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A couple of weeks ago, Google CEO Sundar Pichai told an audience at a Recode-sponsored
Shaan Ray
https://towardsdatascience.com/the-emergence-of-artificial-intelligence-3cde7378768e | The Emergence of Artificial Intelligence | [
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Im writing a piece about the evolution of the Trinity Mirror data unit over the last five years, and as a result have been pulling together some of our front-page stories since last January.
David Ottewell
https://towardsdatascience.com/the-evolution-of-data-journalism-1e4c2802bc3d | The evolution of data journalism | [
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In the past few years, weve seen various statistics showing the NBA audience all the new
Benjamin Xiao
https://towardsdatascience.com/the-evolution-of-the-nba-3-point-line-da6700714ad2 | The Evolution of the NBA 3-Point Line | [
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Now it is time to
Eugenio Culurciello
https://towardsdatascience.com/the-fall-of-rnn-lstm-2d1594c74ce0 | The fall of RNN / LSTM | [
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For a while, Ive been on the hunt for what might qualify as the first computer data
Adventures in Data
https://towardsdatascience.com/the-first-computer-visualization-3d00dc8c9aea | Is this the first computer visualization? | [
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In this post, were going to have a look at some data about the tags used on Stack Overflow to label questions, their frequencies and what we can measure around them.
Martina Pugliese
https://towardsdatascience.com/the-frequency-of-tags-on-stack-overflow-2fb47600e2b2 | The frequency of tags on Stack Overflow | [
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Whenever people talk of Artificial Intelligence or AI, they most likely speak
Vladimir Fedak
https://towardsdatascience.com/the-future-of-ai-deep-learning-or-much-more-eb95ed5da487 | The future of AI: Deep Learning or much more? | [
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We are storytellers. We have been telling stories throughout our long history from the ancient times
Frank Trevino
https://towardsdatascience.com/the-future-of-smart-storytelling-be199ac313d0 | The Future of Smart Storytelling | [
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by Aadhar Sharma, Deepak Singh, and Sukant Khurana
Sukant Khurana
https://towardsdatascience.com/the-great-divide-in-ai-450bec3974e9 | The great divide in AI | [
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Structural estimation, the holy grail of applied econometrics, the two words that drive fear into the bravest souls. Nail it as a PhD student, and youre on your way
Vivian Zheng
https://towardsdatascience.com/the-holy-grail-of-econometrics-3a45a2295ce5 | The Holy Grail of Causal Inference | [
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After Indiana Jones recovers the Lost Ark, it is taken by Army Intelligence agents who insist it is being studied by top men.
Thalia Patrinos
https://towardsdatascience.com/the-human-project-21207dda51cf | The Human Project | [
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Analysing 8 Million IoT tweets in Python.
Rafael Schultze-Kraft
https://towardsdatascience.com/the-internet-of-things-on-twitter-e5d6f6f983c0 | The Internet of Thingson Twitter | [
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There is nothing more frustrating than wasted effort
Robert de Graaf
https://towardsdatascience.com/the-lazy-data-scientist-e7d8b0de4ef | The Lazy Data Scientist | [
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Does unemployment benefit make people lazy?
Vivian Zheng
https://towardsdatascience.com/the-lazy-jobless-reality-or-myth-b3adfdc23ffa | The Danger of Eyeball Data Science | [
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Fundamental reasons organizations cannot implement data driven processes
Kristofer Fosmoe
https://towardsdatascience.com/the-limits-of-data-science-b4e5faad20f4 | The Limits of Data Science | [
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About a year ago I created this map of Machine Learning algorithms. If I would create another one I would most likely
Martin Schmitz, PhD
https://towardsdatascience.com/the-map-of-data-science-e54b46e463ff | The Map of Data Science | [
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The wheels of technological revolution turn much slower than they appear
Arnav Gupta
https://towardsdatascience.com/the-marketing-of-technology-93ac20db624f | The Marketing of Technology | [
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Math
JJ
https://towardsdatascience.com/the-metrics-of-meaning-3a23c7fc30e3 | The Metrics of Meaning | [
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Principles to nurture a healthy and innovative Data Science function
Fabien Girardin
https://towardsdatascience.com/the-mindset-for-innovation-with-data-science-fc51605a4867 | The Mindset for Innovation with Data Science | [
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Despite the widespread adoption of AI, scaling and deploying AI-based products is as hard as ever; but some new technology is looking to change that
Justin Gage
https://towardsdatascience.com/the-missing-part-of-the-machine-learning-revolution-91e58b3427ef | The missing part of the Machine Learning revolution | [
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At least since the year2000.
