[FreeCourseLab.com] Udemy - Machine Learning with Javascript

磁力链接/BT种子名称

[FreeCourseLab.com] Udemy - Machine Learning with Javascript

磁力链接/BT种子简介

种子哈希：501bc57122d38da6db13c5a741531de660e471fc
文件大小： 10.2G
已经下载：5457次
下载速度：极快
收录时间：2020-05-04
最近下载：2025-10-14

地址随时变，回家记住路

小野猫.com黑猫警长.com哆啦a猫.com御猫.com科目三.com猫哭老鼠.com女猫.com☜☜☜找最新地址请保存左面网址

磁力链接

magnet:?xt=urn:btih:501BC57122D38DA6DB13C5A741531DE660E471FC
推荐使用PIKPAK网盘下载资源，PIKPAK是目前最好用网盘，10T超大空间，不和谐任何资源，支持无限次数离线下载，视频在线观看

下载BT种子文件

磁力链接 迅雷下载 在线观看 世界之窗 含羞草 极乐禁地 91视频 51品茶 逼哩逼哩 萝莉岛 欲漫涩 91短视频 成人快手 抖阴破解版 ai色色 pilipili 草榴社区 哆哔涩漫 好色先生 疯马秀 TikTok成人版 悠悠禁区 波多 听泉鉴鲍 xvideo 外网天堂 PornHub 抖音Max 呦乐园 拔萝卜 糖心视频 麻豆Vlog 暗网Xvideo

