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文档列表

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 fro 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-en.srt 26.6 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-en.srt 19.9 kB
03 Onwards to Tensorflow JS/036 Tensor Shape and Dimension-en.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-en.srt 16.9 kB
11 Multi-Value Classification/139 Marginal vs Conditional Probability-en.srt 16.4 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
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/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
13 Performance Optimization/159 Measuring Memory Usage-en.srt 14.2 kB
02 Algorithm Overview/026 Arbitrary Feature Spaces-en.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
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
07 Increasing Performance with Vectorized Solutions/099 Learning Rate Optimization-en.srt 12.8 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
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
14 Appendix Custom CSV Loader/184 Splitting Test and Training-en.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
04 Applications of Tensorflow/050 Averaging Top Values-en.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
10 Natural Binary Classification/121 Updating Linear Regression fro Logistic Regression-en.srt 11.4 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
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
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
13 Performance Optimization/157 The Javascript Garbage Collector-en.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
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/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
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
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
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
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
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/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
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
02 Algorithm Overview/027 Magnitude Offsets in Features-en.srt 8.9 kB
10 Natural Binary Classification/113 Bad Equation Fits-en.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
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
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
03 Onwards to Tensorflow JS/043 Summing Values Along an Axis-en.srt 8.5 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
10 Natural Binary Classification/128 Refactoring with Cross Entropy-en.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
09 Gradient Descent Alterations/106 Refactoring Towards Batch Gradient Descent-en.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
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
11 Multi-Value Classification/135 Refactoring to Multi-Column Weights-en.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
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
05 Getting Started with Gradient Descent/070 Gradient Descent with Multiple Terms-en.srt 7.6 kB
10 Natural Binary Classification/114 The Sigmoid Equation-en.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
02 Algorithm Overview/014 Testing the Algorithm-en.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
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/120 Encoding Label Values-en.srt 7.0 kB
10 Natural Binary Classification/129 Finishing the Cost Refactor-en.srt 7.0 kB
08 Plotting Data with Javascript/102 Observing Changing Learning Rate and MSE-en.srt 7.0 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
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
04 Applications of Tensorflow/059 What Now-en.srt 6.5 kB
02 Algorithm Overview/024 Multi-Dimensional KNN-en.srt 6.5 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
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
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
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
02 Algorithm Overview/018 Generalizing KNN-en.srt 5.8 kB
07 Increasing Performance with Vectorized Solutions/093 Data Processing in a Helper Method-en.srt 5.7 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
12 Image Recognition In Action/146 Many Features-en.srt 5.5 kB
04 Applications of Tensorflow/051 Moving to the Editor-en.srt 5.4 kB
11 Multi-Value Classification/143 Calculating Accuracy-en.srt 5.2 kB
06 Gradient Descent with Tensorflow/075 Formulating the Training Loop-en.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
07 Increasing Performance with Vectorized Solutions/096 Massaging Learning Rates-en.srt 4.8 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
13 Performance Optimization/164 Cleaning up Tensors with Tidy-en.srt 4.5 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
12 Image Recognition In Action/153 Backfilling Variance-en.srt 4.2 kB
10 Natural Binary Classification/111 Introducing Logistic Regression-en.srt 4.0 kB
14 Appendix Custom CSV Loader/179 Dropping Trailing Columns-en.srt 4.0 kB
13 Performance Optimization/168 One More Optimization-en.srt 3.8 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
01 What is Machine Learning/004 App Setup-en.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
14 Appendix Custom CSV Loader/176 A Test Dataset-en.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-en.srt 2.5 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-en.srt 1.8 kB
[FTU Forum].url 1.4 kB
10 Natural Binary Classification/118 Project Download.html 1.1 kB
[FreeCoursesOnline.Me].url 133 Bytes
[FreeTutorials.Eu].url 129 Bytes

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