Billy Maddocks
https://towardsdatascience.com/the-most-important-part-of-machine-learning-is-not-the-machine-ee6fd06161af | The most important part of machine learning is not the machine | [
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A quick lesson on these classification differences
Danilo Pena
https://towardsdatascience.com/the-multiclass-definitions-356d2de7ef20 | The Multiclass Definitions | [
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Using recurrent neural networks to generate plausible titles of computer science publications
Allison Morgan
https://towardsdatascience.com/the-netrrobility-is-a-newsigation-of-exactual-%C2%B9-c7a62010b6af | The Netrrobility is a Newsigation of Exactual | [
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How GDPR will change Data Science and challenge companies creativity and growth.
Dwayne Gefferie
https://towardsdatascience.com/the-new-age-of-data-science-725c7b27ac08 | The New Age of Data Science | [
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Everything there is to know.
Akshay Rana
https://towardsdatascience.com/the-non-coders-guide-to-chatbots-fc038f715f40 | The Non-Coders Guide to Chatbots | [
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What variety of background has ignited the diversity of ideas among TED speakers? Continuing from earlier
Hannah Yan Han
https://towardsdatascience.com/the-occupation-of-ted-speakers-4df1829c5ac7 | The Occupation of TED speakers | [
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As a rule, I think theres probably too much emphasis put on data scientists
Carson Forter
https://towardsdatascience.com/the-only-theorem-data-scientists-need-to-know-a50a263d013c | The Only Theorem Data Scientists Need To Know | [
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An analysis of 18,000 Pitchfork reviews
Andrew Thompson
https://towardsdatascience.com/the-order-of-musical-things-4ccdb3450d76 | The Order of Musical Things | [
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Frank Rosenblatt
Arunava
https://towardsdatascience.com/the-perceptron-3af34c84838c | The Perceptron | [
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The Pioneers of Data Visualization
Carolina Bento
https://towardsdatascience.com/the-pioneers-of-data-visualization-ca58b7dc8013 | How Data Visualization was Born | [
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Pricing your product/service is a complicated task to
Outlier AI
https://towardsdatascience.com/the-price-is-right-pricing-strategy-part-1-d4952dc5f5dd | The Price is Right: Pricing StrategyPart 1 | [
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proofs
Anthony Repetto
https://towardsdatascience.com/the-problem-with-back-propagation-13aa84aabd71 | The Problem with Back-Propagation | [
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Back in the 1980s, video games were at a primitive stage. The only games
Dhairya Vayada
https://towardsdatascience.com/the-problem-with-todays-chatbots-47dc40dc6332 | What grocery shopping taught me about chatbots | [
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Developments in AI presents a big potential for audio signal
Daniel Rothmann
https://towardsdatascience.com/the-promise-of-ai-in-audio-processing-a7e4996eb2ca | The promise of AI in audio processing | [
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Remember that one math class you might have taken
Conor Dewey
https://towardsdatascience.com/the-role-of-combinatorics-in-text-classification-b42906a875ca | The Role of Combinatorics in Text Classification | [
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This article is part of a series on the short-term and long term ethical concerns posed by
Adel Nehme
https://towardsdatascience.com/the-short-term-ethical-concerns-of-ai-c201c03bc0ac | The Short-Term Ethical Concerns of AI | [
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Background
Le Nguyen The Dat
https://towardsdatascience.com/the-stages-of-the-data-organization-b3f4f0589716 | The stages of the data organization | [
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The point of this article is to show you what a successful Data Science job hunt looks like
Kristen Kehrer
https://towardsdatascience.com/the-successful-data-science-job-hunt-6131bf80dd75 | The Successful Data Science Job Hunt | [
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Water is a really weird substance. Its one of a few liquids that, when frozen, its density decreases. Thats why
Brian Yahn
https://towardsdatascience.com/the-tip-of-the-tether-56212a8bd48c | The Tip of the Tether | [
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Hi folks, good to see you here again.