资源截图

API Integration

最近搜索

文件列表

  • 5. Getting Started with Gradient Descent/9. Why a Learning Rate.mp4 196.4 MB
  • 6. Gradient Descent with Tensorflow/13. How it All Works Together!.mp4 150.8 MB
  • 2. Algorithm Overview/13. Investigating Optimal K Values.mp4 135.4 MB
  • 5. Getting Started with Gradient Descent/3. Understanding Gradient Descent.mp4 132.9 MB
  • 5. Getting Started with Gradient Descent/12. Multiple Terms in Action.mp4 129.1 MB
  • 7. Increasing Performance with Vectorized Solutions/13. Moving Towards Multivariate Regression.mp4 127.3 MB
  • 5. Getting Started with Gradient Descent/7. Gradient Descent in Action.mp4 121.0 MB
  • 3. Onwards to Tensorflow JS!/3. Tensor Shape and Dimension.mp4 119.8 MB
  • 1. What is Machine Learning/3. A Complete Walkthrough.mp4 114.4 MB
  • 11. Multi-Value Classification/4. A Single Instance Approach.mp4 108.6 MB
  • 6. Gradient Descent with Tensorflow/8. Interpreting Results.mp4 106.7 MB
  • 13. Performance Optimization/6. Measuring Memory Usage.mp4 101.3 MB
  • 11. Multi-Value Classification/9. Marginal vs Conditional Probability.mp4 99.8 MB
  • 5. Getting Started with Gradient Descent/4. Guessing Coefficients with MSE.mp4 98.0 MB
  • 2. Algorithm Overview/1. How K-Nearest Neighbor Works.mp4 97.9 MB
  • 4. Applications of Tensorflow/11. Normalization or Standardization.mp4 97.5 MB
  • 6. Gradient Descent with Tensorflow/12. Simplification with Matrix Multiplication.mp4 95.2 MB
  • 4. Applications of Tensorflow/8. Loading CSV Data.mp4 93.7 MB
  • 12. Image Recognition In Action/8. Debugging the Calculation Process.mp4 93.4 MB
  • 6. Gradient Descent with Tensorflow/5. Initial Gradient Descent Implementation.mp4 92.2 MB
  • 10. Natural Binary Classification/13. A Touch More Refactoring.mp4 91.7 MB
  • 4. Applications of Tensorflow/14. Debugging Calculations.mp4 90.9 MB
  • 7. Increasing Performance with Vectorized Solutions/2. Refactoring to One Equation.mp4 88.9 MB
  • 7. Increasing Performance with Vectorized Solutions/14. Refactoring for Multivariate Analysis.mp4 86.4 MB
  • 7. Increasing Performance with Vectorized Solutions/5. Calculating Model Accuracy.mp4 84.3 MB
  • 2. Algorithm Overview/22. Feature Selection with KNN.mp4 84.3 MB
  • 12. Image Recognition In Action/6. Implementing an Accuracy Gauge.mp4 83.8 MB
  • 9. Gradient Descent Alterations/6. Making Predictions with the Model.mp4 83.4 MB
  • 10. Natural Binary Classification/5. Decision Boundaries.mp4 83.0 MB
  • 2. Algorithm Overview/16. N-Dimension Distance.mp4 82.7 MB
  • 4. Applications of Tensorflow/3. KNN with Tensorflow.mp4 82.5 MB
  • 5. Getting Started with Gradient Descent/6. Derivatives!.mp4 81.7 MB
  • 9. Gradient Descent Alterations/1. Batch and Stochastic Gradient Descent.mp4 81.0 MB
  • 7. Increasing Performance with Vectorized Solutions/15. Learning Rate Optimization.mp4 80.4 MB
  • 3. Onwards to Tensorflow JS!/1. Let's Get Our Bearings.mp4 80.3 MB
  • 7. Increasing Performance with Vectorized Solutions/6. Implementing Coefficient of Determination.mp4 79.5 MB
  • 14. Appendix Custom CSV Loader/10. Splitting Test and Training.mp4 79.3 MB
  • 2. Algorithm Overview/19. Feature Normalization.mp4 76.5 MB
  • 7. Increasing Performance with Vectorized Solutions/1. Refactoring the Linear Regression Class.mp4 76.2 MB
  • 7. Increasing Performance with Vectorized Solutions/7. Dealing with Bad Accuracy.mp4 74.9 MB
  • 2. Algorithm Overview/17. Arbitrary Feature Spaces.mp4 74.7 MB
  • 2. Algorithm Overview/14. Updating KNN for Multiple Features.mp4 74.0 MB
  • 10. Natural Binary Classification/11. Updating Linear Regression for Logistic Regression.mp4 73.7 MB
  • 10. Natural Binary Classification/16. Variable Decision Boundaries.mp4 71.6 MB
  • 6. Gradient Descent with Tensorflow/9. Matrix Multiplication.mp4 70.7 MB
  • 9. Gradient Descent Alterations/4. Iterating Over Batches.mp4 70.7 MB
  • 6. Gradient Descent with Tensorflow/6. Calculating MSE Slopes.mp4 70.4 MB
  • 2. Algorithm Overview/20. Normalization with MinMax.mp4 70.3 MB
  • 9. Gradient Descent Alterations/5. Evaluating Batch Gradient Descent Results.mp4 69.5 MB
  • 7. Increasing Performance with Vectorized Solutions/3. A Few More Changes.mp4 69.4 MB
