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(No, we are not going to talk about Ripple this week. We have a life and more important things to use our mental cycles on.)
Stefano Bernardi
https://tokeneconomy.co/token-economy-30-gazing-into-the-crypstal-ball-3a02cf9fe778 | Token Economy #30: Gazing into the crypstal ball | [
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+ZK-STARK, DAICO, TON ICO, more
Yannick
https://tokeneconomy.co/token-economy-31-%EF%B8%8F-kraken-the-death-of-centralized-exchanges-6d8b2080cdd0 | Token Economy #31: Kraken & the death of centralized exchanges | [
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As you know, last September we organized a small venture retreat in the Dolomites. We had people coming from most top EU venture funds as well as crypto
Stefano Bernardi
https://tokeneconomy.co/token-economy-32-post-bloodbath-5d991aabc10f | Token Economy #32: post-bloodbath | [
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TL;DR
Stefano Bernardi
https://tokeneconomy.co/token-economy-33-dogfooding-829d1ad18cde | Token Economy #33: Dogfooding | [
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If you enjoy Token Economy you can: on
Stefano Bernardi
https://tokeneconomy.co/token-economy-35-the-failure-of-the-old-world-corporation-35ac2312eced | Token Economy #35: The failure of the old-world corporation | [
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If you enjoy Token Economy you can: on Medium and
Yannick
https://tokeneconomy.co/token-economy-36-governance-done-right-a3be7f4e21d6 | Token Economy #36: Governance done right | [
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If you enjoy Token Economy you can: on
Stefano Bernardi
https://tokeneconomy.co/token-economy-37-implications-of-massive-token-pre-sales-fd835561c3eb | Token Economy #37: Implications of massive token pre-sales | [
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If you enjoy Token Economy you can: on Medium and
Yannick
https://tokeneconomy.co/token-economy-38-regulatory-news-overdose-60d035885a7f | Token Economy #38: regulatory news overdose | [
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If you enjoy Token Economy you can on Medium and stake ETH on StakeTree
Yannick
https://tokeneconomy.co/token-economy-39-back-from-unplug-ethcc-2ee29eac4320 | Token Economy #39: back from Unplug & EthCC | [
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Is The War Against ASICs Worth Fighting?
Stefano Bernardi
https://tokeneconomy.co/token-economy-43-asics-wars-186a4a8a4afe | Token Economy #43: ASICs wars | [
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Making sense of Coinbases Earn acquisition
Stefano Bernardi
https://tokeneconomy.co/token-economy-45-earned-it-6280c9c44639 | Token Economy #45: Earned it? | [
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subscriber? Join us
Stefano Bernardi
https://tokeneconomy.co/token-economy-51-ca4fe0eda32d | Token Economy #51 | [
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This weeks Token Economy is supported by
Yannick
https://tokeneconomy.co/token-economy-53-phew-36eaee8ddfc6 | Token Economy #53: phew | [
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Plasma, Filecoin ICO, Coinbase Series D, Decentralized Twitter, Tezos VC fund &
Stefano Bernardi
https://tokeneconomy.co/token-economy-9-ba4c1a1f8c18 | Token Economy #9 | [
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Filecoin & 0x ICO terms, BTC fork, Crypto funds boom & more
Yannick
https://tokeneconomy.co/token-economy-issue-8-a0bdcfe291fd | Token EconomyIssue #8 | [
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Its a new world out there.
Stefano Bernardi
https://tokeneconomy.co/we-need-more-public-due-diligence-for-icos-7d2cab7baf25 | We need more public due diligence for ICOs | [
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A look back at televisions quintessential British man abroad and all round gentlemanthe late, great Alan Whicker.
Tom Collins
https://tomcollins.scot/alan-whicker-the-first-broadcast-journalist-c9abd01d5aef | Alan Whicker: The First Broadcast Journalist | [
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Every evening, I sit and yearn for the conditions of a Frictionless Morning
Tonner
https://tonner.blog/the-frictionless-morning-6776abf7fba3 | The Frictionless Morning | [
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Witnessing History In The Making
Rajan Nanavati
https://toptentown.com/10-current-nfl-players-wholl-enter-the-hall-of-fame-8078e7479d26 | 10 Current NFL Players Wholl Enter The Hall Of Fame | [
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Lacking In Height, Not In Heart.
