[FreeCourseWorld.Com] Udemy - Machine Learning with Javascript

磁力链接/BT种子名称

[FreeCourseWorld.Com] Udemy - Machine Learning with Javascript

磁力链接/BT种子简介

种子哈希：436a4eac8874c54c2bfdbc0adbe6af1a5c9539f6
文件大小： 10.1G
已经下载：2333次
下载速度：极快
收录时间：2021-04-03
最近下载：2024-11-29

移花宫入口

移花宫.com邀月.com怜星.com花无缺.comyhgbt.icuyhgbt.top

磁力链接下载

magnet:?xt=urn:btih:436A4EAC8874C54C2BFDBC0ADBE6AF1A5C9539F6
推荐使用PIKPAK网盘下载资源，10TB超大空间，不限制资源，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 PIKPAK在线播放 91视频 含羞草 欲漫涩 逼哩逼哩 成人快手 51品茶 抖阴破解版 暗网禁地 91短视频 TikTok成人版 PornHub 草榴社区 乱伦社区

最近搜索

文件列表

  • 05 Getting Started with Gradient Descent/068 Why a Learning Rate.mp4 196.4 MB
  • 06 Gradient Descent with Tensorflow/084 How it All Works Together.mp4 150.8 MB
  • 02 Algorithm Overview/022 Investigating Optimal K Values.mp4 135.4 MB
  • 05 Getting Started with Gradient Descent/062 Understanding Gradient Descent.mp4 132.9 MB
  • 05 Getting Started with Gradient Descent/071 Multiple Terms in Action.mp4 129.1 MB
  • 07 Increasing Performance with Vectorized Solutions/097 Moving Towards Multivariate Regression.mp4 127.3 MB
  • 05 Getting Started with Gradient Descent/066 Gradient Descent in Action.mp4 121.0 MB
  • 03 Onwards to Tensorflow JS/036 Tensor Shape and Dimension.mp4 119.8 MB
  • 01 What is Machine Learning/003 A Complete Walkthrough.mp4 114.4 MB
  • 11 Multi-Value Classification/134 A Single Instance Approach.mp4 108.6 MB
  • 06 Gradient Descent with Tensorflow/079 Interpreting Results.mp4 106.7 MB
  • 13 Performance Optimization/159 Measuring Memory Usage.mp4 101.3 MB
  • 11 Multi-Value Classification/139 Marginal vs Conditional Probability.mp4 99.8 MB
  • 05 Getting Started with Gradient Descent/063 Guessing Coefficients with MSE.mp4 98.0 MB
  • 02 Algorithm Overview/010 How K-Nearest Neighbor Works.mp4 97.9 MB
  • 04 Applications of Tensorflow/055 Normalization or Standardization.mp4 97.5 MB
  • 06 Gradient Descent with Tensorflow/083 Simplification with Matrix Multiplication.mp4 95.2 MB
  • 04 Applications of Tensorflow/052 Loading CSV Data.mp4 93.7 MB
  • 12 Image Recognition In Action/151 Debugging the Calculation Process.mp4 93.4 MB
  • 06 Gradient Descent with Tensorflow/076 Initial Gradient Descent Implementation.mp4 92.2 MB
  • 10 Natural Binary Classification/123 A Touch More Refactoring.mp4 91.7 MB
  • 04 Applications of Tensorflow/058 Debugging Calculations.mp4 90.9 MB
  • 07 Increasing Performance with Vectorized Solutions/086 Refactoring to One Equation.mp4 88.9 MB
  • 07 Increasing Performance with Vectorized Solutions/098 Refactoring for Multivariate Analysis.mp4 86.4 MB
  • 07 Increasing Performance with Vectorized Solutions/089 Calculating Model Accuracy.mp4 84.3 MB
  • 02 Algorithm Overview/031 Feature Selection with KNN.mp4 84.3 MB
  • 12 Image Recognition In Action/149 Implementing an Accuracy Gauge.mp4 83.8 MB
  • 09 Gradient Descent Alterations/110 Making Predictions with the Model.mp4 83.3 MB
  • 10 Natural Binary Classification/115 Decision Boundaries.mp4 83.0 MB
  • 02 Algorithm Overview/025 N-Dimension Distance.mp4 82.7 MB
  • 04 Applications of Tensorflow/047 KNN with Tensorflow.mp4 82.5 MB
  • 05 Getting Started with Gradient Descent/065 Derivatives.mp4 81.7 MB
  • 09 Gradient Descent Alterations/105 Batch and Stochastic Gradient Descent.mp4 81.0 MB
  • 07 Increasing Performance with Vectorized Solutions/099 Learning Rate Optimization.mp4 80.4 MB
  • 03 Onwards to Tensorflow JS/034 Lets Get Our Bearings.mp4 80.3 MB
  • 07 Increasing Performance with Vectorized Solutions/090 Implementing Coefficient of Determination.mp4 79.5 MB
  • 14 Appendix Custom CSV Loader/184 Splitting Test and Training.mp4 79.3 MB
  • 02 Algorithm Overview/028 Feature Normalization.mp4 76.4 MB
  • 07 Increasing Performance with Vectorized Solutions/085 Refactoring the Linear Regression Class.mp4 76.2 MB
  • 07 Increasing Performance with Vectorized Solutions/091 Dealing with Bad Accuracy.mp4 74.9 MB
  • 02 Algorithm Overview/026 Arbitrary Feature Spaces.mp4 74.7 MB
  • 02 Algorithm Overview/023 Updating KNN for Multiple Features.mp4 74.0 MB
  • 10 Natural Binary Classification/121 Updating Linear Regression for Logistic Regression.mp4 73.7 MB
  • 10 Natural Binary Classification/126 Variable Decision Boundaries.mp4 71.6 MB
  • 06 Gradient Descent with Tensorflow/080 Matrix Multiplication.mp4 70.7 MB
  • 09 Gradient Descent Alterations/108 Iterating Over Batches.mp4 70.7 MB
  • 06 Gradient Descent with Tensorflow/077 Calculating MSE Slopes.mp4 70.4 MB
  • 02 Algorithm Overview/029 Normalization with MinMax.mp4 70.3 MB
  • 09 Gradient Descent Alterations/109 Evaluating Batch Gradient Descent Results.mp4 69.5 MB
  • 07 Increasing Performance with Vectorized Solutions/087 A Few More Changes.mp4 69.4 MB
  • 11 Multi-Value Classification/138 Training a Multinominal Model.mp4 69.3 MB
  • 09 Gradient Descent Alterations/107 Determining Batch Size and Quantity.mp4 69.3 MB
  • 02 Algorithm Overview/032 Objective Feature Picking.mp4 69.2 MB
  • 05 Getting Started with Gradient Descent/067 Quick Breather and Review.mp4 69.0 MB
  • 02 Algorithm Overview/011 Lodash Review.mp4 68.1 MB
  • 04 Applications of Tensorflow/054 Reporting Error Percentages.mp4 67.6 MB
  • 02 Algorithm Overview/027 Magnitude Offsets in Features.mp4 67.2 MB
  • 06 Gradient Descent with Tensorflow/081 More on Matrix Multiplication.mp4 66.3 MB
  • 04 Applications of Tensorflow/049 Sorting Tensors.mp4 65.9 MB
  • 01 What is Machine Learning/002 Solving Machine Learning Problems.mp4 65.8 MB
  • 11 Multi-Value Classification/140 Sigmoid vs Softmax.mp4 65.8 MB