  • 11. Multi-Value Classification/8. Training a Multinominal Model.mp4 69.3 MB
  • 9. Gradient Descent Alterations/3. Determining Batch Size and Quantity.mp4 69.3 MB
  • 2. Algorithm Overview/23. Objective Feature Picking.mp4 69.2 MB
  • 5. Getting Started with Gradient Descent/8. Quick Breather and Review.mp4 69.0 MB
  • 2. Algorithm Overview/2. Lodash Review.mp4 68.1 MB
  • 4. Applications of Tensorflow/10. Reporting Error Percentages.mp4 67.6 MB
  • 2. Algorithm Overview/18. Magnitude Offsets in Features.mp4 67.2 MB
  • 6. Gradient Descent with Tensorflow/10. More on Matrix Multiplication.mp4 66.3 MB
  • 4. Applications of Tensorflow/5. Sorting Tensors.mp4 65.9 MB
  • 1. What is Machine Learning/2. Solving Machine Learning Problems.mp4 65.8 MB
  • 11. Multi-Value Classification/10. Sigmoid vs Softmax.mp4 65.8 MB
  • 6. Gradient Descent with Tensorflow/3. Default Algorithm Options.vtt 65.7 MB
  • 6. Gradient Descent with Tensorflow/3. Default Algorithm Options.mp4 65.7 MB
  • 7. Increasing Performance with Vectorized Solutions/17. Updating Learning Rate.mp4 65.2 MB
  • 3. Onwards to Tensorflow JS!/6. Broadcasting Operations.mp4 65.1 MB
  • 12. Image Recognition In Action/5. Encoding Label Values.mp4 65.0 MB
  • 8. Plotting Data with Javascript/2. Plotting MSE Values.mp4 64.4 MB
  • 10. Natural Binary Classification/2. Logistic Regression in Action.mp4 64.0 MB
  • 10. Natural Binary Classification/17. Mean Squared Error vs Cross Entropy.mp4 63.1 MB
  • 6. Gradient Descent with Tensorflow/11. Matrix Form of Slope Equations.mp4 62.5 MB
  • 10. Natural Binary Classification/7. Project Setup for Logistic Regression.mp4 62.3 MB
  • 2. Algorithm Overview/3. Implementing KNN.mp4 62.2 MB
  • 3. Onwards to Tensorflow JS!/10. Creating Slices of Data.mp4 61.8 MB
  • 3. Onwards to Tensorflow JS!/5. Elementwise Operations.mp4 61.2 MB
  • 4. Applications of Tensorflow/6. Averaging Top Values.mp4 61.0 MB
  • 7. Increasing Performance with Vectorized Solutions/10. Reapplying Standardization.mp4 60.8 MB
  • 12. Image Recognition In Action/4. Flattening Image Data.mp4 60.6 MB
  • 4. Applications of Tensorflow/4. Maintaining Order Relationships.mp4 60.6 MB
  • 14. Appendix Custom CSV Loader/8. Extracting Data Columns.mp4 60.0 MB
  • 6. Gradient Descent with Tensorflow/1. Project Overview.mp4 59.8 MB
  • 3. Onwards to Tensorflow JS!/13. Massaging Dimensions with ExpandDims.mp4 59.8 MB
  • 13. Performance Optimization/5. Shallow vs Retained Memory Usage.mp4 59.7 MB
  • 5. Getting Started with Gradient Descent/5. Observations Around MSE.mp4 58.8 MB
  • 13. Performance Optimization/4. The Javascript Garbage Collector.mp4 58.5 MB
  • 10. Natural Binary Classification/3. Bad Equation Fits.mp4 58.1 MB
  • 12. Image Recognition In Action/2. Greyscale Values.mp4 58.0 MB
  • 9. Gradient Descent Alterations/2. Refactoring Towards Batch Gradient Descent.mp4 57.8 MB
  • 13. Performance Optimization/21. Improving Model Accuracy.mp4 57.7 MB
  • 4. Applications of Tensorflow/1. KNN with Regression.mp4 57.7 MB
  • 10. Natural Binary Classification/15. Implementing a Test Function.mp4 57.4 MB
  • 2. Algorithm Overview/10. Gauging Accuracy.mp4 56.6 MB
  • 4. Applications of Tensorflow/12. Numerical Standardization with Tensorflow.mp4 55.6 MB
  • 4. Applications of Tensorflow/9. Running an Analysis.mp4 55.1 MB
  • 2. Algorithm Overview/12. Refactoring Accuracy Reporting.mp4 54.8 MB
  • 14. Appendix Custom CSV Loader/9. Shuffling Data via Seed Phrase.mp4 54.7 MB
  • 7. Increasing Performance with Vectorized Solutions/16. Recording MSE History.mp4 54.5 MB
  • 5. Getting Started with Gradient Descent/2. Why Linear Regression.mp4 52.8 MB
  • 2. Algorithm Overview/4. Finishing KNN Implementation.mp4 52.7 MB
  • 11. Multi-Value Classification/2. A Smart Refactor to Multinominal Analysis.mp4 52.4 MB
  • 10. Natural Binary Classification/18. Refactoring with Cross Entropy.mp4 51.8 MB
  • 10. Natural Binary Classification/19. Finishing the Cost Refactor.mp4 51.5 MB
  • 13. Performance Optimization/3. Creating Memory Snapshots.mp4 51.4 MB
  • 11. Multi-Value Classification/11. Refactoring Sigmoid to Softmax.mp4 51.2 MB