Madhav Mishra
https://towardsdatascience.com/the-tools-of-statistics-1f3021f8ec2c | The Tools Of Statistics | [
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Why Cryptocurrencies & Blockchain Protocols Will Thrive
Michael Guo
https://towardsdatascience.com/the-top-3-ways-blockchain-matter-in-the-future-204d93009aaa | The Top 3 Ways Blockchain Matter in the Future | [
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Are you a Data Scientist looking for a Job? Are you
Favio V zquez
https://towardsdatascience.com/the-two-sides-of-getting-a-job-as-a-data-scientist-a4571acc58bc | The two sides of Getting a Job as a Data Scientist | [
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Tableau Desktop is an awesome data analysis and data visualization tool. It allows
Kate Strachnyi
https://towardsdatascience.com/the-ultimate-cheat-sheet-on-tableau-charts-642bca94dde5 | The Ultimate Cheat Sheet on Tableau Charts | [
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Alice and Bob at the Autoencoding Olympics
Max Frenzel
https://towardsdatascience.com/the-variational-autoencoder-as-a-two-player-game-part-i-4c3737f0987b | The Variational Autoencoder as a Two-Player GamePart I | [
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Variational Return to the Autoencoding Olympics
Max Frenzel
https://towardsdatascience.com/the-variational-autoencoder-as-a-two-player-game-part-ii-b80d48512f46 | The Variational Autoencoder as a Two-Player GamePart II | [
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The Difficulties of Encoding Text
Max Frenzel
https://towardsdatascience.com/the-variational-autoencoder-as-a-two-player-game-part-iii-d8d56c301600 | The Variational Autoencoder as a Two-Player GamePart III | [
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TL;DRTake some vendor tech, add to your real client environment
Jon Hawes
https://towardsdatascience.com/the-vendor-flamability-acid-test-e56dc0587da5 | The vendor flamability acid test | [
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Data sciencea universally recognizable term that is in desperate need of dissemination.
Iliya Valchanov
https://towardsdatascience.com/the-what-where-and-how-of-data-science-6dda1af98671 | The What, Where and How of Data Science | [
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If you are a machine learning
Aditya Ananthram
https://towardsdatascience.com/the-whos-who-of-machine-learning-and-why-you-should-know-them-9cefbbc84f07 | The Whos Who Of Machine Learning, And Why You Should Know Them | [
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Ive always been pretty into crosswords. My mom and sisters would pull the New York Times Crossword
Kurt Reckziegel
https://towardsdatascience.com/the-wild-world-of-crossword-data-71d560e222f5 | The Wild World of Crossword Data | [
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Regardless of where you work or what you do, everything around you is defined by metrics. What is the
Syed Sadat Nazrul
https://towardsdatascience.com/the-world-of-data-visualization-d4b621b77e76 | The World of Data Visualization | [
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The reason I am writing this article is because I think that although both Elon Musk and Mark Zuckerberg are right. They both fail to give the entire picture. They are scaring people that are not familiar with the concept of Artificial Intelligence, or Machine Learning. The
Nessim Btesh
https://towardsdatascience.com/t... | The Zuckerberg and Musk Dilemma | [
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Data Science is getting very popular and many people are trying to jump into the
Marc-Olivier Arsenault
https://towardsdatascience.com/this-is-what-i-really-do-as-a-data-scientist-d637ed747ef9 | This is what I really do as a Data Scientist | [
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Data Visualization even a 4-year-old can understand
Narcel Reedus
https://towardsdatascience.com/this-is-your-final-warning-feaaf7248b62 | [