  • 06 Gradient Descent with Tensorflow/074 Default Algorithm Options.mp4 65.7 MB
  • 07 Increasing Performance with Vectorized Solutions/101 Updating Learning Rate.mp4 65.2 MB
  • 03 Onwards to Tensorflow JS/038 Broadcasting Operations.mp4 65.1 MB
  • 12 Image Recognition In Action/148 Encoding Label Values.mp4 65.0 MB
  • 08 Plotting Data with Javascript/103 Plotting MSE Values.mp4 64.4 MB
  • 10 Natural Binary Classification/112 Logistic Regression in Action.mp4 64.0 MB
  • 10 Natural Binary Classification/127 Mean Squared Error vs Cross Entropy.mp4 63.1 MB
  • 06 Gradient Descent with Tensorflow/082 Matrix Form of Slope Equations.mp4 62.5 MB
  • 10 Natural Binary Classification/117 Project Setup for Logistic Regression.mp4 62.3 MB
  • 02 Algorithm Overview/012 Implementing KNN.mp4 62.2 MB
  • 03 Onwards to Tensorflow JS/041 Creating Slices of Data.mp4 61.8 MB
  • 03 Onwards to Tensorflow JS/037 Elementwise Operations.mp4 61.2 MB
  • 04 Applications of Tensorflow/050 Averaging Top Values.mp4 61.0 MB
  • 07 Increasing Performance with Vectorized Solutions/094 Reapplying Standardization.mp4 60.8 MB
  • 12 Image Recognition In Action/147 Flattening Image Data.mp4 60.6 MB
  • 04 Applications of Tensorflow/048 Maintaining Order Relationships.mp4 60.6 MB
  • 14 Appendix Custom CSV Loader/182 Extracting Data Columns.mp4 60.0 MB
  • 06 Gradient Descent with Tensorflow/072 Project Overview.mp4 59.8 MB
  • 03 Onwards to Tensorflow JS/044 Massaging Dimensions with ExpandDims.mp4 59.8 MB
  • 13 Performance Optimization/158 Shallow vs Retained Memory Usage.mp4 59.7 MB
  • 05 Getting Started with Gradient Descent/064 Observations Around MSE.mp4 58.8 MB
  • 13 Performance Optimization/157 The Javascript Garbage Collector.mp4 58.5 MB
  • 10 Natural Binary Classification/113 Bad Equation Fits.mp4 58.1 MB
  • 12 Image Recognition In Action/145 Greyscale Values.mp4 58.0 MB
  • 09 Gradient Descent Alterations/106 Refactoring Towards Batch Gradient Descent.mp4 57.8 MB
  • 13 Performance Optimization/174 Improving Model Accuracy.mp4 57.7 MB
  • 04 Applications of Tensorflow/045 KNN with Regression.mp4 57.6 MB
  • 10 Natural Binary Classification/125 Implementing a Test Function.mp4 57.4 MB
  • 02 Algorithm Overview/019 Gauging Accuracy.mp4 56.6 MB
  • 04 Applications of Tensorflow/056 Numerical Standardization with Tensorflow.mp4 55.6 MB
  • 04 Applications of Tensorflow/053 Running an Analysis.mp4 55.0 MB
  • 02 Algorithm Overview/021 Refactoring Accuracy Reporting.mp4 54.8 MB
  • 14 Appendix Custom CSV Loader/183 Shuffling Data via Seed Phrase.mp4 54.7 MB
  • 07 Increasing Performance with Vectorized Solutions/100 Recording MSE History.mp4 54.5 MB
  • 05 Getting Started with Gradient Descent/061 Why Linear Regression.mp4 52.8 MB
  • 02 Algorithm Overview/013 Finishing KNN Implementation.mp4 52.7 MB
  • 11 Multi-Value Classification/132 A Smart Refactor to Multinominal Analysis.mp4 52.4 MB
  • 10 Natural Binary Classification/128 Refactoring with Cross Entropy.mp4 51.8 MB
  • 10 Natural Binary Classification/129 Finishing the Cost Refactor.mp4 51.5 MB
  • 13 Performance Optimization/156 Creating Memory Snapshots.mp4 51.4 MB
  • 11 Multi-Value Classification/141 Refactoring Sigmoid to Softmax.mp4 51.2 MB
  • 03 Onwards to Tensorflow JS/035 A Plan to Move Forward.mp4 51.0 MB
  • 10 Natural Binary Classification/120 Encoding Label Values.mp4 50.9 MB
  • 11 Multi-Value Classification/135 Refactoring to Multi-Column Weights.mp4 50.8 MB
  • 11 Multi-Value Classification/136 A Problem to Test Multinominal Classification.mp4 50.8 MB
  • 01 What is Machine Learning/007 Dataset Structures.mp4 50.6 MB
  • 12 Image Recognition In Action/152 Dealing with Zero Variances.mp4 50.2 MB
  • 07 Increasing Performance with Vectorized Solutions/095 Fixing Standardization Issues.mp4 50.2 MB
  • 08 Plotting Data with Javascript/104 Plotting MSE History against B Values.mp4 50.1 MB
  • 13 Performance Optimization/170 Plotting Cost History.mp4 49.9 MB
  • 01 What is Machine Learning/009 What Type of Problem.mp4 49.3 MB
  • 13 Performance Optimization/163 Tensorflows Eager Memory Usage.mp4 49.1 MB
  • 13 Performance Optimization/172 Fixing Cost History.mp4 49.0 MB
  • 13 Performance Optimization/171 NaN in Cost History.mp4 48.6 MB
  • 13 Performance Optimization/166 Tidying the Training Loop.mp4 48.2 MB
  • 08 Plotting Data with Javascript/102 Observing Changing Learning Rate and MSE.mp4 48.1 MB
  • 10 Natural Binary Classification/114 The Sigmoid Equation.mp4 47.7 MB
  • 02 Algorithm Overview/030 Applying Normalization.mp4 47.6 MB
  • 02 Algorithm Overview/016 Test and Training Data.mp4 47.4 MB
  • 02 Algorithm Overview/014 Testing the Algorithm.mp4 47.1 MB
  • 12 Image Recognition In Action/146 Many Features.mp4 46.9 MB
  • 11 Multi-Value Classification/137 Classifying Continuous Values.mp4 46.7 MB
  • 07 Increasing Performance with Vectorized Solutions/092 Reminder on Standardization.mp4 46.6 MB
  • 13 Performance Optimization/154 Handing Large Datasets.mp4 46.6 MB
  • 02 Algorithm Overview/024 Multi-Dimensional KNN.mp4 46.4 MB
  • 05 Getting Started with Gradient Descent/070 Gradient Descent with Multiple Terms.mp4 46.3 MB
  • 03 Onwards to Tensorflow JS/042 Tensor Concatenation.mp4 46.3 MB
  • 06 Gradient Descent with Tensorflow/073 Data Loading.mp4 45.6 MB
  • 13 Performance Optimization/161 Measuring Footprint Reduction.mp4 45.4 MB
  • 10 Natural Binary Classification/130 Plotting Changing Cost History.mp4 45.0 MB
  • 04 Applications of Tensorflow/059 What Now.mp4 44.4 MB
  • 04 Applications of Tensorflow/057 Applying Standardization.mp4 43.5 MB