  • 3. Onwards to Tensorflow JS!/2. A Plan to Move Forward.mp4 51.0 MB
  • 10. Natural Binary Classification/10. Encoding Label Values.mp4 50.9 MB
  • 11. Multi-Value Classification/5. Refactoring to Multi-Column Weights.mp4 50.8 MB
  • 11. Multi-Value Classification/6. A Problem to Test Multinominal Classification.mp4 50.8 MB
  • 1. What is Machine Learning/7. Dataset Structures.mp4 50.6 MB
  • 12. Image Recognition In Action/9. Dealing with Zero Variances.mp4 50.2 MB
  • 7. Increasing Performance with Vectorized Solutions/11. Fixing Standardization Issues.mp4 50.2 MB
  • 8. Plotting Data with Javascript/3. Plotting MSE History against B Values.mp4 50.1 MB
  • 13. Performance Optimization/17. Plotting Cost History.mp4 49.9 MB
  • 1. What is Machine Learning/9. What Type of Problem.mp4 49.3 MB
  • 13. Performance Optimization/10. Tensorflow's Eager Memory Usage.mp4 49.1 MB
  • 13. Performance Optimization/19. Fixing Cost History.mp4 49.0 MB
  • 13. Performance Optimization/18. NaN in Cost History.mp4 48.6 MB
  • 13. Performance Optimization/13. Tidying the Training Loop.mp4 48.2 MB
  • 8. Plotting Data with Javascript/1. Observing Changing Learning Rate and MSE.mp4 48.1 MB
  • 10. Natural Binary Classification/4. The Sigmoid Equation.mp4 47.7 MB
  • 2. Algorithm Overview/21. Applying Normalization.mp4 47.6 MB
  • 2. Algorithm Overview/7. Test and Training Data.mp4 47.4 MB
  • 2. Algorithm Overview/5. Testing the Algorithm.mp4 47.2 MB
  • 12. Image Recognition In Action/3. Many Features.mp4 46.9 MB
  • 11. Multi-Value Classification/7. Classifying Continuous Values.mp4 46.7 MB
  • 7. Increasing Performance with Vectorized Solutions/8. Reminder on Standardization.mp4 46.6 MB
  • 13. Performance Optimization/1. Handing Large Datasets.mp4 46.6 MB
  • 5. Getting Started with Gradient Descent/11. Gradient Descent with Multiple Terms.mp4 46.4 MB
  • 2. Algorithm Overview/15. Multi-Dimensional KNN.mp4 46.4 MB
  • 3. Onwards to Tensorflow JS!/11. Tensor Concatenation.mp4 46.3 MB
  • 6. Gradient Descent with Tensorflow/2. Data Loading.mp4 45.6 MB
  • 13. Performance Optimization/8. Measuring Footprint Reduction.mp4 45.4 MB
  • 10. Natural Binary Classification/20. Plotting Changing Cost History.mp4 45.0 MB
  • 4. Applications of Tensorflow/15. What Now.mp4 44.4 MB
  • 3. Onwards to Tensorflow JS!/2. A Plan to Move Forward.vtt 44.1 MB
  • 4. Applications of Tensorflow/13. Applying Standardization.mp4 43.5 MB
  • 3. Onwards to Tensorflow JS!/12. Summing Values Along an Axis.mp4 43.4 MB
  • 4. Applications of Tensorflow/2. A Change in Data Structure.mp4 43.4 MB
  • 5. Getting Started with Gradient Descent/10. Answering Common Questions.mp4 42.9 MB
  • 2. Algorithm Overview/6. Interpreting Bad Results.mp4 42.7 MB
  • 2. Algorithm Overview/9. Generalizing KNN.mp4 40.9 MB
  • 10. Natural Binary Classification/9. Importing Vehicle Data.mp4 40.8 MB
  • 11. Multi-Value Classification/3. A Smarter Refactor!.mp4 40.2 MB
  • 13. Performance Optimization/2. Minimizing Memory Usage.mp4 40.0 MB
  • 13. Performance Optimization/12. Implementing TF Tidy.mp4 39.4 MB
  • 7. Increasing Performance with Vectorized Solutions/9. Data Processing in a Helper Method.mp4 39.0 MB
  • 14. Appendix Custom CSV Loader/7. Custom Value Parsing.mp4 38.5 MB
  • 10. Natural Binary Classification/14. Gauging Classification Accuracy.mp4 38.5 MB
  • 7. Increasing Performance with Vectorized Solutions/12. Massaging Learning Rates.mp4 38.2 MB
  • 13. Performance Optimization/16. Final Memory Report.mp4 38.0 MB
  • 2. Algorithm Overview/8. Randomizing Test Data.mp4 37.8 MB
  • 13. Performance Optimization/7. Releasing References.mp4 37.7 MB
  • 4. Applications of Tensorflow/7. Moving to the Editor.mp4 36.0 MB
  • 1. What is Machine Learning/6. Identifying Relevant Data.mp4 35.6 MB
  • 6. Gradient Descent with Tensorflow/7. Updating Coefficients.mp4 35.5 MB
  • 7. Increasing Performance with Vectorized Solutions/4. Same Results Or Not.mp4 35.5 MB
  • 2. Algorithm Overview/11. Printing a Report.mp4 34.9 MB
  • 10. Natural Binary Classification/12. The Sigmoid Equation with Logistic Regression.mp4 34.4 MB
