{"id":4652,"date":"2021-09-22T03:28:01","date_gmt":"2021-09-22T01:28:01","guid":{"rendered":"https:\/\/mribeirodantas.xyz\/blog\/?p=4652"},"modified":"2021-09-22T03:28:05","modified_gmt":"2021-09-22T01:28:05","slug":"best-links-of-the-week-86","status":"publish","type":"post","link":"https:\/\/mribeirodantas.xyz\/blog\/index.php\/2021\/09\/22\/best-links-of-the-week-86\/","title":{"rendered":"Best links of the week #86"},"content":{"rendered":"Reading time: <\/span> 3<\/span> minutes<\/b><\/span><\/span>\n

Best links of the week from August 2nd to September 21st<\/h3>\n\n\n\n
\"This<\/figure>\n\n\n\n

Links<\/h4>\n\n\n\n
  1. Maximum Likelihood Estimates<\/a> by Jeremy Orloff and Jonathan Bloom.<\/li>
  2. Moravec’s paradox<\/a> at Wikipedia<\/a>.<\/li>
  3. Parrondo’s paradox<\/a> at Wikipedia<\/a>.<\/li>
  4. R-squared Cautions<\/a> at STAT 462 (Applied Regression Analysis), PennState University<\/a>.<\/li>
  5. Spearman\u2019s correlation<\/a>.<\/li>
  6. CausalCity: Introducing a high-fidelity simulation with agency for advancing causal reasoning in machine learning<\/a> at Microsoft Research Blog<\/a>.<\/li>
  7. The Most Dangerous Equation<\/a> at American Scientist<\/a>. More here<\/a> and here<\/a>.<\/li>
  8. One day course on causal inference, MPI-EVA 9 September 2021<\/a> at Richard McElreath<\/a>‘s GitHub.<\/li>
  9. The cost of dichotomising continuous variables<\/a>, published in BMJ, at PubMed Central<\/a>.<\/li>
  10. How Computationally Complex Is a Single Neuron?<\/a> at QuantaMagazine<\/a>.<\/li>
  11. Focus on Peer Review Course<\/a> at Nature Masterclasses<\/a>.<\/li><\/ol>\n\n\n\n\n\n\n\n

    Blog posts<\/h4>\n\n\n\n
    1. What Is TF-IDF?<\/a> at MonkeyLearn<\/a>.<\/li>
    2. The Beginner\u2019s Guide to Text Vectorization<\/a> at MonkeyLearn<\/a>.<\/li>
    3. Naive Bayes and Text Classification<\/a> at Sebastian Raschka<\/a>.<\/li>
    4. Neural Network: Architecture, Components & Top Algorithms<\/a> at UpGrad<\/a>.<\/li>
    5. Why Initialize a Neural Network with Random Weights?<\/a> at Machine Learning Mastery<\/a>.<\/li>
    6. Hacks, IA\u2019s e manipula\u00e7\u00e3o humana<\/a> at Portal Deviante<\/a>.<\/li>
    7. Neural Network Models in R<\/a> at DataCamp Tutorial<\/a>.<\/li>
    8. Demystifying Mathematical Concepts for Deep Learning<\/a> at DataCamp Tutorial<\/a>.<\/li>
    9. Deduce the Number of Layers and Neurons for ANN<\/a> at DataCamp Tutorial<\/a>.<\/li>
    10. A Gentle Introduction to Exploding Gradients in Neural Networks<\/a> at Machine Learning Mastery<\/a>.<\/li>
    11. How to Fix the Vanishing Gradients Problem Using the ReLU<\/a> at Machine Learning Mastery<\/a>.<\/li>
    12. A Gentle Introduction to the Rectified Linear Unit (ReLU)<\/a> at Machine Learning Mastery<\/a>.<\/li>
    13. The New Base Pipe<\/a> at MyKo101<\/a>.<\/li>
    14. ETL vs ELT Explained<\/a> at The Grouparoo Blog<\/a>.<\/li>
    15. The Mathematics Behind Deep Learning<\/a> by Trist’n Joseph<\/a> at Towards Data Science<\/a>.<\/li>
    16. The Unreasonable Ineffectiveness of Deep Learning on Tabular Data<\/a> by Paul Tune<\/a> at Towards Data Science<\/a>.<\/li>
    17. SHAP Values Explained Exactly How You Wished Someone Explained to You<\/a> by Samuele Mazzanti<\/a> at Towards Data Science<\/a>. <\/li>
    18. Black-Box models are actually more explainable than a Logistic Regression<\/a> by Samuele Mazzanti<\/a> at Towards Data Science<\/a>.<\/li>
    19. Increasing developer happiness with GitHub code scanning<\/a> at GitHub blog<\/a>.<\/li>
    20. What can Avengers: Endgame teach us about Git?<\/a> at Lj Miranda<\/a>‘s Blog.<\/li><\/ol>\n\n\n\n