  • 03 Onwards to Tensorflow JS/043 Summing Values Along an Axis.mp4 43.4 MB
  • 04 Applications of Tensorflow/046 A Change in Data Structure.mp4 43.4 MB
  • 05 Getting Started with Gradient Descent/069 Answering Common Questions.mp4 42.9 MB
  • 02 Algorithm Overview/015 Interpreting Bad Results.mp4 42.7 MB
  • 02 Algorithm Overview/018 Generalizing KNN.mp4 40.9 MB
  • 10 Natural Binary Classification/119 Importing Vehicle Data.mp4 40.8 MB
  • 11 Multi-Value Classification/133 A Smarter Refactor.mp4 40.2 MB
  • 13 Performance Optimization/155 Minimizing Memory Usage.mp4 40.0 MB
  • 13 Performance Optimization/165 Implementing TF Tidy.mp4 39.4 MB
  • 07 Increasing Performance with Vectorized Solutions/093 Data Processing in a Helper Method.mp4 39.0 MB
  • 14 Appendix Custom CSV Loader/181 Custom Value Parsing.mp4 38.5 MB
  • 10 Natural Binary Classification/124 Gauging Classification Accuracy.mp4 38.5 MB
  • 07 Increasing Performance with Vectorized Solutions/096 Massaging Learning Rates.mp4 38.2 MB
  • 13 Performance Optimization/169 Final Memory Report.mp4 38.0 MB
  • 02 Algorithm Overview/017 Randomizing Test Data.mp4 37.7 MB
  • 13 Performance Optimization/160 Releasing References.mp4 37.7 MB
  • 04 Applications of Tensorflow/051 Moving to the Editor.mp4 36.0 MB
  • 01 What is Machine Learning/006 Identifying Relevant Data.mp4 35.6 MB
  • 06 Gradient Descent with Tensorflow/078 Updating Coefficients.mp4 35.5 MB
  • 07 Increasing Performance with Vectorized Solutions/088 Same Results Or Not.mp4 35.5 MB
  • 02 Algorithm Overview/020 Printing a Report.mp4 34.9 MB
  • 10 Natural Binary Classification/122 The Sigmoid Equation with Logistic Regression.mp4 34.4 MB
  • 01 What is Machine Learning/008 Recording Observation Data.mp4 34.3 MB
  • 14 Appendix Custom CSV Loader/180 Parsing Number Values.mp4 32.9 MB
  • 11 Multi-Value Classification/143 Calculating Accuracy.mp4 32.8 MB
  • 01 What is Machine Learning/005 Problem Outline.mp4 32.7 MB
  • 03 Onwards to Tensorflow JS/040 Tensor Accessors.mp4 31.9 MB
  • 11 Multi-Value Classification/142 Implementing Accuracy Gauges.mp4 30.1 MB
  • 02 Algorithm Overview/033 Evaluating Different Feature Values.mp4 29.3 MB
  • 06 Gradient Descent with Tensorflow/075 Formulating the Training Loop.mp4 29.0 MB
  • 13 Performance Optimization/168 One More Optimization.mp4 28.8 MB
  • 03 Onwards to Tensorflow JS/039 Logging Tensor Data.mp4 27.3 MB
  • 12 Image Recognition In Action/153 Backfilling Variance.mp4 27.0 MB
  • 05 Getting Started with Gradient Descent/060 Linear Regression.mp4 26.6 MB
  • 11 Multi-Value Classification/131 Multinominal Logistic Regression.mp4 26.2 MB
  • 12 Image Recognition In Action/144 Handwriting Recognition.mp4 25.9 MB
  • 13 Performance Optimization/164 Cleaning up Tensors with Tidy.mp4 25.4 MB
  • 10 Natural Binary Classification/111 Introducing Logistic Regression.mp4 24.6 MB
  • 13 Performance Optimization/173 Massaging Learning Parameters.mp4 23.6 MB
  • 14 Appendix Custom CSV Loader/178 Splitting into Columns.mp4 21.3 MB
  • 12 Image Recognition In Action/150 Unchanging Accuracy.mp4 21.3 MB
  • 01 What is Machine Learning/004 App Setup.mp4 20.2 MB
  • 14 Appendix Custom CSV Loader/177 Reading Files from Disk.mp4 19.5 MB
  • 13 Performance Optimization/162 Optimization Tensorflow Memory Usage.mp4 19.4 MB
  • 14 Appendix Custom CSV Loader/179 Dropping Trailing Columns.mp4 19.3 MB
  • 13 Performance Optimization/167 Measuring Reduced Memory Usage.mp4 19.0 MB
  • 14 Appendix Custom CSV Loader/175 Loading CSV Files.mp4 16.6 MB
  • 10 Natural Binary Classification/116 Changes for Logistic Regression.mp4 13.1 MB
  • 14 Appendix Custom CSV Loader/176 A Test Dataset.mp4 10.0 MB
  • 01 What is Machine Learning/001 Getting Started - How to Get Help.mp4 8.8 MB
  • 10 Natural Binary Classification/118 regressions.zip 35.1 kB
  • 05 Getting Started with Gradient Descent/068 Why a Learning Rate.id.srt 28.9 kB
  • 05 Getting Started with Gradient Descent/068 Why a Learning Rate.en.srt 26.6 kB
  • 06 Gradient Descent with Tensorflow/084 How it All Works Together.id.srt 22.2 kB
  • 06 Gradient Descent with Tensorflow/084 How it All Works Together.en.srt 21.4 kB
  • 05 Getting Started with Gradient Descent/062 Understanding Gradient Descent.id.srt 21.0 kB
  • 03 Onwards to Tensorflow JS/036 Tensor Shape and Dimension.id.srt 20.1 kB
  • 05 Getting Started with Gradient Descent/062 Understanding Gradient Descent.en.srt 19.9 kB
  • 05 Getting Started with Gradient Descent/066 Gradient Descent in Action.id.srt 19.9 kB
  • 07 Increasing Performance with Vectorized Solutions/097 Moving Towards Multivariate Regression.id.srt 19.6 kB
  • 03 Onwards to Tensorflow JS/036 Tensor Shape and Dimension.en.srt 19.5 kB
  • 02 Algorithm Overview/022 Investigating Optimal K Values.id.srt 19.5 kB
  • 05 Getting Started with Gradient Descent/066 Gradient Descent in Action.en.srt 18.9 kB
  • 07 Increasing Performance with Vectorized Solutions/097 Moving Towards Multivariate Regression.en.srt 18.6 kB
  • 02 Algorithm Overview/022 Investigating Optimal K Values.en.srt 18.5 kB
  • 05 Getting Started with Gradient Descent/071 Multiple Terms in Action.id.srt 17.9 kB
  • 11 Multi-Value Classification/139 Marginal vs Conditional Probability.id.srt 17.3 kB
  • 05 Getting Started with Gradient Descent/071 Multiple Terms in Action.en.srt 16.9 kB
  • 06 Gradient Descent with Tensorflow/079 Interpreting Results.id.srt 16.9 kB
  • 02 Algorithm Overview/011 Lodash Review.id.srt 16.8 kB
  • 05 Getting Started with Gradient Descent/063 Guessing Coefficients with MSE.id.srt 16.8 kB
  • 11 Multi-Value Classification/139 Marginal vs Conditional Probability.en.srt 16.4 kB
