How to Use Google Colab for Deep Learning and Machine Learning

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Google Colaboratory is a free online cloud-based Jupyter notebook environment that allows us to train our machine learning and deep learning models on CPUs, GPUs, and TPUs. Here's what I truly love about Colab. It does not matter which computer you have, what it's configuration is, and how ancient it might be. You can still use Google Colab! All you need is a Google account and a web browser.


Data Science Fails: Building AI You Can Trust

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The game-changing potential of artificial intelligence (AI) and machine learning is well-documented. AI has the power to transform countless industries -- including the healthcare, banking, insurance, and public service sectors, to name just a few -- by introducing new efficiencies and revealing new opportunities for companies to solve problems. Any organization that is considering adopting AI at their organization must first be willing to trust in AI technology. Organizations must feel confident that human error did not inadvertently contribute to AI bias that resulted in inaccurate or misleading findings. The new DataRobot whitepaper, Data Science Fails: Building AI You Can Trust, outlines six common important lessons that organizations must understand to follow best data science practices and ensure that AI is being implemented successfully.


AI Most Promising Open Source Projects in 2020

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Are you ready to start your new AI project? In the below map, you'll find a curated list of the most advanced and innovative open source projects to be considered in you data science initiative for 2020. Drop a comment below or submit a pull-request here, if you believe a relevant project was left behind. AllenNLP – An open-source NLP research library, built on PyTorch. Zeppelin – Web-based notebook that enables data-driven, interactive data analytics.


r/datascience - :)

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I know this is a joke, but are there really people out there using linear regression and calling it "machine learning"? Edit: y'all are right that linear regression is certainly a form of statistical learning. I think the main reason I instinctively think of it as being "not machine learning" is that it has a simple closed-form solution--so the "machine" part of statistical learning with linear regression is unnecessary. The same can't be said for a lot of other algorithms that are more comfortably referred to as "machine learning", you know?


AlphaGo - The Movie Full Documentary

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With more board configurations than there are atoms in the universe, the ancient Chinese game of Go has long been considered a grand challenge for artificial intelligence. On March 9, 2016, the worlds of Go and artificial intelligence collided in South Korea for an extraordinary best-of-five-game competition, coined The DeepMind Challenge Match. Hundreds of millions of people around the world watched as a legendary Go master took on an unproven AI challenger for the first time in history. Directed by Greg Kohs with an original score by Academy Award nominee, Hauschka, AlphaGo chronicles a journey from the halls of Oxford, through the backstreets of Bordeaux, past the coding terminals of DeepMind in London, and ultimately, to the seven-day tournament in Seoul. As the drama unfolds, more questions emerge: What can artificial intelligence reveal about a 3000-year-old game?


Join a webinar with Pam Didner: How to leverage Artificial Intelligence (AI) for Sales and Marketing. She shares practical AI exmaples and she makes it FUN. Come find out yourself.

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Pam Didner will share practical examples of how to use AI for your sales and marketing. And she will make it FUN! 11 am PST / 2 pm EST, April 9, 2020 You'll learn: - What is AI? What are the benefits of AI? - How can you evaluate AI for Marketing and Sales? - What are practical examples of AI applications that you need to know? At the end of this webinar, you won't be intimidated by AI anymore. You'll have ideas on how to apply AI to your marketing or sales jobs.


Updated: This AI camera detects people who may have COVID-19

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IPVM also points out that it's unusual for a surveillance tech company to be selling high-end thermal cameras and software via an e-commerce site with a "shopping cart," which Athena continues to do. Athena CEO Lisa Falzone told Fast Company they took that approach so that customers wouldn't have to wait for weeks to get the technology. IPVM demonstrated that the price of Athena's "Coronavirus Detection System" shown on the website has risen from $3,900 as of March 17 to $8,900 on March 23. Athena CTO Chris Ciabarra says the price of the software-hardware solution can fluctuate with the prices and availability of the thermal cameras used. With the U.S. lagging other countries in the distribution of coronavirus testing kits, health authorities have had to look to other means of detection, like the infrared ear thermometers used in some countries.


A Top Machine Learning Algorithm Explained: Support Vector Machines (SVM) - KDnuggets

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By Clare Liu, Data Scientist at fintech industry, based in HK. One of the most prevailing and exciting supervised learning models with associated learning algorithms that analyse data and recognise patterns is Support Vector Machines (SVMs). It is used for solving both regression and classification problems. However, it is mostly used in solving classification problems. SVMs were first introduced by B.E. Boser et al. in 1992 and has become popular due to success in handwritten digit recognition in 1994.


12 Thought-Provoking Quotes About Artificial Intelligence

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Alan Turing (1912-1954) was one of the first thinkers to take the concept of artificial intelligence ... [ ] seriously. His pioneering work laid the foundation for the fields of digital computing and AI as we know them today. In the most direct sense, artificial intelligence is an engineering challenge. The mathematics underlying today's cutting-edge AI algorithms is complex. The amount of computing resources required to train state-of-the-art AI models is formidable.


AI, facial recognition, and ethics: Will this new tech invade public privacy?

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We live in an age where we have unprecedented access to almost any information we need. With the emergence of new technology like artificial intelligence (AI), facial recognition, big data and more, the human experience is being changed forever. Almost anything you need is just a tap away; but this access comes at a price--data for data. A simple online search may seem harmless, but before you know it, you're being bombarded with ads offering you exactly what you were looking for. How exactly does this work?