      Videos<\/h4>\n\n\n\n
      1. StatQuest: Maximum Likelihood, clearly explained!!!<\/a> at StatQuest with Josh Starmer<\/a>.<\/li>
      2. Maximum Likelihood for the Exponential Distribution, Clearly Explained! V2.0<\/a> at StatQuest with Josh Starmer<\/a>.<\/li>
      3. Maximum Likelihood for the Binomial Distribution, Clearly Explained!!!<\/a> at StatQuest with Josh Starmer<\/a>.<\/li>
      4. Parte 1 – Regress\u00e3o Linear – \u00c9 a sua chance! Agora ou nunca! – Prof. Adriana Silva<\/a> at EstaTiDados<\/a>.<\/li>
      5. Parte 2 – Regress\u00e3o Linear – \u00c9 a sua chance! Agora ou nunca! – Prof. Adriana Silva<\/a> at EstaTiDados<\/a>.<\/li>
      6. Parte 3 – Regress\u00e3o Log\u00edstica – \u00c9 a sua chance! Agora ou nunca! – Prof. Adriana Silva<\/a> at EstaTiDados<\/a>.<\/li>
      7. Estreia da S\u00e9rie Teoria de Conjuntos para Estat\u00edstica – Teoria de conjuntos #1<\/a> at A Ci\u00eancia da Estat\u00edstica<\/a>.<\/li>
      8. Axiomas da EXTENS\u00c3O e da ESPECIFICA\u00c7\u00c3O (Pertencimento, Igualdade e Inclus\u00e3o) -Teoria de Conjuntos #2<\/a> at A Ci\u00eancia da Estat\u00edstica<\/a>.<\/li>
      9. Axiomas do PAR e da UNI\u00c3O (uni\u00e3o, interse\u00e7\u00e3o, diferen\u00e7a, Complementar) – Teoria de Conjuntos #3<\/a> at A Ci\u00eancia da Estat\u00edstica<\/a>.<\/li>
      10. Axioma da POT\u00caNCIA (Conjuntos das partes, produtos cartesianos e rela\u00e7\u00f5es) – Teoria de conjuntos #4<\/a> at A Ci\u00eancia da Estat\u00edstica<\/a>.<\/li>
      11. FUN\u00c7\u00d5ES injetoras, sobrejetoras e bijetoras (IMAGEM e IMAGEM INVERSA) – Teoria de conjuntos #5<\/a> at A Ci\u00eancia da Estat\u00edstica<\/a>.<\/li>
      12. Cost Function in Neural Network | Types of Cost function we use in different applications<\/a> at Coding Lane<\/a>.<\/li>
      13. What is Activation function in Neural Network ? Types of Activation Function in Neural Network<\/a> at Coding Lane<\/a>.<\/li>
      14. Mas o que *\u00e9* uma Rede Neural? | Deep learning, cap\u00edtulo 1<\/a> at 3Blue1Brown<\/a>‘s YouTube channel.<\/li>
      15. Causal Discovery Part 1 by Bernhard Sch\u00f6lkopf<\/a> at MLSS Africa’s YouTube channel<\/a>.<\/li>
      16. Causal Discovery Part 2 by Bernhard Sch\u00f6lkopf<\/a> at MLSS Africa’s YouTube channel<\/a>.<\/li>
      17. Spearman’s Rank-Order Correlation<\/a> at Laerd Statistics<\/a>.<\/li>
      18. Spearman’s Rank-Order Correlation using SPSS Statistics<\/a> at Laerd Statistics<\/a>.<\/li>
      19. An Ode To R-Squared<\/a> by Callum Ballard<\/a> at Towads Data Science<\/a>.<\/li>
      20. Spearman’s Rank Correlation part 1<\/a> at <\/a>ECOHOLICS – Largest Platform for Economics<\/a>.<\/li>
      21. Spearman’s Rank Correlation part 2<\/a> at <\/a>ECOHOLICS – Largest Platform for Economics<\/a>.<\/li>
      22. <\/a><\/a>Spearman’s Rank Correlation part 3 at ECOHOLICS – Largest Platform for Economics<\/a>.<\/li>
      23. Machine Learning Experiments with DVC (Hands-On Tutorial!)<\/a> at DVCorg<\/a>‘s YouTube channel.<\/li>
      24. Checkpoints<\/a> at DVCorg<\/a>‘s YouTube channel.<\/li>
      25. Experiments<\/a> at DVCorg<\/a>‘s YouTube channel.<\/li>
      26. Deep Learning Basics: Introduction and Overview<\/a> at Lex Fridman<\/a>‘s YouTube channel.<\/li><\/ol>\n\n\n\n