  • 02 Algorithm Overview/025 N-Dimension Distance.id.srt 16.4 kB
  • 01 What is Machine Learning/003 A Complete Walkthrough.id.srt 16.2 kB
  • 11 Multi-Value Classification/134 A Single Instance Approach.id.srt 16.1 kB
  • 04 Applications of Tensorflow/052 Loading CSV Data.id.srt 16.1 kB
  • 04 Applications of Tensorflow/047 KNN with Tensorflow.id.srt 16.0 kB
  • 06 Gradient Descent with Tensorflow/079 Interpreting Results.en.srt 15.8 kB
  • 05 Getting Started with Gradient Descent/063 Guessing Coefficients with MSE.en.srt 15.8 kB
  • 11 Multi-Value Classification/134 A Single Instance Approach.en.srt 15.7 kB
  • 02 Algorithm Overview/025 N-Dimension Distance.en.srt 15.6 kB
  • 02 Algorithm Overview/011 Lodash Review.en.srt 15.6 kB
  • 06 Gradient Descent with Tensorflow/083 Simplification with Matrix Multiplication.id.srt 15.6 kB
  • 01 What is Machine Learning/003 A Complete Walkthrough.en.srt 15.5 kB
  • 04 Applications of Tensorflow/052 Loading CSV Data.en.srt 15.4 kB
  • 04 Applications of Tensorflow/047 KNN with Tensorflow.en.srt 15.3 kB
  • 06 Gradient Descent with Tensorflow/076 Initial Gradient Descent Implementation.id.srt 15.2 kB
  • 07 Increasing Performance with Vectorized Solutions/086 Refactoring to One Equation.id.srt 15.1 kB
  • 13 Performance Optimization/159 Measuring Memory Usage.id.srt 15.0 kB
  • 06 Gradient Descent with Tensorflow/083 Simplification with Matrix Multiplication.en.srt 14.8 kB
  • 06 Gradient Descent with Tensorflow/076 Initial Gradient Descent Implementation.en.srt 14.6 kB
  • 07 Increasing Performance with Vectorized Solutions/086 Refactoring to One Equation.en.srt 14.2 kB
  • 02 Algorithm Overview/026 Arbitrary Feature Spaces.id.srt 14.2 kB
  • 13 Performance Optimization/159 Measuring Memory Usage.en.srt 14.2 kB
  • 07 Increasing Performance with Vectorized Solutions/089 Calculating Model Accuracy.id.srt 14.1 kB
  • 02 Algorithm Overview/031 Feature Selection with KNN.id.srt 14.0 kB
  • 04 Applications of Tensorflow/058 Debugging Calculations.id.srt 13.9 kB
  • 02 Algorithm Overview/010 How K-Nearest Neighbor Works.id.srt 13.7 kB
  • 12 Image Recognition In Action/151 Debugging the Calculation Process.id.srt 13.7 kB
  • 02 Algorithm Overview/026 Arbitrary Feature Spaces.en.srt 13.7 kB
  • 07 Increasing Performance with Vectorized Solutions/099 Learning Rate Optimization.id.srt 13.7 kB
  • 07 Increasing Performance with Vectorized Solutions/089 Calculating Model Accuracy.en.srt 13.6 kB
  • 04 Applications of Tensorflow/058 Debugging Calculations.en.srt 13.3 kB
  • 02 Algorithm Overview/010 How K-Nearest Neighbor Works.en.srt 13.3 kB
  • 12 Image Recognition In Action/151 Debugging the Calculation Process.en.srt 13.3 kB
  • 06 Gradient Descent with Tensorflow/074 Default Algorithm Options.id.srt 13.3 kB
  • 04 Applications of Tensorflow/049 Sorting Tensors.id.srt 13.2 kB
  • 03 Onwards to Tensorflow JS/044 Massaging Dimensions with ExpandDims.id.srt 13.2 kB
  • 03 Onwards to Tensorflow JS/037 Elementwise Operations.id.srt 13.1 kB
  • 03 Onwards to Tensorflow JS/034 Lets Get Our Bearings.id.srt 13.1 kB
  • 02 Algorithm Overview/031 Feature Selection with KNN.en.srt 13.0 kB
  • 06 Gradient Descent with Tensorflow/074 Default Algorithm Options.en.srt 13.0 kB
  • 14 Appendix Custom CSV Loader/184 Splitting Test and Training.id.srt 13.0 kB
  • 09 Gradient Descent Alterations/108 Iterating Over Batches.id.srt 12.9 kB
  • 04 Applications of Tensorflow/050 Averaging Top Values.id.srt 12.8 kB
  • 07 Increasing Performance with Vectorized Solutions/099 Learning Rate Optimization.en.srt 12.8 kB
  • 10 Natural Binary Classification/115 Decision Boundaries.id.srt 12.8 kB
  • 07 Increasing Performance with Vectorized Solutions/098 Refactoring for Multivariate Analysis.id.srt 12.8 kB
  • 10 Natural Binary Classification/123 A Touch More Refactoring.id.srt 12.8 kB
  • 07 Increasing Performance with Vectorized Solutions/091 Dealing with Bad Accuracy.id.srt 12.7 kB
  • 09 Gradient Descent Alterations/110 Making Predictions with the Model.id.srt 12.7 kB
  • 03 Onwards to Tensorflow JS/044 Massaging Dimensions with ExpandDims.en.srt 12.6 kB
  • 03 Onwards to Tensorflow JS/034 Lets Get Our Bearings.en.srt 12.6 kB
  • 07 Increasing Performance with Vectorized Solutions/085 Refactoring the Linear Regression Class.id.srt 12.6 kB
  • 04 Applications of Tensorflow/056 Numerical Standardization with Tensorflow.id.srt 12.5 kB
  • 02 Algorithm Overview/028 Feature Normalization.id.srt 12.5 kB
  • 07 Increasing Performance with Vectorized Solutions/090 Implementing Coefficient of Determination.id.srt 12.5 kB
  • 10 Natural Binary Classification/126 Variable Decision Boundaries.id.srt 12.4 kB
  • 09 Gradient Descent Alterations/108 Iterating Over Batches.en.srt 12.4 kB
  • 04 Applications of Tensorflow/049 Sorting Tensors.en.srt 12.4 kB
  • 12 Image Recognition In Action/149 Implementing an Accuracy Gauge.id.srt 12.4 kB
  • 04 Applications of Tensorflow/055 Normalization or Standardization.id.srt 12.3 kB
  • 14 Appendix Custom CSV Loader/184 Splitting Test and Training.en.srt 12.3 kB
  • 09 Gradient Descent Alterations/105 Batch and Stochastic Gradient Descent.id.srt 12.3 kB
  • 07 Increasing Performance with Vectorized Solutions/098 Refactoring for Multivariate Analysis.en.srt 12.2 kB
  • 10 Natural Binary Classification/115 Decision Boundaries.en.srt 12.2 kB
  • 03 Onwards to Tensorflow JS/037 Elementwise Operations.en.srt 12.2 kB
  • 09 Gradient Descent Alterations/110 Making Predictions with the Model.en.srt 12.2 kB
  • 07 Increasing Performance with Vectorized Solutions/091 Dealing with Bad Accuracy.en.srt 12.2 kB