  • 1. What is Machine Learning/8. Recording Observation Data.mp4 34.3 MB
  • 14. Appendix Custom CSV Loader/6. Parsing Number Values.mp4 32.9 MB
  • 11. Multi-Value Classification/13. Calculating Accuracy.mp4 32.8 MB
  • 1. What is Machine Learning/5. Problem Outline.mp4 32.7 MB
  • 3. Onwards to Tensorflow JS!/9. Tensor Accessors.mp4 31.9 MB
  • 11. Multi-Value Classification/12. Implementing Accuracy Gauges.mp4 30.1 MB
  • 2. Algorithm Overview/24. Evaluating Different Feature Values.mp4 29.3 MB
  • 6. Gradient Descent with Tensorflow/4. Formulating the Training Loop.mp4 29.0 MB
  • 13. Performance Optimization/15. One More Optimization.mp4 28.8 MB
  • 3. Onwards to Tensorflow JS!/8. Logging Tensor Data.mp4 27.3 MB
  • 12. Image Recognition In Action/10. Backfilling Variance.mp4 27.0 MB
  • 5. Getting Started with Gradient Descent/1. Linear Regression.mp4 26.6 MB
  • 11. Multi-Value Classification/1. Multinominal Logistic Regression.mp4 26.2 MB
  • 12. Image Recognition In Action/1. Handwriting Recognition.mp4 25.9 MB
  • 13. Performance Optimization/11. Cleaning up Tensors with Tidy.mp4 25.4 MB
  • 10. Natural Binary Classification/1. Introducing Logistic Regression.mp4 24.6 MB
  • 13. Performance Optimization/20. Massaging Learning Parameters.mp4 23.6 MB
  • 14. Appendix Custom CSV Loader/4. Splitting into Columns.mp4 21.3 MB
  • 12. Image Recognition In Action/7. Unchanging Accuracy.mp4 21.3 MB
  • 1. What is Machine Learning/4. App Setup.mp4 20.2 MB
  • 14. Appendix Custom CSV Loader/3. Reading Files from Disk.mp4 19.5 MB
  • 13. Performance Optimization/9. Optimization Tensorflow Memory Usage.mp4 19.4 MB
  • 14. Appendix Custom CSV Loader/5. Dropping Trailing Columns.mp4 19.3 MB
  • 13. Performance Optimization/14. Measuring Reduced Memory Usage.mp4 19.0 MB
  • 14. Appendix Custom CSV Loader/1. Loading CSV Files.mp4 16.6 MB
  • 10. Natural Binary Classification/6. Changes for Logistic Regression.mp4 13.1 MB
  • 14. Appendix Custom CSV Loader/2. A Test Dataset.mp4 10.0 MB
  • 1. What is Machine Learning/1. Getting Started - How to Get Help.mp4 8.8 MB
  • 10. Natural Binary Classification/8.1 regressions.zip.zip 35.1 kB
  • 5. Getting Started with Gradient Descent/9. Why a Learning Rate.vtt 23.2 kB
  • 6. Gradient Descent with Tensorflow/13. How it All Works Together!.vtt 18.7 kB
  • 5. Getting Started with Gradient Descent/3. Understanding Gradient Descent.vtt 17.4 kB
  • 3. Onwards to Tensorflow JS!/3. Tensor Shape and Dimension.vtt 17.1 kB
  • 5. Getting Started with Gradient Descent/7. Gradient Descent in Action.vtt 16.5 kB
  • 7. Increasing Performance with Vectorized Solutions/13. Moving Towards Multivariate Regression.vtt 16.3 kB
  • 2. Algorithm Overview/13. Investigating Optimal K Values.vtt 16.1 kB
  • 5. Getting Started with Gradient Descent/12. Multiple Terms in Action.vtt 14.8 kB
  • 11. Multi-Value Classification/9. Marginal vs Conditional Probability.vtt 14.4 kB
  • 6. Gradient Descent with Tensorflow/8. Interpreting Results.vtt 13.9 kB
  • 5. Getting Started with Gradient Descent/4. Guessing Coefficients with MSE.vtt 13.9 kB
  • 11. Multi-Value Classification/4. A Single Instance Approach.vtt 13.8 kB
  • 2. Algorithm Overview/2. Lodash Review.vtt 13.7 kB
  • 2. Algorithm Overview/16. N-Dimension Distance.vtt 13.7 kB
  • 1. What is Machine Learning/3. A Complete Walkthrough.vtt 13.7 kB
  • 4. Applications of Tensorflow/8. Loading CSV Data.vtt 13.5 kB
  • 4. Applications of Tensorflow/3. KNN with Tensorflow.vtt 13.3 kB
  • 6. Gradient Descent with Tensorflow/12. Simplification with Matrix Multiplication.vtt 12.9 kB
  • 6. Gradient Descent with Tensorflow/5. Initial Gradient Descent Implementation.vtt 12.8 kB
  • 7. Increasing Performance with Vectorized Solutions/2. Refactoring to One Equation.vtt 12.5 kB
  • 13. Performance Optimization/6. Measuring Memory Usage.vtt 12.3 kB
  • 2. Algorithm Overview/17. Arbitrary Feature Spaces.vtt 12.0 kB
  • 7. Increasing Performance with Vectorized Solutions/5. Calculating Model Accuracy.vtt 11.9 kB
  • 4. Applications of Tensorflow/14. Debugging Calculations.vtt 11.7 kB
  • 2. Algorithm Overview/1. How K-Nearest Neighbor Works.vtt 11.7 kB