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When journalists write about the disruptive power of artificial intelligence in healthcare they tend to zero in on radiology and pathology and for good reason. Both trades involve the interpretation of
Leonard D'Avolio PhD
https://towardsdatascience.com/thoughts-on-jamas-adapting-to-artificial-intelligence-by-jha-and-t... | Thoughts on JAMAs Adapting to Artificial Intelligence by Jha and Topol | [
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Have you ever been in the situation, when you drive by a gas station and become
Martin Schmitz, PhD
https://towardsdatascience.com/time-series-forecasting-for-optimal-gas-refill-88650b1cf029 | Time Series Forecasting for Optimal Gas Refill | [
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This blog post discusses the pitfalls of using traditional cross-validation with time series
Courtney Cochrane
https://towardsdatascience.com/time-series-nested-cross-validation-76adba623eb9 | Time Series Nested Cross-Validation | [
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Your data is only as good as your ability to understand and communicate it, which is why choosing the right visualization/ chart is essential. If you are unable to present the data effectively, key insights may be lost. Information can be visualized in several different ways
Kate Strachnyi
https://towardsdatascience.co... | Tips for Data Visualization | [
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If you are learning a new skill, think about HOW you are learning.
Heather A. Passmore
https://towardsdatascience.com/to-learn-data-science-better-use-science-ffaae3d56bea | To Learn Data Science Better, Use SCIENCE! | [
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Todays paper is:
Tom Jacobs
https://towardsdatascience.com/tom-reads-papers-deeploco-949ec0e2e7ef | Tom Reads Papers: DeepLoco | [
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Reading the streams of numbers is possible only in the movies. In the real world, the
Vladimir Fedak
https://towardsdatascience.com/top-4-popular-big-data-visualization-tools-4ee945fe207d | Top 4 Popular Big Data Visualization Tools | [
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This curated list of mindset-changing books will help you become a better Data Scientist
Jan Zawadzki
https://towardsdatascience.com/top-5-business-related-books-every-data-scientist-should-read-6e252a3f2713 | Top 5 business-related books every Data Scientist should read | [
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You binged online courses and landed your first Data Science job. Avoid these mistakes to be successful right away.
Jan Zawadzki
https://towardsdatascience.com/top-5-mistakes-of-greenhorn-data-scientists-90fa26201d51 | Top 5 Mistakes of Greenhorn Data Scientists | [
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In this post, we will learn how to identity which topic is discussed in a
Susan Li
https://towardsdatascience.com/topic-modelling-in-python-with-nltk-and-gensim-4ef03213cd21 | Topic Modelling in Python with NLTK and Gensim | [
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Which countries in Europe have
David Peterson
https://towardsdatascience.com/tourism-trends-in-europe-which-european-countries-are-overrun-with-tourists-f60c860bd23a | Which European Countries are Overrun with Tourists? A Data Story | [
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Data scientists of the world you have nothing to gain but more customers
Robert de Graaf
https://towardsdatascience.com/towards-a-manifesto-of-defensive-data-science-a09fb9d37acd | Towards a Manifesto of Defensive Data Science | [
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TWiML Talk 118
Sam Charrington
https://towardsdatascience.com/towards-abstract-robotic-understanding-with-raja-chatila-d1c32e9c0f6d | Towards Abstract Robotic Understanding with Raja Chatila | [
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What Ive learned using Beeminder religiously for a month.