  • 04 Applications of Tensorflow/056 Numerical Standardization with Tensorflow.en.srt 12.1 kB
  • 10 Natural Binary Classification/123 A Touch More Refactoring.en.srt 12.1 kB
  • 03 Onwards to Tensorflow JS/041 Creating Slices of Data.id.srt 12.1 kB
  • 10 Natural Binary Classification/121 Updating Linear Regression for Logistic Regression.id.srt 12.1 kB
  • 04 Applications of Tensorflow/050 Averaging Top Values.en.srt 12.1 kB
  • 06 Gradient Descent with Tensorflow/080 Matrix Multiplication.id.srt 12.1 kB
  • 04 Applications of Tensorflow/055 Normalization or Standardization.en.srt 12.0 kB
  • 07 Increasing Performance with Vectorized Solutions/090 Implementing Coefficient of Determination.en.srt 12.0 kB
  • 02 Algorithm Overview/028 Feature Normalization.en.srt 11.9 kB
  • 07 Increasing Performance with Vectorized Solutions/085 Refactoring the Linear Regression Class.en.srt 11.9 kB
  • 03 Onwards to Tensorflow JS/041 Creating Slices of Data.en.srt 11.9 kB
  • 09 Gradient Descent Alterations/105 Batch and Stochastic Gradient Descent.en.srt 11.7 kB
  • 12 Image Recognition In Action/149 Implementing an Accuracy Gauge.en.srt 11.7 kB
  • 10 Natural Binary Classification/126 Variable Decision Boundaries.en.srt 11.7 kB
  • 06 Gradient Descent with Tensorflow/080 Matrix Multiplication.en.srt 11.6 kB
  • 05 Getting Started with Gradient Descent/065 Derivatives.id.srt 11.6 kB
  • 02 Algorithm Overview/012 Implementing KNN.id.srt 11.5 kB
  • 04 Applications of Tensorflow/048 Maintaining Order Relationships.id.srt 11.4 kB
  • 10 Natural Binary Classification/112 Logistic Regression in Action.id.srt 11.4 kB
  • 10 Natural Binary Classification/121 Updating Linear Regression for Logistic Regression.en.srt 11.4 kB
  • 03 Onwards to Tensorflow JS/038 Broadcasting Operations.id.srt 11.3 kB
  • 02 Algorithm Overview/029 Normalization with MinMax.id.srt 11.2 kB
  • 05 Getting Started with Gradient Descent/065 Derivatives.en.srt 11.2 kB
  • 10 Natural Binary Classification/112 Logistic Regression in Action.en.srt 11.1 kB
  • 07 Increasing Performance with Vectorized Solutions/101 Updating Learning Rate.id.srt 11.0 kB
  • 07 Increasing Performance with Vectorized Solutions/087 A Few More Changes.id.srt 11.0 kB
  • 03 Onwards to Tensorflow JS/038 Broadcasting Operations.en.srt 11.0 kB
  • 04 Applications of Tensorflow/048 Maintaining Order Relationships.en.srt 10.9 kB
  • 13 Performance Optimization/157 The Javascript Garbage Collector.id.srt 10.9 kB
  • 02 Algorithm Overview/023 Updating KNN for Multiple Features.id.srt 10.8 kB
  • 12 Image Recognition In Action/152 Dealing with Zero Variances.id.srt 10.8 kB
  • 02 Algorithm Overview/012 Implementing KNN.en.srt 10.8 kB
  • 02 Algorithm Overview/029 Normalization with MinMax.en.srt 10.6 kB
  • 02 Algorithm Overview/023 Updating KNN for Multiple Features.en.srt 10.5 kB
  • 11 Multi-Value Classification/138 Training a Multinominal Model.id.srt 10.5 kB
  • 11 Multi-Value Classification/140 Sigmoid vs Softmax.id.srt 10.5 kB
  • 06 Gradient Descent with Tensorflow/077 Calculating MSE Slopes.id.srt 10.4 kB
  • 13 Performance Optimization/157 The Javascript Garbage Collector.en.srt 10.4 kB
  • 06 Gradient Descent with Tensorflow/082 Matrix Form of Slope Equations.id.srt 10.4 kB
  • 07 Increasing Performance with Vectorized Solutions/087 A Few More Changes.en.srt 10.3 kB
  • 07 Increasing Performance with Vectorized Solutions/101 Updating Learning Rate.en.srt 10.3 kB
  • 12 Image Recognition In Action/152 Dealing with Zero Variances.en.srt 10.2 kB
  • 02 Algorithm Overview/032 Objective Feature Picking.id.srt 10.2 kB
  • 04 Applications of Tensorflow/054 Reporting Error Percentages.id.srt 10.2 kB
  • 05 Getting Started with Gradient Descent/067 Quick Breather and Review.id.srt 10.1 kB
  • 11 Multi-Value Classification/138 Training a Multinominal Model.en.srt 10.1 kB
  • 11 Multi-Value Classification/140 Sigmoid vs Softmax.en.srt 10.1 kB
  • 06 Gradient Descent with Tensorflow/081 More on Matrix Multiplication.id.srt 10.1 kB
  • 09 Gradient Descent Alterations/109 Evaluating Batch Gradient Descent Results.id.srt 10.0 kB
  • 01 What is Machine Learning/002 Solving Machine Learning Problems.id.srt 10.0 kB
  • 04 Applications of Tensorflow/053 Running an Analysis.id.srt 9.9 kB
  • 05 Getting Started with Gradient Descent/064 Observations Around MSE.id.srt 9.9 kB
  • 06 Gradient Descent with Tensorflow/072 Project Overview.id.srt 9.9 kB
  • 06 Gradient Descent with Tensorflow/077 Calculating MSE Slopes.en.srt 9.9 kB
  • 06 Gradient Descent with Tensorflow/082 Matrix Form of Slope Equations.en.srt 9.8 kB
  • 10 Natural Binary Classification/117 Project Setup for Logistic Regression.id.srt 9.8 kB
  • 13 Performance Optimization/158 Shallow vs Retained Memory Usage.id.srt 9.8 kB
  • 01 What is Machine Learning/007 Dataset Structures.id.srt 9.7 kB
  • 06 Gradient Descent with Tensorflow/072 Project Overview.en.srt 9.7 kB
  • 06 Gradient Descent with Tensorflow/081 More on Matrix Multiplication.en.srt 9.7 kB
  • 02 Algorithm Overview/032 Objective Feature Picking.en.srt 9.6 kB
  • 07 Increasing Performance with Vectorized Solutions/095 Fixing Standardization Issues.id.srt 9.6 kB
  • 02 Algorithm Overview/013 Finishing KNN Implementation.id.srt 9.6 kB
  • 04 Applications of Tensorflow/053 Running an Analysis.en.srt 9.6 kB
  • 04 Applications of Tensorflow/054 Reporting Error Percentages.en.srt 9.5 kB
  • 05 Getting Started with Gradient Descent/064 Observations Around MSE.en.srt 9.5 kB
  • 10 Natural Binary Classification/127 Mean Squared Error vs Cross Entropy.id.srt 9.5 kB