Rajan Nanavati
https://toptentown.com/10-greatest-nba-players-under-6-feet-tall-ddf5f618a32e | 10 Greatest NBA Players Under 6-Feet Tall | [
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The better halves of NFL Stars
Rajan Nanavati
https://toptentown.com/10-hottest-nfl-wags-wives-and-girlfriends-21ec3e61deb4 | The 10 Hottest NFL Wives and Girlfriends | [
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Even if youre just reading it for the articles.
Rajan Nanavati
https://toptentown.com/10-hottest-sports-illustrated-swimsuit-issue-models-of-all-time-e3b6af483320 | The 10 Hottest SI Swimsuit Issue Cover Models | [
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Pros In Different Area Codes Next Year
Rajan Nanavati
https://toptentown.com/10-nfl-players-who-will-be-on-different-teams-in-2018-22f9a8a6b859 | 10 NFL Players Who Will Be On Different Teams in 2018 | [
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The Chumbawambas of NFL Lore
Rajan Nanavati
https://toptentown.com/nfl-biggest-one-hit-wonders-rg3-ickey-woods-nick-foles-8f4cc4e2280e | The Top 10 One-Hit Wonders In NFL History | [
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And a look at where they are today.
Rajan Nanavati
https://toptentown.com/the-10-biggest-nfl-draft-busts-of-the-2000s-51cdb0167838 | The 10 Biggest NFL Draft Busts of the 2000's. | [
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Giving new meaning to the term heavyweight.
Rajan Nanavati
https://toptentown.com/the-10-fattest-players-in-nfl-history-9c35b5381c71 | The 10 Fattest Players In NFL History | [
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The true terrors of the gridiron.
Rajan Nanavati
https://toptentown.com/the-10-scariest-players-in-nfl-history-7f8159a7dc0d | The 10 Scariest Players In NFL History | [
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The worst winning performances since 2000.
Rajan Nanavati
https://toptentown.com/the-10-worst-qbs-to-win-a-playoff-game-bdca011fc9ef | The 10 Worst QBs To Win A Playoff Game | [
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Things you may never have known about King James
Rajan Nanavati
https://toptentown.com/top-10-surprising-facts-about-lebron-james-bc7e056cd54c | Top 10 Surprising Facts About LeBron James | [
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Guidelines for lean, customer-centric startups
Suelyn Yu
https://tostring.informedk12.com/how-to-set-up-a-b2b-customer-council-a4ad854f3ed1 | How to set-up a B2B Customer Council | [
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Are you having trouble hiring data scientists? or, once you hire them, do they not
Formulated.by
https://towardsdatascience.com/10-common-mistakes-in-hiring-data-scientists-30db415f4ff2 | 10 Common Mistakes in Hiring Data Scientists | [
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When we talk about Big Data analytics, we should first understand why this data is
Vladimir Fedak
https://towardsdatascience.com/10-hot-trends-of-big-data-analytics-for-2017-857679364890 | 10 hot trends of Big Data Analytics for 2017 | [
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After reading an MNIST tutorial (or ten) and brushing up on some Tensorflow/Keras best practices, you might be tricked into thinking that applying a neural network for your prediction task is a plug and
Gal Yona
https://towardsdatascience.com/10-things-to-think-about-before-starting-to-code-your-deep-neural-network-650... | 10 Things to Think About Before Starting to Code Your Deep Neural Network | [
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My career in data science started a decade ago, when I took my first machine learning
Ben Weber
https://towardsdatascience.com/10-years-of-data-science-visualizations-af1dd8e443a7 | 10 Years of Data Science Visualizations | [
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Reach out in Slack to level up in your career
Formulated.by
https://towardsdatascience.com/15-data-science-slack-communities-to-join-8fac301bd6ce | 15 Data Science Slack Communities to Join | [
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Why is data science sexy? It has something to do with so many new applications
James Le
https://towardsdatascience.com/16-useful-advices-for-aspiring-data-scientists-6da9afa8c72c | 16 Useful Advices for Aspiring Data Scientists | [
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Including data in your presentations is a great way to bolster your points. Displaying data
Payman Taei
https://towardsdatascience.com/17-totally-free-venn-diagram-templates-bc384bdae8a4 | 17 Totally Free Venn Diagram Templates | [
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Do you want to develop advanced apps and software? If so, why not to use prediction and machine learning APIs as the part of your development?