Brennan Kenneth Brown
https://towardsdatascience.com/tracking-for-good-db4809c9f456 | Tracking for Good | [
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Recently, I wrote an article about my process for designing AI that could learn to play the
Peter Binggeser
https://towardsdatascience.com/train-ai-to-play-snake-in-your-browser-ca657097d707 | Train AI to Play Snake in Your Browser | [
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What is Grid World? Grid World is a 2D rectangular grid of size (Ny, Nx) with an agent starting off at one grid square and trying to move to another grid square located elsewhere. Such an environment is a natural one for applying reinforcement learning algorithms
Anson Wong
https://towardsdatascience.com/training-an-ag... | Training an Agent to beat Grid World | [
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Doing cool things with data!
Priya Dwivedi
https://towardsdatascience.com/training-and-visualising-word-vectors-2f946c6430f8 | Training and Visualising Word Vectors | [
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Train with your own Images with Tensorflow
SAGAR SHARMA
https://towardsdatascience.com/training-inception-with-tensorflow-on-custom-images-using-cpu-8ecd91595f26 | Train Inception with Custom Images on CPU | [
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A concise introduction to problem faced during training RNNs
Prakhar Mishra
https://towardsdatascience.com/training-recurrent-networks-523d3b3bad3c | Training Recurrent Networks | [
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Transfer Learning is the reuse of a pre-trained model on a new problem. It is currently very popular in the field of Deep
Niklas Donges
https://towardsdatascience.com/transfer-learning-946518f95666 | Transfer Learning | [
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The World Bank hosts one of the richest sources of data on the Interwebs. This data has many practical applications such as forecasting economic growth or predicting poverty with machine learning. I recently used this data to create a few Tableau
Jake Huneycutt
https://towardsdatascience.com/transforming-data-in-python... | Transforming Data in Python with Pandas Melt | [
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Understanding how transpose convolution works.
Keshav Aggarwal
https://towardsdatascience.com/transpose-convolution-77818e55a123 | Demystifying Transpose Convolution | [
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A data-driven look at Trumps changing priorities over the course of his 2016
Alex P. Miller
https://towardsdatascience.com/trump-in-his-own-words-62af05ad76d4 | Trump, in his own words | [
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Your audience can be your friend or foe
Tricia Aanderud
https://towardsdatascience.com/trying-to-persuade-with-data-dont-alienate-your-audience-and-other-guidelines-2c0cd0ce6857 | Trying to Persuade with Data? | [
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TWiML & AI meetup talk: Trust in AI
Chris Butler
https://towardsdatascience.com/twiml-ai-meetup-trust-and-ai-86c32f91a0e0 | You need the right amount of trust from humans in AI | [
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Can you mutate/modify a NumPy
Tirthajyoti Sarkar
https://towardsdatascience.com/two-cool-features-of-python-numpy-mutating-by-slicing-and-broadcasting-3b0b86e8b4c7 | Two cool features of Python NumPy: Mutating by slicing and Broadcasting | [
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Ensembling sounds like a very intimidating word at first but its actually deceptively simple.lemme explain ensembling with an analogy
Sangarshanan
https://towardsdatascience.com/two-is-better-than-one-ensembling-models-611ee4fa9bd8 | Two is better than one: Ensembling Models | [
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Unless you are someone who for some very mystical or other reasons have been living away from technology, you must be very
Avinash
https://towardsdatascience.com/uber-maps-tech-talk-8df91c300f80 | Uber Maps Tech Talk | [
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This blog aims in understanding about ARIMA Models. We will understand it via knowing what it stands for followed by
Madhav Mishra
https://towardsdatascience.com/unboxing-arima-models-1dc09d2746f8 | Unboxing ARIMA Models | [
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Also the blog has the second section which has all the basic shell commands for the folks who are new in the ecosystem.