  • 01 What is Machine Learning/002 Solving Machine Learning Problems.en.srt 9.5 kB
  • 10 Natural Binary Classification/117 Project Setup for Logistic Regression.en.srt 9.5 kB
  • 12 Image Recognition In Action/147 Flattening Image Data.id.srt 9.5 kB
  • 01 What is Machine Learning/007 Dataset Structures.en.srt 9.4 kB
  • 05 Getting Started with Gradient Descent/067 Quick Breather and Review.en.srt 9.4 kB
  • 10 Natural Binary Classification/113 Bad Equation Fits.id.srt 9.4 kB
  • 09 Gradient Descent Alterations/107 Determining Batch Size and Quantity.id.srt 9.4 kB
  • 13 Performance Optimization/158 Shallow vs Retained Memory Usage.en.srt 9.3 kB
  • 09 Gradient Descent Alterations/109 Evaluating Batch Gradient Descent Results.en.srt 9.3 kB
  • 07 Increasing Performance with Vectorized Solutions/094 Reapplying Standardization.id.srt 9.3 kB
  • 10 Natural Binary Classification/125 Implementing a Test Function.id.srt 9.2 kB
  • 03 Onwards to Tensorflow JS/040 Tensor Accessors.id.srt 9.2 kB
  • 02 Algorithm Overview/027 Magnitude Offsets in Features.id.srt 9.2 kB
  • 14 Appendix Custom CSV Loader/183 Shuffling Data via Seed Phrase.id.srt 9.2 kB
  • 12 Image Recognition In Action/148 Encoding Label Values.id.srt 9.2 kB
  • 07 Increasing Performance with Vectorized Solutions/095 Fixing Standardization Issues.en.srt 9.2 kB
  • 10 Natural Binary Classification/127 Mean Squared Error vs Cross Entropy.en.srt 9.1 kB
  • 12 Image Recognition In Action/147 Flattening Image Data.en.srt 9.0 kB
  • 08 Plotting Data with Javascript/103 Plotting MSE Values.id.srt 9.0 kB
  • 02 Algorithm Overview/013 Finishing KNN Implementation.en.srt 9.0 kB
  • 09 Gradient Descent Alterations/107 Determining Batch Size and Quantity.en.srt 9.0 kB
  • 03 Onwards to Tensorflow JS/042 Tensor Concatenation.id.srt 9.0 kB
  • 11 Multi-Value Classification/132 A Smart Refactor to Multinominal Analysis.id.srt 9.0 kB
  • 02 Algorithm Overview/027 Magnitude Offsets in Features.en.srt 8.9 kB
  • 03 Onwards to Tensorflow JS/043 Summing Values Along an Axis.id.srt 8.9 kB
  • 10 Natural Binary Classification/113 Bad Equation Fits.en.srt 8.8 kB
  • 13 Performance Optimization/156 Creating Memory Snapshots.id.srt 8.8 kB
  • 07 Increasing Performance with Vectorized Solutions/100 Recording MSE History.id.srt 8.8 kB
  • 10 Natural Binary Classification/125 Implementing a Test Function.en.srt 8.8 kB
  • 03 Onwards to Tensorflow JS/040 Tensor Accessors.en.srt 8.8 kB
  • 02 Algorithm Overview/019 Gauging Accuracy.id.srt 8.7 kB
  • 03 Onwards to Tensorflow JS/042 Tensor Concatenation.en.srt 8.7 kB
  • 07 Increasing Performance with Vectorized Solutions/094 Reapplying Standardization.en.srt 8.7 kB
  • 10 Natural Binary Classification/128 Refactoring with Cross Entropy.id.srt 8.7 kB
  • 12 Image Recognition In Action/148 Encoding Label Values.en.srt 8.7 kB
  • 14 Appendix Custom CSV Loader/183 Shuffling Data via Seed Phrase.en.srt 8.7 kB
  • 09 Gradient Descent Alterations/106 Refactoring Towards Batch Gradient Descent.id.srt 8.5 kB
  • 12 Image Recognition In Action/145 Greyscale Values.id.srt 8.5 kB
  • 03 Onwards to Tensorflow JS/043 Summing Values Along an Axis.en.srt 8.5 kB
  • 03 Onwards to Tensorflow JS/035 A Plan to Move Forward.id.srt 8.4 kB
  • 11 Multi-Value Classification/132 A Smart Refactor to Multinominal Analysis.en.srt 8.4 kB
  • 08 Plotting Data with Javascript/103 Plotting MSE Values.en.srt 8.4 kB
  • 04 Applications of Tensorflow/045 KNN with Regression.id.srt 8.4 kB
  • 10 Natural Binary Classification/128 Refactoring with Cross Entropy.en.srt 8.4 kB
  • 06 Gradient Descent with Tensorflow/073 Data Loading.id.srt 8.4 kB
  • 13 Performance Optimization/156 Creating Memory Snapshots.en.srt 8.4 kB
  • 07 Increasing Performance with Vectorized Solutions/100 Recording MSE History.en.srt 8.3 kB
  • 02 Algorithm Overview/021 Refactoring Accuracy Reporting.id.srt 8.3 kB
  • 14 Appendix Custom CSV Loader/182 Extracting Data Columns.id.srt 8.3 kB
  • 09 Gradient Descent Alterations/106 Refactoring Towards Batch Gradient Descent.en.srt 8.2 kB
  • 05 Getting Started with Gradient Descent/061 Why Linear Regression.id.srt 8.2 kB
  • 04 Applications of Tensorflow/045 KNN with Regression.en.srt 8.2 kB
  • 02 Algorithm Overview/019 Gauging Accuracy.en.srt 8.2 kB
  • 12 Image Recognition In Action/145 Greyscale Values.en.srt 8.1 kB
  • 11 Multi-Value Classification/141 Refactoring Sigmoid to Softmax.id.srt 8.1 kB
  • 11 Multi-Value Classification/135 Refactoring to Multi-Column Weights.id.srt 8.1 kB
  • 05 Getting Started with Gradient Descent/070 Gradient Descent with Multiple Terms.id.srt 8.1 kB
  • 01 What is Machine Learning/009 What Type of Problem.id.srt 8.0 kB
  • 13 Performance Optimization/155 Minimizing Memory Usage.id.srt 8.0 kB
  • 06 Gradient Descent with Tensorflow/073 Data Loading.en.srt 8.0 kB
  • 14 Appendix Custom CSV Loader/182 Extracting Data Columns.en.srt 7.9 kB
  • 03 Onwards to Tensorflow JS/035 A Plan to Move Forward.en.srt 7.9 kB
  • 10 Natural Binary Classification/114 The Sigmoid Equation.id.srt 7.9 kB
  • 11 Multi-Value Classification/135 Refactoring to Multi-Column Weights.en.srt 7.8 kB
  • 13 Performance Optimization/172 Fixing Cost History.id.srt 7.8 kB
  • 05 Getting Started with Gradient Descent/061 Why Linear Regression.en.srt 7.8 kB
  • 02 Algorithm Overview/021 Refactoring Accuracy Reporting.en.srt 7.8 kB
  • 01 What is Machine Learning/009 What Type of Problem.en.srt 7.8 kB
  • 13 Performance Optimization/154 Handing Large Datasets.id.srt 7.8 kB
  • 11 Multi-Value Classification/136 A Problem to Test Multinominal Classification.id.srt 7.7 kB