Oleksii Kharkovyna
https://towardsdatascience.com/20-apis-that-prove-what-ml-and-prediction-is-capable-of-257bb7d71ed2 | 20 APIs that prove what ML and prediction is capable of | [
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Technical takeaways from a Python EDA of all
Finn Qiao
https://towardsdatascience.com/2018-world-cup-goals-through-iterators-and-zip-functions-8e4c338adb28 | 2018 World Cup Goals Through Iterators and Zip Functions | [
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Part II: Working with real world data
Brian
https://towardsdatascience.com/25-lights-part-ii-e021b66e449b | 25 Lights | [
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In NLP, we also want to find the similarity among sentence or document. Text is not
Edward Ma
https://towardsdatascience.com/3-basic-distance-measurement-in-text-mining-5852becff1d7 | 3 basic Distance Measurement in Text Mining | [
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Word Embedding is silver bullet to resolve many NLP problem. Most of modern NLP
Edward Ma
https://towardsdatascience.com/3-silver-bullets-of-word-embedding-in-nlp-10fa8f50cc5a | 3 silver bullets of word embeddings in NLP | [
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Tips and tricks on how to build efficient CNN
Arthur Douillard
https://towardsdatascience.com/3-small-but-powerful-convolutional-networks-27ef86faa42d | 3 Small But Powerful Convolutional Networks | [
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Sustained growth is the only way to ensure survival of your startup. While you might be willing to work with blood and tears, this might not be enough. Big Data can become the key to victory.
Vladimir Fedak
https://towardsdatascience.com/3-ways-to-use-big-data-to-help-your-startup-grow-6fc81f264b80 | 3 ways to use Big Data to help your startup grow | [
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The world of IT cant get enough of machine learning (ML) and its potential to
Karl Utermohlen
https://towardsdatascience.com/4-machine-learning-trends-to-watch-out-for-9eae26cdc3a7 | 4 Machine Learning Trends to Watch Out For | [
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By Ben Rogojan
SeattleDataGuy
https://towardsdatascience.com/4-must-have-skills-every-data-scientist-should-learn-8ab3f23bc325 | 4 Must Have Skills Every Data Scientist Should Learn | [
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The world of academia is becoming more personalized and convenient for students
Karl Utermohlen
https://towardsdatascience.com/4-ways-ai-is-changing-the-education-industry-b473c5d2c706 | 4 Ways AI is Changing the Education Industry | [
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Data Scientist
Ganes Kesari
https://towardsdatascience.com/4-ways-to-fail-a-data-scientist-job-interview-d9c4c85c683 | 4 Ways to fail a Data scientist job interview | [
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We asked our LinkedIn group members what their greatest challenges were to
Kirill Eremenko
https://towardsdatascience.com/45-ways-to-activate-your-data-science-career-6a0d9c664e84 | 45 Ways to Activate Your Data Science Career | [
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Successful Big Data mining relies on the correct analytical model, choosing the relevant data sources, receiving worthy results and using them to ensure the positive end-users experience.
Vladimir Fedak
https://towardsdatascience.com/5-critical-success-factors-for-big-data-mining-1f46af602836 | 5 critical success factors for Big Data mining | [
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Read your way to better visualizations
Formulated.by
https://towardsdatascience.com/5-dataviz-blogs-to-follow-d30dbd90e52c | 5 DataViz Blogs to Follow | [
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Big Data analysis is an essential tool for Business Intelligence, and Natural Language Processing (NLP) tools help process a flow of unstructured data from disparate sources.
