #génétique #base_de_données
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What #data #privacy Means for the Future of #blockchain
▻https://hackernoon.com/what-data-privacy-means-for-the-future-of-blockchain-c0212cd16680?sourceData Analytics and BlockchainData analytics and machine learning are hugely valuable, providing insights and spurring advancements in many industries including IoT, healthcare, and financial services.Today’s blockchain platforms cannot directly support applications that compute over sensitive data.Unfortunately, the data that powers these advancements is often highly sensitive. For example, medical research requires access to sensitive patient data. In many cases this data cannot be accessed or shared due to privacy concerns. This results in data silos in which data is not used for its full potential value.Blockchain can help solve this problem, though several challenges remain. For example, one would like to use smart contracts to allow researchers to run machine learning over (...)
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Mapping All of the Trees with Machine Learning – descarteslabs-team – Medium
▻https://medium.com/descarteslabs-team/descartes-labs-urban-trees-tree-canopy-mapping-3b6c85c5c9ccAll this fuss is not without good reason. Trees are great! They make oxygen for breathing, suck up CO₂, provide shade, reduce noise pollution, and just look at them — they’re beautiful!
8th Street in Park Slope, Brooklyn last May. Look at those beautiful trees!The thing is, though, that trees are pretty hard to map. The 124,795 trees in the San Francisco Urban Forest Map shown below, for example, were cataloged over a year of survey work by a team of certified arborists. The database they created is thorough, with information on tree species and size as well as environmental factors like the presence of power lines or broken pavement.
But surveys like this are expensive to conduct, difficult to maintain, and provide an incomplete picture of the entire extent of the urban tree canopy. Both the San Francisco inventory below and the New York City TreesCount! do an impeccable job mapping the location, size and health of street trees, but exclude large chunks within the cities, like parks.
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How It Feels to Learn #data Science in 2019
▻https://hackernoon.com/how-it-feels-to-learn-data-science-in-2019-50a7200f4129?source=rss----3aSeeing the (Random) Forest Through the (Decision) TreesThe following was inspired by the Hacker Noon article How it Feels to Learn JavaScript in 2016 and was originally published on Towards Data Science. Do not take this article too seriously. This is satire so do not treat it as actual advice. Like all advice, some of it is good and some of it is terrible. This piece is just an opinion, much like people’s definition of data science.I heard you are the one to go to. Thank you for meeting with me, and thanks for the coffee. You know data science, right?Well, I know of it. I went to PyData and O’Reilly Strata last year and built a few models.Yeah, I heard you gave a great presentation on machine learning to our company last week. My coworker said it was really useful.Oh, the cat and dog (...)
#learn-data-science #artificial-intelligence #data-science #machine-learning
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How Can AI Help Small Businesses ?
▻https://hackernoon.com/how-can-ai-help-small-businesses-e3f6938d384b?source=rss----3a8144eabfe3Hey Siri! Can you help me with my business?Quite recently I got into a discussion with my marketing manager, a tall and burly mid-age man; Let’s call him Steve.Steve and I got into a discussion on the subject of how AI in general, is transforming the way business is conducted.This man, a well-educated gentleman, only partially agreed with my perspective,He added,“I acknowledge technology has made quite an impact, but this impact is austerely restricted. My domain of work, or for that matter most small businesses haven’t observed any major transformation even after the recent developments in AI,”He continued,“It is predominantly large organizations that have profited from new-age technologies like AI and Machine learning.”This got me wondering…Is the AI use-case still confined to big & (...)
#small-business #machine-learning #artificial-intelligence #ai-small-business #chatbots
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#python #bootcamp For ML
▻https://hackernoon.com/python-bootcamp-for-ml-c321177b957e?source=rss----3a8144eabfe3---4A simple effort from me to make a Python bootcamp 3 Days for beginners who are enthusiastic about Machine Learning.Introduction:Few days ago i think that i can make a bootcamp on python which most needed for machine learning enthusiastic or deep learning enthusiastic or data science enthusiastic.Then i was started this bootcamp. I hope that this bootcamp will be helpful for everyone who’s want to work in Data Science field or Machine learning field.Data Science.AI CommunityLet’s start learning with us!!Photo on UnsplashPython Basic(Day-01)Data Science.AI CommunityWe are going to get familiar with Python. In Day-01 we was covered the basic portion of Python which can be very useful for Machine Learning or Data Science.What is python?Python is developed by Guido van Rossum. Guido van Rossum (...)
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Interview with Radiologist, fast.ai fellow and Kaggle expert: Dr. Alexandre Cadrin-Chenevert
▻https://hackernoon.com/interview-with-radiologist-fast-ai-fellow-and-kaggle-expert-dr-alexandrePart 20 of The series where I interview my heroes.Index to “Interviews with ML Heroes”Today, I’m super excited to be interviewing one of the domain experts in #medical Practice: A Radiologist, a great member of the fast.ai community and a kaggle expert: Dr. Alexandre Cadrin-Chenevert.Alexandre is an MD, Radiologist and a Computer Engineer. He is also a Deep Learning Practitioner, Kaggle Competition Expert (Ranked #72). He is actively working in the application of Deep Learning in the Medical Domain.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 community online and the great people that have helped me.In this Series of Blog Posts, I talk with People that have (...)
#deep-learning #computer-vision #machine-learning #artificial-intelligence
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3 Ways #blockchain Could Unleash the Full Potential of Machine Learning
▻https://hackernoon.com/3-ways-blockchain-will-unleash-the-full-potential-of-machine-learning-3dWe are currently living in the midst of a Artificial Intelligence revolution with new research and applications coming to fruition. Advancements in AI technology, like deep learning, have changed everything from the recommendations on your Netflix account to how doctors are treating patients before they become ill. It seems like every day there are new developments and new tasks being accomplished by machines previously done by humans. Not too long ago, A.I. seemed like something only part of a science fiction novel. Today, AI is all around us.Even with all of these current exciting applications of AI, many more will soon be realized in the near future. However, the advent of this new technology also brings unrealistically high expectations. The latest example, machine learning. It’s (...)