  • 12. Image Recognition In Action/8. Debugging the Calculation Process.vtt 11.6 kB
  • 2. Algorithm Overview/22. Feature Selection with KNN.vtt 11.5 kB
  • 7. Increasing Performance with Vectorized Solutions/15. Learning Rate Optimization.vtt 11.3 kB
  • 3. Onwards to Tensorflow JS!/1. Let's Get Our Bearings.vtt 11.1 kB
  • 3. Onwards to Tensorflow JS!/13. Massaging Dimensions with ExpandDims.vtt 11.0 kB
  • 4. Applications of Tensorflow/5. Sorting Tensors.vtt 10.9 kB
  • 10. Natural Binary Classification/5. Decision Boundaries.vtt 10.8 kB
  • 9. Gradient Descent Alterations/4. Iterating Over Batches.vtt 10.8 kB
  • 7. Increasing Performance with Vectorized Solutions/14. Refactoring for Multivariate Analysis.vtt 10.7 kB
  • 9. Gradient Descent Alterations/6. Making Predictions with the Model.vtt 10.7 kB
  • 3. Onwards to Tensorflow JS!/5. Elementwise Operations.vtt 10.7 kB
  • 14. Appendix Custom CSV Loader/10. Splitting Test and Training.vtt 10.7 kB
  • 7. Increasing Performance with Vectorized Solutions/7. Dealing with Bad Accuracy.vtt 10.7 kB
  • 10. Natural Binary Classification/13. A Touch More Refactoring.vtt 10.6 kB
  • 4. Applications of Tensorflow/12. Numerical Standardization with Tensorflow.vtt 10.6 kB
  • 7. Increasing Performance with Vectorized Solutions/6. Implementing Coefficient of Determination.vtt 10.6 kB
  • 4. Applications of Tensorflow/11. Normalization or Standardization.vtt 10.6 kB
  • 4. Applications of Tensorflow/6. Averaging Top Values.vtt 10.5 kB
  • 7. Increasing Performance with Vectorized Solutions/1. Refactoring the Linear Regression Class.vtt 10.5 kB
  • 3. Onwards to Tensorflow JS!/10. Creating Slices of Data.vtt 10.4 kB
  • 2. Algorithm Overview/19. Feature Normalization.vtt 10.4 kB
  • 9. Gradient Descent Alterations/1. Batch and Stochastic Gradient Descent.vtt 10.3 kB
  • 10. Natural Binary Classification/16. Variable Decision Boundaries.vtt 10.3 kB
  • 12. Image Recognition In Action/6. Implementing an Accuracy Gauge.vtt 10.3 kB
  • 6. Gradient Descent with Tensorflow/9. Matrix Multiplication.vtt 10.1 kB
  • 10. Natural Binary Classification/11. Updating Linear Regression for Logistic Regression.vtt 10.0 kB
  • 5. Getting Started with Gradient Descent/6. Derivatives!.vtt 9.8 kB
  • 10. Natural Binary Classification/2. Logistic Regression in Action.vtt 9.7 kB
  • 3. Onwards to Tensorflow JS!/6. Broadcasting Operations.vtt 9.6 kB
  • 4. Applications of Tensorflow/4. Maintaining Order Relationships.vtt 9.5 kB
  • 2. Algorithm Overview/3. Implementing KNN.vtt 9.5 kB
  • 2. Algorithm Overview/20. Normalization with MinMax.vtt 9.3 kB
  • 2. Algorithm Overview/14. Updating KNN for Multiple Features.vtt 9.3 kB
  • 13. Performance Optimization/4. The Javascript Garbage Collector.vtt 9.2 kB
  • 7. Increasing Performance with Vectorized Solutions/17. Updating Learning Rate.vtt 9.1 kB
  • 7. Increasing Performance with Vectorized Solutions/3. A Few More Changes.vtt 9.1 kB
  • 12. Image Recognition In Action/9. Dealing with Zero Variances.vtt 9.0 kB
  • 11. Multi-Value Classification/10. Sigmoid vs Softmax.vtt 8.9 kB
  • 11. Multi-Value Classification/8. Training a Multinominal Model.vtt 8.8 kB
  • 6. Gradient Descent with Tensorflow/11. Matrix Form of Slope Equations.vtt 8.7 kB
  • 6. Gradient Descent with Tensorflow/6. Calculating MSE Slopes.vtt 8.7 kB
  • 6. Gradient Descent with Tensorflow/1. Project Overview.vtt 8.6 kB
  • 6. Gradient Descent with Tensorflow/10. More on Matrix Multiplication.vtt 8.4 kB
  • 2. Algorithm Overview/23. Objective Feature Picking.vtt 8.4 kB
  • 4. Applications of Tensorflow/10. Reporting Error Percentages.vtt 8.4 kB
  • 1. What is Machine Learning/2. Solving Machine Learning Problems.vtt 8.4 kB
  • 5. Getting Started with Gradient Descent/5. Observations Around MSE.vtt 8.4 kB
  • 4. Applications of Tensorflow/9. Running an Analysis.vtt 8.4 kB
  • 10. Natural Binary Classification/7. Project Setup for Logistic Regression.vtt 8.3 kB
  • 1. What is Machine Learning/7. Dataset Structures.vtt 8.3 kB
  • 13. Performance Optimization/5. Shallow vs Retained Memory Usage.vtt 8.2 kB
  • 5. Getting Started with Gradient Descent/8. Quick Breather and Review.vtt 8.2 kB