Vladimir Fedak
https://towardsdatascience.com/5-heroic-tools-for-natural-language-processing-7f3c1f8fc9f0 | 5 Heroic Tools for Natural Language Processing | [
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I went to Estonias first AI conference to learn from the best
Daniel Rothmann
https://towardsdatascience.com/5-lessons-learned-at-north-star-ai-11c57edcbc4d | 5 lessons learned at North Star AI | [
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This article is a short version of the recently published article by one of our
Formulated.by
https://towardsdatascience.com/5-neat-dash-apps-made-by-the-dash-community-9b37852456f3 | 5 Neat Dash Apps Made by the Dash Community | [
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Im working since roughly 3 years up to 60% of my time with jupyter notebooks. I think they are
Alexander Mueller
https://towardsdatascience.com/5-reasons-why-jupyter-notebooks-suck-4dc201e27086 | 5 reasons why jupyter notebooks suck | [
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Dont worrygetting started is the hardest part
Conor Dewey
https://towardsdatascience.com/5-resources-to-inspire-your-next-data-science-project-ea6afbe20319 | 5 Resources to Inspire Your Next Data Science Project | [
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I recently had the chance to attend Big Data Spain 2017, one of
Enrique Herreros
https://towardsdatascience.com/5-takeaways-from-big-data-spain-2017-56ce76670233 | 5 Takeaways from Big Data Spain 2017 | [
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It sounded easy. Just train a facial recognition model on every NBA player for the 2017 season. But it was actually really challenging. Here is what I learned that might end up saving you a lot of time.
Aaron Edell
https://towardsdatascience.com/5-things-i-learned-training-an-ai-model-on-every-nba-player-32d906b28688 | 5 things I learned training an AI model on every NBA player | [
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Linear and Logistic regressions are usually the first modelling algorithms that
George Seif
https://towardsdatascience.com/5-types-of-regression-and-their-properties-c5e1fa12d55e | 5 Types of Regression and their properties | [
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Game of Thrones, game of wines, and other games
Oleksii Kharkovyna
https://towardsdatascience.com/5-weird-ways-to-use-data-science-c6f82afcf36 | 5 Weird Ways to Use Data Science | [
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TensorFlow API Cheetsheet
SAGAR SHARMA
https://towardsdatascience.com/50-tensorflow-js-api-explained-in-5-minutes-tensorflow-js-cheetsheet-4f8c7f9cc8b2 | 50 TensorFlow.js API Explained in 5 Minutes | TensorFlow.js Cheetsheet | [
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Drawing from anecdotal learnings in-the-trenches, this
Ganes Kesari
https://towardsdatascience.com/6-reasons-why-data-visualisation-projects-fail-1ea7a56d7602 | 6 Reasons why Data Visualisation projects Fail | [
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When new technologies become widespread, they often raise ethical questions. For example:
Orlando Torres
https://towardsdatascience.com/7-short-term-ai-ethics-questions-32791956a6ad | 7 Short-Term AI ethics questions | [
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I attended my first AI-focused conference in several years at MLConf. It was great catching up on the
Ben Weber
https://towardsdatascience.com/7-takeaways-from-mlconf-sf-1b2703db5ecb | 7 Takeaways from MLconf SF | [
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Hi, If youre interested in knowing something about me, you can start reading from the next line, if not, just jump to the first point.
Kajal Puri
https://towardsdatascience.com/7-things-i-learnt-after-speaking-at-my-first-international-conference-pycon-thailand-2018-908869baccfd | 7 Things I learnt after speaking at my first International Conference (PyCon Thailand, 2018) | [
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Imagine going to a movie with a great storyline, and all the actors in the movie are
Karishma Borkakoty
https://towardsdatascience.com/7-things-you-probably-didnt-know-about-ai-b666e8f49b39 | 7 THINGS YOU PROBABLY DIDNT KNOW ABOUT AI | [
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Data science is a tool that has been applied to many problems in the modern workplace. Thanks to faster computing and cheaper storage we have been able to predict and calculate outcomes that would have taken several times more human
SeattleDataGuy
https://towardsdatascience.com/7-use-cases-for-data-science-and-predicti... | 7 Use Cases For Data Science And Predictive Analytics | [
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We are living in the golden age of Data Driven Organizations. Algorithms! Big Data! Why
Michael Muse
https://towardsdatascience.com/8-ways-to-set-up-a-data-team-for-success-b223c5e8e674 | 8 Ways To Set Up A Data Team for Success | [
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The importance of newsletters for content curation in 2018
Conor Dewey
https://towardsdatascience.com/9-essential-newsletters-for-data-scientists-e225e4227318 | 9 Essential Newsletters for Data Scientists | [
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Tensorflow is one of the widely used libraries for implementing Machine learning and other algorithms involving large number of mathematical operations. Tensorflow was developed by Google and its one of the most popular Machine Learning libraries
Narasimha Prasanna HN
https://towardsdatascience.com/a-beginner-introduct... | A beginner introduction to TensorFlow (Part-1) | [
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Learning is a universal skill/trait that is acquired by any
Randy Lao
https://towardsdatascience.com/a-beginners-guide-to-machine-learning-5d87d1b06111 | A Beginners Guide to Machine Learning | [
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What are outliers and how to deal with them?