Madhav Mishra
https://towardsdatascience.com/unboxing-hadoop-distributed-file-system-hdfs-2628a69df0b3 | Unboxing Hadoop Distributed File System (HDFS) | [
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Hi folks, I hope you all are doing well. In todays edition we will understand about Linear Regression. The aim of this blog is to get a quick overview on revising the basic of linear regression with a small hands-on implementation using python, so lets fasten our shoe laces and
Madhav Mishra
https://towardsdatascience.... | Unboxing Linear Regression | [
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Introduction
Matt Ross
https://towardsdatascience.com/under-the-hood-of-neural-network-forward-propagation-the-dreaded-matrix-multiplication-a5360b33426 | Under The Hood of Neural Network Forward PropagationThe Dreaded Matrix Multiplication | [
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In Machine Learning, performance measurement is an essential task. So when it comes to a classification
Sarang Narkhede
https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5 | Understanding AUC - ROC Curve | [
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Quick Recap
Ceshine Lee
https://towardsdatascience.com/understanding-bidirectional-rnn-in-pytorch-5bd25a5dd66 | Understanding Bidirectional RNN in PyTorch | [
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I am working with many clients across the globe on implementing various data science
Martin Schmitz, PhD
https://towardsdatascience.com/understanding-clustering-cf0117148ef4 | How to Understand your Clustering Results | [
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When we get the data, after data cleaning, pre-processing and wrangling, the first step we do is to feed
Sarang Narkhede
https://towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62 | Understanding Confusion Matrix | [
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In this blog post, were going to explore (or at least attempt to) the intuition behind Convolutional Neural Networks, one of the most important deep learning techniques in machine vision and image recognition. Were also going to work through an example
Adel Nehme
https://towardsdatascience.com/understanding-convolution... | Understanding Convolutional Neural Networks | [
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A TL: DR Approach To Become Familiar With Deep Learning
Jean-Carlos Paredes
https://towardsdatascience.com/understanding-convolutions-using-excel-886ca0a964b7 | Understanding Neural Networks Using Excel | [
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Statistics is a branch of mathematics that deals with collecting, interpreting, organization
Sarang Narkhede
https://towardsdatascience.com/understanding-descriptive-statistics-c9c2b0641291 | Understanding Descriptive Statistics | [
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We have come across a lot of client requirements that boil down to using AI to understand events. Some systems need to classify events into types, others need to listen for specific events, and some need to predict events. These requirements
Daniel Shapiro, PhD
https://towardsdatascience.com/understanding-events-with-a... | Understanding Events with Artificial Intelligence | [
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Strategies for working with
DipanjanDJ) Sarkar
https://towardsdatascience.com/understanding-feature-engineering-part-1-continuous-numeric-data-da4e47099a7b | Understanding Feature Engineering (Part 1)Continuous Numeric Data | [
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Strategies for working with discrete
DipanjanDJ) Sarkar
https://towardsdatascience.com/understanding-feature-engineering-part-2-categorical-data-f54324193e63 | Understanding Feature Engineering (Part 2)Categorical Data | [
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Traditional strategies for taming unstructured, textual data
DipanjanDJ) Sarkar
https://towardsdatascience.com/understanding-feature-engineering-part-3-traditional-methods-for-text-data-f6f7d70acd41 | Understanding Feature Engineering (Part 3)Traditional Methods for Text Data | [
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Newer, advanced strategies for taming unstructured, textual
DipanjanDJ) Sarkar
https://towardsdatascience.com/understanding-feature-engineering-part-4-deep-learning-methods-for-text-data-96c44370bbfa | Understanding Feature Engineering (Part 4)A hands-on intuitive approach to Deep Learning Methods for Text DataWord2Vec, GloVe and FastText | [
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In this article, I will try to give a fairly simple and understandable explanation of one really fascinating
Simeon Kostadinov
https://towardsdatascience.com/understanding-gru-networks-2ef37df6c9be | Understanding GRU networks | [
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We were used to thinking that computers communicate through ones and zeros for
Pam Viedor
https://towardsdatascience.com/understanding-how-machines-understand-us-765a5bb8440a | Understanding How Machines Understand Us | [
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0.10070154815912247,
-0.1765650063753128,
-0.17707641422748566,
0.2679113745689392,
0.2027798742055893,
-0.04977554827928543,
-0.17096012830734... |
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