  • 9. Gradient Descent Alterations/5. Evaluating Batch Gradient Descent Results.vtt 8.2 kB
  • 7. Increasing Performance with Vectorized Solutions/11. Fixing Standardization Issues.vtt 8.1 kB
  • 10. Natural Binary Classification/17. Mean Squared Error vs Cross Entropy.vtt 8.1 kB
  • 9. Gradient Descent Alterations/3. Determining Batch Size and Quantity.vtt 8.0 kB
  • 12. Image Recognition In Action/4. Flattening Image Data.vtt 8.0 kB
  • 2. Algorithm Overview/4. Finishing KNN Implementation.vtt 7.9 kB
  • 10. Natural Binary Classification/3. Bad Equation Fits.vtt 7.8 kB
  • 2. Algorithm Overview/18. Magnitude Offsets in Features.vtt 7.8 kB
  • 7. Increasing Performance with Vectorized Solutions/10. Reapplying Standardization.vtt 7.7 kB
  • 3. Onwards to Tensorflow JS!/9. Tensor Accessors.vtt 7.7 kB
  • 10. Natural Binary Classification/15. Implementing a Test Function.vtt 7.6 kB
  • 3. Onwards to Tensorflow JS!/11. Tensor Concatenation.vtt 7.6 kB
  • 12. Image Recognition In Action/5. Encoding Label Values.vtt 7.6 kB
  • 14. Appendix Custom CSV Loader/9. Shuffling Data via Seed Phrase.vtt 7.6 kB
  • 3. Onwards to Tensorflow JS!/12. Summing Values Along an Axis.vtt 7.4 kB
  • 11. Multi-Value Classification/2. A Smart Refactor to Multinominal Analysis.vtt 7.4 kB
  • 8. Plotting Data with Javascript/2. Plotting MSE Values.vtt 7.4 kB
  • 10. Natural Binary Classification/18. Refactoring with Cross Entropy.vtt 7.4 kB
  • 13. Performance Optimization/3. Creating Memory Snapshots.vtt 7.3 kB
  • 7. Increasing Performance with Vectorized Solutions/16. Recording MSE History.vtt 7.3 kB
  • 9. Gradient Descent Alterations/2. Refactoring Towards Batch Gradient Descent.vtt 7.3 kB
  • 4. Applications of Tensorflow/1. KNN with Regression.vtt 7.2 kB
  • 12. Image Recognition In Action/2. Greyscale Values.vtt 7.2 kB
  • 2. Algorithm Overview/10. Gauging Accuracy.vtt 7.2 kB
  • 6. Gradient Descent with Tensorflow/2. Data Loading.vtt 7.0 kB
  • 14. Appendix Custom CSV Loader/8. Extracting Data Columns.vtt 7.0 kB
  • 5. Getting Started with Gradient Descent/2. Why Linear Regression.vtt 6.9 kB
  • 1. What is Machine Learning/9. What Type of Problem.vtt 6.9 kB
  • 2. Algorithm Overview/12. Refactoring Accuracy Reporting.vtt 6.9 kB
  • 11. Multi-Value Classification/5. Refactoring to Multi-Column Weights.vtt 6.8 kB
  • 13. Performance Optimization/2. Minimizing Memory Usage.vtt 6.8 kB
  • 5. Getting Started with Gradient Descent/11. Gradient Descent with Multiple Terms.vtt 6.7 kB
  • 11. Multi-Value Classification/11. Refactoring Sigmoid to Softmax.vtt 6.7 kB
  • 10. Natural Binary Classification/4. The Sigmoid Equation.vtt 6.6 kB
  • 11. Multi-Value Classification/6. A Problem to Test Multinominal Classification.vtt 6.5 kB
  • 13. Performance Optimization/19. Fixing Cost History.vtt 6.4 kB
  • 2. Algorithm Overview/5. Testing the Algorithm.vtt 6.4 kB
  • 8. Plotting Data with Javascript/3. Plotting MSE History against B Values.vtt 6.4 kB
  • 11. Multi-Value Classification/7. Classifying Continuous Values.vtt 6.3 kB
  • 13. Performance Optimization/1. Handing Large Datasets.vtt 6.3 kB
  • 7. Increasing Performance with Vectorized Solutions/8. Reminder on Standardization.vtt 6.3 kB
  • 13. Performance Optimization/10. Tensorflow's Eager Memory Usage.vtt 6.3 kB
  • 2. Algorithm Overview/21. Applying Normalization.vtt 6.2 kB
  • 10. Natural Binary Classification/10. Encoding Label Values.vtt 6.2 kB
  • 13. Performance Optimization/18. NaN in Cost History.vtt 6.2 kB
  • 10. Natural Binary Classification/12. The Sigmoid Equation with Logistic Regression.vtt 6.1 kB
  • 10. Natural Binary Classification/19. Finishing the Cost Refactor.vtt 6.1 kB
  • 8. Plotting Data with Javascript/1. Observing Changing Learning Rate and MSE.vtt 6.1 kB
  • 13. Performance Optimization/21. Improving Model Accuracy.vtt 6.1 kB
  • 1. What is Machine Learning/6. Identifying Relevant Data.vtt 6.0 kB
  • 10. Natural Binary Classification/9. Importing Vehicle Data.vtt 6.0 kB
  • 2. Algorithm Overview/6. Interpreting Bad Results.vtt 5.9 kB
  • 13. Performance Optimization/17. Plotting Cost History.vtt 5.9 kB