Sergio Santoyo
https://towardsdatascience.com/a-brief-overview-of-outlier-detection-techniques-1e0b2c19e561 | A Brief Overview of Outlier Detection Techniques | [
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Like most people, I used to consider statistics a plague that I should try
Marc Laforet
https://towardsdatascience.com/a-case-study-of-the-p-value-f0d708861334 | The problem with p-values | [
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dissed
Jason Peterson
https://towardsdatascience.com/a-century-in-wordclouds-72be5f5ca391 | A Century in Wordclouds | [
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Model Selection, Hyperparameter Tuning
William Koehrsen
https://towardsdatascience.com/a-complete-machine-learning-project-walk-through-in-python-part-two-300f1f8147e2 | A Complete Machine Learning Walk-Through in Python: Part Two | [
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Putting the machine learning pieces
William Koehrsen
https://towardsdatascience.com/a-complete-machine-learning-walk-through-in-python-part-one-c62152f39420 | A Complete Machine Learning Walk-Through in Python: Part One | [
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Interpreting a machine learning model
William Koehrsen
https://towardsdatascience.com/a-complete-machine-learning-walk-through-in-python-part-three-388834e8804b | A Complete Machine Learning Walk-Through in Python: Part Three | [
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Doing data analysis can be fun and rewarding. Its something I do a lot of in my free time
Ben Rudolph
https://towardsdatascience.com/a-data-ninjas-toolkit-abfe11d38fe8 | The toolkit for the modern data ninja | [
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The Jupyter Notebook can be found here.
Aakash Tandel
https://towardsdatascience.com/a-data-science-workflow-26c3f05a010e | A Data Science Workflow | [
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On reading Analytics for the Internet of Things (IoT) by Andrew Minteer
Eric van Rees
https://towardsdatascience.com/a-deep-dive-into-the-internet-of-things-2c8d813e9653 | A Deep Dive into The Internet of Things | [
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Deep Learning and AI were the buzz words for 2016; by the end of 2017, they have become more frequent and more confusing. So lets try and understand everything one at a time. We will look into the
Pranjal Yadav
https://towardsdatascience.com/a-deeper-understanding-of-nnets-part-1-cnns-263a6e3ac61 | A deeper understanding of NNets (Part 1)CNNs | [
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How does Artificial Intelligence affect human emotional wellbeing?