        Positions available<\/h4>\n\n\n\n
        1. PostDoc in Protein Biochemistry<\/a> at MPI CBG.<\/li>
        2. PhD student in Computational Biology<\/a> at MPI CBG.<\/li>
        3. Postdoctoral Position in Computational Biology<\/a> at MPI CBG.<\/li>
        4. Postdoctoral Position in Cell Biology<\/a> at MPI CBG.<\/li>
        5. PhD scholarship in “AI for electricity market design”<\/a> at DTU.<\/li>
        6. Engineer R&D<\/a> at SteelSeries.<\/li>
        7. Postdoctoral opportunity on (hierarchical) multi-agent reinforcement learning<\/a> at Northeastern University.<\/li>
        8. Machine Learning Fairness Research PhD Intern<\/a> at Tiktok.<\/li>
        9. Assistant Professor – Computational Linguistics<\/a> at University of Toronto Scarborough.<\/li>
        10. Several postdoctoral research positions in\u00a0Bayesian\u00a0optimization\u00a0and\/or simulation-based optimization<\/a> in Montreal.<\/li>
        11. Postdoc in Computer Vision\/Deep Learning: video monitoring of insect pollinators<\/a> at the University of Puerto Rico.<\/li>
        12. Open positions for AI professors<\/a> at FHWS.<\/li>
        13. PhD scholarship in NLP<\/a> at James Cook University.<\/li>
        14. Assistant Professor – AI<\/a> at Iowa State University.<\/li>
        15. Open Postdoc Position Medical AI<\/a> at Med Uni Graz.<\/li>
        16. Staff Data Scientist<\/a> at FireEye.<\/li>
        17. Cientista de Dados | Big Data & AI<\/a> at Grupo Globo.<\/li>
        18. Pessoa Engenheira de Dados | Big Data & AI<\/a> at Grupo Globo.<\/li>
        19. Pessoa Engenheira de Machine Learning | Big Data & AI<\/a> at Grupo Globo.<\/li>
        20. Analista de Dados S\u00eanior<\/a> at OLX.<\/li><\/ol>\n\n\n\n

          <\/p>\n","protected":false},"excerpt":{"rendered":"

          Reading time: <\/span> 3<\/span> minutes<\/b><\/span><\/span>Best links of the week from August 2nd to September 21st Links Maximum Likelihood Estimates by Jeremy Orloff and Jonathan Bloom. Moravec’s paradox at Wikipedia. Parrondo’s paradox at Wikipedia. R-squared Cautions at STAT 462 (Applied Regression Analysis), PennState University. Spearman\u2019s correlation. CausalCity: Introducing a high-fidelity simulation with agency for advancing causal reasoning in machine learning …<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_uag_custom_page_level_css":"","jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false},"version":2}},"categories":[8],"tags":[],"class_list":["post-4652","post","type-post","status-publish","format-standard","hentry","category-bestlinks"],"jetpack_publicize_connections":[],"yoast_head":"\nBest links of the week #86 - The Dataist Storyteller<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/mribeirodantas.xyz\/blog\/index.php\/2021\/09\/22\/best-links-of-the-week-86\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Best links of the week #86 - The Dataist Storyteller\" \/>\n<meta property=\"og:description\" content=\"Reading time: 3 minutesBest links of the week from August 2nd to September 21st Links Maximum Likelihood Estimates by Jeremy Orloff and Jonathan Bloom. 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Moravec’s paradox at Wikipedia. Parrondo’s paradox at Wikipedia. R-squared Cautions at STAT 462 (Applied Regression Analysis), PennState University. Spearman\u2019s correlation. 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