  • 4. Applications of Tensorflow/2. A Change in Data Structure.vtt 5.9 kB
  • 14. Appendix Custom CSV Loader/7. Custom Value Parsing.vtt 5.9 kB
  • 4. Applications of Tensorflow/15. What Now.vtt 5.8 kB
  • 2. Algorithm Overview/15. Multi-Dimensional KNN.vtt 5.7 kB
  • 3. Onwards to Tensorflow JS!/8. Logging Tensor Data.vtt 5.6 kB
  • 13. Performance Optimization/13. Tidying the Training Loop.vtt 5.6 kB
  • 13. Performance Optimization/8. Measuring Footprint Reduction.vtt 5.6 kB
  • 4. Applications of Tensorflow/13. Applying Standardization.vtt 5.5 kB
  • 2. Algorithm Overview/7. Test and Training Data.vtt 5.5 kB
  • 1. What is Machine Learning/8. Recording Observation Data.vtt 5.5 kB
  • 5. Getting Started with Gradient Descent/10. Answering Common Questions.vtt 5.4 kB
  • 11. Multi-Value Classification/3. A Smarter Refactor!.vtt 5.4 kB
  • 2. Algorithm Overview/8. Randomizing Test Data.vtt 5.1 kB
  • 2. Algorithm Overview/9. Generalizing KNN.vtt 5.1 kB
  • 10. Natural Binary Classification/20. Plotting Changing Cost History.vtt 5.1 kB
  • 7. Increasing Performance with Vectorized Solutions/9. Data Processing in a Helper Method.vtt 5.0 kB
  • 10. Natural Binary Classification/14. Gauging Classification Accuracy.vtt 4.9 kB
  • 7. Increasing Performance with Vectorized Solutions/4. Same Results Or Not.vtt 4.9 kB
  • 14. Appendix Custom CSV Loader/6. Parsing Number Values.vtt 4.9 kB
  • 13. Performance Optimization/12. Implementing TF Tidy.vtt 4.9 kB
  • 12. Image Recognition In Action/3. Many Features.vtt 4.8 kB
  • 4. Applications of Tensorflow/7. Moving to the Editor.vtt 4.8 kB
  • 11. Multi-Value Classification/13. Calculating Accuracy.vtt 4.6 kB
  • 6. Gradient Descent with Tensorflow/4. Formulating the Training Loop.vtt 4.5 kB
  • 6. Gradient Descent with Tensorflow/7. Updating Coefficients.vtt 4.5 kB
  • 2. Algorithm Overview/11. Printing a Report.vtt 4.5 kB
  • 13. Performance Optimization/7. Releasing References.vtt 4.5 kB
  • 1. What is Machine Learning/5. Problem Outline.vtt 4.4 kB
  • 7. Increasing Performance with Vectorized Solutions/12. Massaging Learning Rates.vtt 4.3 kB
  • 5. Getting Started with Gradient Descent/1. Linear Regression.vtt 4.0 kB
  • 13. Performance Optimization/16. Final Memory Report.vtt 4.0 kB
  • 14. Appendix Custom CSV Loader/3. Reading Files from Disk.vtt 4.0 kB
  • 13. Performance Optimization/11. Cleaning up Tensors with Tidy.vtt 4.0 kB
  • 11. Multi-Value Classification/12. Implementing Accuracy Gauges.vtt 3.9 kB
  • 14. Appendix Custom CSV Loader/4. Splitting into Columns.vtt 3.8 kB
  • 2. Algorithm Overview/24. Evaluating Different Feature Values.vtt 3.8 kB
  • 12. Image Recognition In Action/10. Backfilling Variance.vtt 3.7 kB
  • 10. Natural Binary Classification/1. Introducing Logistic Regression.vtt 3.5 kB
  • 14. Appendix Custom CSV Loader/5. Dropping Trailing Columns.vtt 3.5 kB
  • 13. Performance Optimization/15. One More Optimization.vtt 3.4 kB
  • 11. Multi-Value Classification/1. Multinominal Logistic Regression.vtt 3.2 kB
  • 12. Image Recognition In Action/1. Handwriting Recognition.vtt 3.2 kB
  • 1. What is Machine Learning/4. App Setup.vtt 3.1 kB
  • 14. Appendix Custom CSV Loader/1. Loading CSV Files.vtt 3.0 kB
  • 12. Image Recognition In Action/7. Unchanging Accuracy.vtt 3.0 kB
  • 14. Appendix Custom CSV Loader/2. A Test Dataset.vtt 2.6 kB
  • 13. Performance Optimization/20. Massaging Learning Parameters.vtt 2.5 kB
  • 13. Performance Optimization/9. Optimization Tensorflow Memory Usage.vtt 2.4 kB
  • 13. Performance Optimization/14. Measuring Reduced Memory Usage.vtt 2.2 kB
  • 10. Natural Binary Classification/6. Changes for Logistic Regression.vtt 1.8 kB
  • 1. What is Machine Learning/1. Getting Started - How to Get Help.vtt 1.6 kB
  • 10. Natural Binary Classification/8. Project Download.html 215 Bytes
  • 3. Onwards to Tensorflow JS!/4. Tensor Dimension and Shapes.html 139 Bytes
  • 3. Onwards to Tensorflow JS!/7. Broadcasting Elementwise Operations.html 139 Bytes
  • [FreeCourseLab.com].url 126 Bytes

温馨提示

本站不存储任何资源内容，只收集BT种子元数据(例如文件名和文件大小)和磁力链接(BT种子标识符)，并提供查询服务，是一个完全合法的搜索引擎系统。网站不提供种子下载服务，用户可以通过第三方链接或磁力链接获取到相关的种子资源。本站也不对BT种子真实性及合法性负责，请用户注意甄别！