Chris Merritt
https://towardsdatascience.com/a-driverless-world-c3baea61734b | A Driverless World | [
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Bioinformatics
Vijini Mallawaarachchi
https://towardsdatascience.com/a-dummies-intro-to-bioinformatics-e8212ed7c09b | A Dummies Intro to Bioinformatics | [
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Using the FeatureSelector for efficient
William Koehrsen
https://towardsdatascience.com/a-feature-selection-tool-for-machine-learning-in-python-b64dd23710f0 | A Feature Selection Tool for Machine Learning in Python | [
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Introduction
Susan Li
https://towardsdatascience.com/a-gentle-introduction-on-market-basket-analysis-association-rules-fa4b986a40ce | A Gentle Introduction on Market Basket AnalysisAssociation Rules | [
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maximum likelihood estimation
Jonathan Balaban
https://towardsdatascience.com/a-gentle-introduction-to-maximum-likelihood-estimation-9fbff27ea12f | A Gentle Introduction to Maximum Likelihood Estimation | [
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As described in a previous post, I collected data on 1,000 of Mediums most popular stories. Here is a visualization of their most common tags. I took the liberty of mapping synonymous tags to their canonical names
Ludi Rehak
https://towardsdatascience.com/a-graph-of-mediums-tags-8e3cf6cad1d9 | A Graph of Mediums Tags | [
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The crucial need for validation and how people start to neglect
Kemal Tugrul
https://towardsdatascience.com/a-great-pitfall-neglecting-validation-9b8621dc5a87 | A Great Pitfall: Neglecting Validation | [
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After recently using Pandas and Matplotlib to produce the graphs /
Hugo Dolan
https://towardsdatascience.com/a-guide-to-pandas-and-matplotlib-for-data-exploration-56fad95f951c | A Guide to Pandas and Matplotlib for Data Exploration | [
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AI Research in RTS games has a rich history. For over a decade
Ben Weber
https://towardsdatascience.com/a-history-of-rts-ai-research-72339bcaa3ee | A History of RTS AI Research | [
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A step-by-step implementation
Pedro Buarque
https://towardsdatascience.com/a-modified-artificial-bee-colony-algorithm-to-solve-clustering-problems-fc0b69bd0788 | A modified Artificial Bee Colony algorithm to solve Clustering problems | [
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In my previous post, while discussing the importance of DSLs in ML and AI, we
Mattia Ferrini
https://towardsdatascience.com/a-new-programming-paradigm-for-deep-learning-8ce53b5b6345 | Deep Learning and a New Programming Paradigm | [
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Working in a corporate innovation lab has taught me many things, with one of the most notable
Dexter Fichuk
https://towardsdatascience.com/a-practical-start-to-machine-learning-421b0e8d5b2a | A Practical Start to Machine Learning | [
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Proven and tested hands-on strategies to tackle NLP tasks
DipanjanDJ) Sarkar
https://towardsdatascience.com/a-practitioners-guide-to-natural-language-processing-part-i-processing-understanding-text-9f4abfd13e72 | A Practitioner's Guide to Natural Language Processing (Part I)Processing & Understanding Text | [
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I started using PyTorch a couple of days ago. Below I outline key PyTorch concepts along with a
Radek Osmulski
https://towardsdatascience.com/a-practitioners-guide-to-pytorch-1d0f6a238040 | A practitioner's guide to PyTorch | [
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Deep learning will soon be an invisible part of every organization, says the 11th annual report on emerging technologies from The Future Today Institute. The report highlights massive increases in computational power and the
Rahul Dev
https://towardsdatascience.com/a-roadmap-for-integrating-deep-learning-in-an-enterpri... | A roadmap for integrating deep learning in an enterprise | [
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Convolutional Neural Networks (CNNs) are the current state-of-art architecture for
Sambit Mahapatra
https://towardsdatascience.com/a-simple-2d-cnn-for-mnist-digit-recognition-a998dbc1e79a | A simple 2D CNN for MNIST digit recognition | [
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Gathering data about application usage and user behavior such as player progress in games
Ben Weber
https://towardsdatascience.com/a-simple-and-scalable-analytics-pipeline-53720b1dbd35 | A Simple and Scalable Analytics Pipeline | [
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The Inception network was an important milestone in the development of CNN classifiers. Prior to its inception (pun intended), most popular CNNs just stacked convolution layers deeper and deeper, hoping to get better performance.
Bharath Raj
https://towardsdatascience.com/a-simple-guide-to-the-versions-of-the-inception... | A Simple Guide to the Versions of the Inception Network | [
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-0.020389895886182785,
-0.13622578978538513,
-0.2075963020324707,
-0.06184222549200058,
-0.5090153217315674,
0.039911139756441116,
-0.010756682604551315,
-0.08996297419071198,
-0.08493465185165405,
-0.1266... |
Currently a limited variety of tools are available in terms of deep learning frameworks since
Nuzhi Meyen
https://towardsdatascience.com/a-survey-of-deep-learning-frameworks-43b88b11af34 | A Survey of Deep Learning Frameworks | [
0.003413337515667081,
0.13440483808517456,
-0.45872604846954346,
-0.17618314921855927,
0.12243032455444336,
0.12826921045780182,
-0.17465537786483765,
-0.09767632931470871,
-0.07113657146692276,
-0.05882459878921509,
-0.3528801202774048,
0.11402750015258789,
0.319038450717926,
0.0579840093... |
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