  • 11 Multi-Value Classification/141 Refactoring Sigmoid to Softmax.en.srt 7.7 kB
  • 13 Performance Optimization/155 Minimizing Memory Usage.en.srt 7.7 kB
  • 02 Algorithm Overview/014 Testing the Algorithm.id.srt 7.6 kB
  • 05 Getting Started with Gradient Descent/070 Gradient Descent with Multiple Terms.en.srt 7.6 kB
  • 11 Multi-Value Classification/137 Classifying Continuous Values.id.srt 7.6 kB
  • 02 Algorithm Overview/030 Applying Normalization.id.srt 7.5 kB
  • 13 Performance Optimization/163 Tensorflows Eager Memory Usage.id.srt 7.5 kB
  • 08 Plotting Data with Javascript/104 Plotting MSE History against B Values.id.srt 7.5 kB
  • 07 Increasing Performance with Vectorized Solutions/092 Reminder on Standardization.id.srt 7.5 kB
  • 13 Performance Optimization/171 NaN in Cost History.id.srt 7.5 kB
  • 10 Natural Binary Classification/114 The Sigmoid Equation.en.srt 7.4 kB
  • 10 Natural Binary Classification/129 Finishing the Cost Refactor.id.srt 7.4 kB
  • 08 Plotting Data with Javascript/102 Observing Changing Learning Rate and MSE.id.srt 7.4 kB
  • 13 Performance Optimization/174 Improving Model Accuracy.id.srt 7.4 kB
  • 11 Multi-Value Classification/136 A Problem to Test Multinominal Classification.en.srt 7.3 kB
  • 13 Performance Optimization/172 Fixing Cost History.en.srt 7.3 kB
  • 10 Natural Binary Classification/122 The Sigmoid Equation with Logistic Regression.id.srt 7.3 kB
  • 02 Algorithm Overview/014 Testing the Algorithm.en.srt 7.3 kB
  • 10 Natural Binary Classification/120 Encoding Label Values.id.srt 7.3 kB
  • 08 Plotting Data with Javascript/104 Plotting MSE History against B Values.en.srt 7.2 kB
  • 13 Performance Optimization/154 Handing Large Datasets.en.srt 7.2 kB
  • 11 Multi-Value Classification/137 Classifying Continuous Values.en.srt 7.2 kB
  • 07 Increasing Performance with Vectorized Solutions/092 Reminder on Standardization.en.srt 7.1 kB
  • 13 Performance Optimization/163 Tensorflows Eager Memory Usage.en.srt 7.1 kB
  • 13 Performance Optimization/170 Plotting Cost History.id.srt 7.1 kB
  • 02 Algorithm Overview/030 Applying Normalization.en.srt 7.1 kB
  • 13 Performance Optimization/171 NaN in Cost History.en.srt 7.1 kB
  • 10 Natural Binary Classification/119 Importing Vehicle Data.id.srt 7.0 kB
  • 10 Natural Binary Classification/120 Encoding Label Values.en.srt 7.0 kB
  • 10 Natural Binary Classification/129 Finishing the Cost Refactor.en.srt 7.0 kB
  • 14 Appendix Custom CSV Loader/181 Custom Value Parsing.id.srt 7.0 kB
  • 04 Applications of Tensorflow/059 What Now.id.srt 7.0 kB
  • 01 What is Machine Learning/006 Identifying Relevant Data.id.srt 7.0 kB
  • 08 Plotting Data with Javascript/102 Observing Changing Learning Rate and MSE.en.srt 7.0 kB
  • 04 Applications of Tensorflow/046 A Change in Data Structure.id.srt 6.9 kB
  • 10 Natural Binary Classification/122 The Sigmoid Equation with Logistic Regression.en.srt 6.9 kB
  • 13 Performance Optimization/174 Improving Model Accuracy.en.srt 6.9 kB
  • 01 What is Machine Learning/006 Identifying Relevant Data.en.srt 6.8 kB
  • 10 Natural Binary Classification/119 Importing Vehicle Data.en.srt 6.8 kB
  • 02 Algorithm Overview/015 Interpreting Bad Results.id.srt 6.8 kB
  • 13 Performance Optimization/161 Measuring Footprint Reduction.id.srt 6.8 kB
  • 03 Onwards to Tensorflow JS/039 Logging Tensor Data.id.srt 6.8 kB
  • 13 Performance Optimization/170 Plotting Cost History.en.srt 6.8 kB
  • 04 Applications of Tensorflow/046 A Change in Data Structure.en.srt 6.7 kB
  • 14 Appendix Custom CSV Loader/181 Custom Value Parsing.en.srt 6.7 kB
  • 02 Algorithm Overview/015 Interpreting Bad Results.en.srt 6.6 kB
  • 02 Algorithm Overview/024 Multi-Dimensional KNN.id.srt 6.6 kB
  • 02 Algorithm Overview/016 Test and Training Data.id.srt 6.6 kB
  • 13 Performance Optimization/166 Tidying the Training Loop.id.srt 6.6 kB
  • 04 Applications of Tensorflow/059 What Now.en.srt 6.5 kB
  • 04 Applications of Tensorflow/057 Applying Standardization.id.srt 6.5 kB
  • 05 Getting Started with Gradient Descent/069 Answering Common Questions.id.srt 6.5 kB
  • 02 Algorithm Overview/024 Multi-Dimensional KNN.en.srt 6.5 kB
  • 01 What is Machine Learning/008 Recording Observation Data.id.srt 6.4 kB
  • 13 Performance Optimization/166 Tidying the Training Loop.en.srt 6.4 kB
  • 03 Onwards to Tensorflow JS/039 Logging Tensor Data.en.srt 6.4 kB
  • 13 Performance Optimization/161 Measuring Footprint Reduction.en.srt 6.4 kB
  • 04 Applications of Tensorflow/057 Applying Standardization.en.srt 6.3 kB
  • 11 Multi-Value Classification/133 A Smarter Refactor.id.srt 6.3 kB
  • 02 Algorithm Overview/016 Test and Training Data.en.srt 6.2 kB
  • 01 What is Machine Learning/008 Recording Observation Data.en.srt 6.2 kB
  • 10 Natural Binary Classification/130 Plotting Changing Cost History.id.srt 6.2 kB
  • 02 Algorithm Overview/017 Randomizing Test Data.id.srt 6.1 kB
  • 02 Algorithm Overview/018 Generalizing KNN.id.srt 6.1 kB
  • 05 Getting Started with Gradient Descent/069 Answering Common Questions.en.srt 6.1 kB
  • 11 Multi-Value Classification/133 A Smarter Refactor.en.srt 6.1 kB
  • 07 Increasing Performance with Vectorized Solutions/088 Same Results Or Not.id.srt 6.0 kB
  • 07 Increasing Performance with Vectorized Solutions/093 Data Processing in a Helper Method.id.srt 5.8 kB
  • 02 Algorithm Overview/017 Randomizing Test Data.en.srt 5.8 kB
  • 10 Natural Binary Classification/130 Plotting Changing Cost History.en.srt 5.8 kB
  • 14 Appendix Custom CSV Loader/180 Parsing Number Values.id.srt 5.8 kB