#data-science #machine-learning #bitcoin #artificial-intelligence
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Principal Component Analysis — Unsupervised Learning Model
▻https://hackernoon.com/principal-component-analysis-unsupervised-learning-model-8f18c7683262?soPrincipal Component Analysis — Unsupervised Learning ModelLearn how to train and evaluate an unsupervised machine learning model — principal component analysis in this article by Jillur Quddus, a lead technical architect, polyglot software engineer and data scientist.There are numerous real-world use cases, where the number of features available, which may potentially be used to train a model, is very large. A common example is economic data and using its constituents, stock price data, employment data, banking data, industrial data, and housing data together to predict the gross domestic product (GDP). Such types of data are said to have high dimensionality. Though they offer numerous features that can be used to model a given use case, high-dimensional datasets increase the computational (...)
#machine-learning #apache-spark #component-analysis #principal-component #unsupervised-learning
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#open-source Frameworks for Creating Machine Learning Models
▻https://hackernoon.com/open-source-frameworks-for-creating-machine-learning-models-955e975da4ccOpen-Source Machine Learning FrameworksWith the rise of artificial intelligence (AI), the demand for machine learning capabilities has increased dramatically. A vast array of industries from finance to health are seeing an uptake of machine learning-based #technology. Yet, defining machine learning models remains a complex and resource-intensive endeavour for most businesses and organizations. The challenges can be reduced with the help of a good machine learning framework.Below is a list of some of the best open-source frameworks and libraries that businesses and individuals can use to build machine learning models.Amazon Machine LearningAmazon Machine Learning provides tools and wizards for developing machine learning models. AML makes machine learning more accessible to developers by (...)
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Building #python Data Science Container using #docker
▻https://hackernoon.com/building-python-data-science-container-using-docker-c8e346295669?source=Photo by Bryan Goff on UnsplashTL;DRArtificial Intelligence(AI) and Machine Learning(ML) are literally on fire these days. Powering a wide spectrum of use-cases ranging from self-driving cars to drug discovery and to God knows what. AI and ML have a bright and thriving future ahead of them.On the other hand, Docker revolutionized the computing world through the introduction of ephemeral lightweight containers. Containers basically package all the software required to run inside an image(a bunch of read-only layers) with a COW(Copy On Write) layer to persist the data.Enough talk let’s get started with building a Python data science container.Python Data Science PackagesOur Python data science container makes use of the following super cool python packages:NumPy: NumPy or Numeric Python (...)
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Founder Interviews: David Kofoed Wind of Peergrade
▻https://hackernoon.com/founder-interviews-david-kofoed-wind-of-peergrade-de9dff229197?source=rsLearn how David and the Peergrade team found their first customers by manually reaching out to potential users one by one, eventually scaling their peer grading tool to over 7000 institutions.Davis Baer: What’s your background, and what are you working on?My name is David Kofoed Wind. I have a degree in applied math and computer science from The Technical University of Denmark and a Ph.D. in machine learning and learning analytics from the same place. I taught myself programming at a very young age in order to build games and worked as a software developer up through high school.Peergrade is an educational tool that increases critical thinking and collaborative learning through peer to peer feedback. Teachers set up peer feedback sessions on Peergrade, students submit their work and then (...)
#education-startup #education #founder-stories #startup-founders #davis-baer
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The Perceptron
▻http://constantvzw.org/site/The-Perceptron.htmlIn machine learning, the perceptron is an #Algorithm for supervised learning of binary classifiers. A binary classifier is a model which can decide whether an input belongs to some specific class. Neural Networks work the same way as the perceptron. Perceptron is a single layer neural network and a multi-layer perceptron is called Neural Networks. During this session we will perform the Perceptron physically as a game. Afterwards, we will look at the code. For those wishing to experiment (...)
/ #Workshop, #Netnative_literature, Algorithm
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How we used #ai to hybridize humans with cartoon animals and made a business out of it.
▻https://hackernoon.com/how-we-used-ai-to-hybridize-humans-with-cartoon-animals-and-made-a-businHave you ever imagined yourself as a cartoon character? Well, now this is more than real.We are a team of 20 engineers and art designers who have developed a machine learning technology that morphs human faces with animated characters.The process starts by constructing a user’s 3D face model from just a single selfie shot. Importantly, our technology even works with older, regular smartphone cameras. With this single photo, our neural network builds a 3D mesh of the user’s head that looks like this:The neural network regresses a 3D model from a 2D photoNext, 3 other neural networks swing into action. The first draws eyebrows, the second detects and matches eye color, and the third detects and draws glasses if the user is wearing them. When these elements are ready, we morph the user with (...)
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Machine Learning #notes 2
▻https://hackernoon.com/machine-learning-notes-2-c0fe5a841c54?source=rss----3a8144eabfe3---4From Machine Learning -Tom M. MitchellMachine Learning is at the forefront of advancements in Artificial Intelligence. It’s moving fast with new research coming out each and every day. This post is in continuation of important concepts and notes right from the basics to advance, from the book Machine Learning, by Tom M. Mitchell.For Machine Learning Notes 1, please click the link below.Machine Learning Notes 1CHAPTER 2: CONCEPT LEARNING AND THE GENERAL-TO-SPECIFIC ORDERING2.1 Concept LearningA problem of searching through a predefined space of potential hypothesis for the hypothesis that best fits the training example.Inferring a boolean-valued function from training examples of its input and output.Inductive Learning HypothesisAny hypothesis found to approximate the target function well (...)