  • 02 Algorithm Overview/018 Generalizing KNN.en.srt 5.8 kB
  • 13 Performance Optimization/165 Implementing TF Tidy.id.srt 5.7 kB
  • 10 Natural Binary Classification/124 Gauging Classification Accuracy.id.srt 5.7 kB
  • 07 Increasing Performance with Vectorized Solutions/093 Data Processing in a Helper Method.en.srt 5.7 kB
  • 12 Image Recognition In Action/146 Many Features.id.srt 5.6 kB
  • 14 Appendix Custom CSV Loader/180 Parsing Number Values.en.srt 5.6 kB
  • 07 Increasing Performance with Vectorized Solutions/088 Same Results Or Not.en.srt 5.6 kB
  • 10 Natural Binary Classification/124 Gauging Classification Accuracy.en.srt 5.6 kB
  • 13 Performance Optimization/165 Implementing TF Tidy.en.srt 5.5 kB
  • 02 Algorithm Overview/020 Printing a Report.id.srt 5.5 kB
  • 11 Multi-Value Classification/143 Calculating Accuracy.id.srt 5.5 kB
  • 12 Image Recognition In Action/146 Many Features.en.srt 5.5 kB
  • 04 Applications of Tensorflow/051 Moving to the Editor.id.srt 5.5 kB
  • 06 Gradient Descent with Tensorflow/078 Updating Coefficients.id.srt 5.4 kB
  • 04 Applications of Tensorflow/051 Moving to the Editor.en.srt 5.4 kB
  • 06 Gradient Descent with Tensorflow/075 Formulating the Training Loop.id.srt 5.4 kB
  • 13 Performance Optimization/160 Releasing References.id.srt 5.3 kB
  • 11 Multi-Value Classification/143 Calculating Accuracy.en.srt 5.2 kB
  • 01 What is Machine Learning/005 Problem Outline.id.srt 5.2 kB
  • 06 Gradient Descent with Tensorflow/075 Formulating the Training Loop.en.srt 5.2 kB
  • 07 Increasing Performance with Vectorized Solutions/096 Massaging Learning Rates.id.srt 5.2 kB
  • 02 Algorithm Overview/020 Printing a Report.en.srt 5.1 kB
  • 06 Gradient Descent with Tensorflow/078 Updating Coefficients.en.srt 5.1 kB
  • 13 Performance Optimization/160 Releasing References.en.srt 5.1 kB
  • 01 What is Machine Learning/005 Problem Outline.en.srt 5.0 kB
  • 05 Getting Started with Gradient Descent/060 Linear Regression.id.srt 4.9 kB
  • 13 Performance Optimization/169 Final Memory Report.id.srt 4.9 kB
  • 07 Increasing Performance with Vectorized Solutions/096 Massaging Learning Rates.en.srt 4.8 kB
  • 13 Performance Optimization/164 Cleaning up Tensors with Tidy.id.srt 4.7 kB
  • 14 Appendix Custom CSV Loader/177 Reading Files from Disk.id.srt 4.6 kB
  • 05 Getting Started with Gradient Descent/060 Linear Regression.en.srt 4.6 kB
  • 13 Performance Optimization/169 Final Memory Report.en.srt 4.6 kB
  • 14 Appendix Custom CSV Loader/177 Reading Files from Disk.en.srt 4.5 kB
  • 14 Appendix Custom CSV Loader/178 Splitting into Columns.id.srt 4.5 kB
  • 13 Performance Optimization/164 Cleaning up Tensors with Tidy.en.srt 4.5 kB
  • 02 Algorithm Overview/033 Evaluating Different Feature Values.id.srt 4.4 kB
  • 11 Multi-Value Classification/142 Implementing Accuracy Gauges.id.srt 4.4 kB
  • 12 Image Recognition In Action/153 Backfilling Variance.id.srt 4.4 kB
  • 11 Multi-Value Classification/142 Implementing Accuracy Gauges.en.srt 4.4 kB
  • 14 Appendix Custom CSV Loader/178 Splitting into Columns.en.srt 4.4 kB
  • 02 Algorithm Overview/033 Evaluating Different Feature Values.en.srt 4.3 kB
  • 14 Appendix Custom CSV Loader/179 Dropping Trailing Columns.id.srt 4.2 kB
  • 12 Image Recognition In Action/153 Backfilling Variance.en.srt 4.2 kB
  • 10 Natural Binary Classification/111 Introducing Logistic Regression.id.srt 4.1 kB
  • 10 Natural Binary Classification/111 Introducing Logistic Regression.en.srt 4.0 kB
  • 13 Performance Optimization/168 One More Optimization.id.srt 4.0 kB
  • 14 Appendix Custom CSV Loader/179 Dropping Trailing Columns.en.srt 4.0 kB
  • 11 Multi-Value Classification/131 Multinominal Logistic Regression.id.srt 3.9 kB
  • 13 Performance Optimization/168 One More Optimization.en.srt 3.8 kB
  • 12 Image Recognition In Action/144 Handwriting Recognition.id.srt 3.8 kB
  • 01 What is Machine Learning/004 App Setup.id.srt 3.7 kB
  • 15 Extras/185 Bonus.html 3.7 kB
  • 11 Multi-Value Classification/131 Multinominal Logistic Regression.en.srt 3.7 kB
  • 12 Image Recognition In Action/144 Handwriting Recognition.en.srt 3.7 kB
  • 14 Appendix Custom CSV Loader/175 Loading CSV Files.id.srt 3.6 kB
  • 01 What is Machine Learning/004 App Setup.en.srt 3.5 kB
  • 12 Image Recognition In Action/150 Unchanging Accuracy.id.srt 3.5 kB
  • 14 Appendix Custom CSV Loader/175 Loading CSV Files.en.srt 3.5 kB
  • 12 Image Recognition In Action/150 Unchanging Accuracy.en.srt 3.3 kB
  • 13 Performance Optimization/173 Massaging Learning Parameters.id.srt 3.1 kB
  • 14 Appendix Custom CSV Loader/176 A Test Dataset.id.srt 3.0 kB
  • 14 Appendix Custom CSV Loader/176 A Test Dataset.en.srt 3.0 kB
  • 13 Performance Optimization/162 Optimization Tensorflow Memory Usage.id.srt 3.0 kB
  • 13 Performance Optimization/173 Massaging Learning Parameters.en.srt 2.8 kB
  • 13 Performance Optimization/162 Optimization Tensorflow Memory Usage.en.srt 2.7 kB
  • 13 Performance Optimization/167 Measuring Reduced Memory Usage.id.srt 2.6 kB
  • 13 Performance Optimization/167 Measuring Reduced Memory Usage.en.srt 2.5 kB
  • 10 Natural Binary Classification/116 Changes for Logistic Regression.id.srt 2.1 kB
  • 10 Natural Binary Classification/116 Changes for Logistic Regression.en.srt 2.0 kB
  • 01 What is Machine Learning/001 Getting Started - How to Get Help.id.srt 1.9 kB
  • 01 What is Machine Learning/001 Getting Started - How to Get Help.en.srt 1.8 kB
  • 10 Natural Binary Classification/118 Project Download.html 1.1 kB
  • [FreeCourseWorld.Com].url 54 Bytes

随机展示

相关说明

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！
>