[FreeCourseSite.com] Udemy - Machine Learning with Javascript

Creation Time	Aug. 10, 2025, 1:31 a.m.
Last Access Time	Sept. 16, 2026, 6:16 a.m.
File Size	6.8 GB
Keywords	FreeCourseSite with Javascript - Udemy Machine Learning com
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Total Files	438
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05 - Getting Started with Gradient Descent/009 Why a Learning Rate.mp4 148.5 MB
02 - Algorithm Overview/013 Investigating Optimal K Values.mp4 112.4 MB
06 - Gradient Descent with Tensorflow/013 How it All Works Together!.mp4 110.6 MB
05 - Getting Started with Gradient Descent/012 Multiple Terms in Action.mp4 103.3 MB
06 - Gradient Descent with Tensorflow/008 Interpreting Results.mp4 89.7 MB
05 - Getting Started with Gradient Descent/007 Gradient Descent in Action.mp4 89.2 MB
13 - Performance Optimization/006 Measuring Memory Usage.mp4 85.9 MB
07 - Increasing Performance with Vectorized Solutions/013 Moving Towards Multivariate Regression.mp4 85.0 MB
10 - Natural Binary Classification/013 A Touch More Refactoring.mp4 78.7 MB
04 - Applications of Tensorflow/011 Normalization or Standardization.mp4 77.9 MB
05 - Getting Started with Gradient Descent/003 Understanding Gradient Descent.mp4 77.9 MB
12 - Image Recognition In Action/008 Debugging the Calculation Process.mp4 77.6 MB
04 - Applications of Tensorflow/014 Debugging Calculations.mp4 75.0 MB
11 - Multi-Value Classification/004 A Single Instance Approach.mp4 74.4 MB
07 - Increasing Performance with Vectorized Solutions/014 Refactoring for Multivariate Analysis.mp4 72.4 MB
05 - Getting Started with Gradient Descent/004 Guessing Coefficients with MSE.mp4 70.8 MB
03 - Onwards to Tensorflow JS!/003 Tensor Shape and Dimension.mp4 70.3 MB
02 - Algorithm Overview/001 How K-Nearest Neighbor Works.mp4 69.7 MB
04 - Applications of Tensorflow/008 Loading CSV Data.mp4 68.8 MB
11 - Multi-Value Classification/009 Marginal vs Conditional Probability.mp4 68.4 MB
06 - Gradient Descent with Tensorflow/005 Initial Gradient Descent Implementation.mp4 67.7 MB
07 - Increasing Performance with Vectorized Solutions/002 Refactoring to One Equation.mp4 63.8 MB
09 - Gradient Descent Alterations/001 Batch and Stochastic Gradient Descent.mp4 63.7 MB
01 - What is Machine Learning/005 A Complete Walkthrough.mp4 63.3 MB
01 - What is Machine Learning/004 Solving Machine Learning Problems.mp4 62.8 MB
12 - Image Recognition In Action/006 Implementing an Accuracy Gauge.mp4 62.2 MB
06 - Gradient Descent with Tensorflow/012 Simplification with Matrix Multiplication.mp4 60.8 MB
04 - Applications of Tensorflow/003 KNN with Tensorflow.mp4 59.5 MB
02 - Algorithm Overview/016 N-Dimension Distance.mp4 59.5 MB
02 - Algorithm Overview/003 Implementing KNN.mp4 59.3 MB
07 - Increasing Performance with Vectorized Solutions/003 A Few More Changes.mp4 58.6 MB
10 - Natural Binary Classification/016 Variable Decision Boundaries.mp4 58.4 MB
02 - Algorithm Overview/017 Arbitrary Feature Spaces.mp4 58.1 MB
07 - Increasing Performance with Vectorized Solutions/007 Dealing with Bad Accuracy.mp4 57.7 MB
02 - Algorithm Overview/022 Feature Selection with KNN.mp4 57.3 MB
07 - Increasing Performance with Vectorized Solutions/005 Calculating Model Accuracy.mp4 56.5 MB
02 - Algorithm Overview/020 Normalization with MinMax.mp4 54.4 MB
10 - Natural Binary Classification/011 Updating Linear Regression for Logistic Regression.mp4 54.3 MB
02 - Algorithm Overview/019 Feature Normalization.mp4 54.1 MB
09 - Gradient Descent Alterations/003 Determining Batch Size and Quantity.mp4 53.9 MB
07 - Increasing Performance with Vectorized Solutions/015 Learning Rate Optimization.mp4 53.0 MB
06 - Gradient Descent with Tensorflow/006 Calculating MSE Slopes.mp4 51.1 MB
09 - Gradient Descent Alterations/005 Evaluating Batch Gradient Descent Results.mp4 50.7 MB
14 - Appendix Custom CSV Loader/008 Extracting Data Columns.mp4 50.7 MB
07 - Increasing Performance with Vectorized Solutions/010 Reapplying Standardization.mp4 50.2 MB
02 - Algorithm Overview/014 Updating KNN for Multiple Features.mp4 48.8 MB
14 - Appendix Custom CSV Loader/010 Splitting Test and Training.mp4 48.3 MB
02 - Algorithm Overview/002 Lodash Review.mp4 48.3 MB
01 - What is Machine Learning/009 Dataset Structures.mp4 48.2 MB
04 - Applications of Tensorflow/006 Averaging Top Values.mp4 47.5 MB
07 - Increasing Performance with Vectorized Solutions/006 Implementing Coefficient of Determination.mp4 47.5 MB
10 - Natural Binary Classification/007 Project Setup for Logistic Regression.mp4 46.9 MB
03 - Onwards to Tensorflow JS!/001 Let's Get Our Bearings.mp4 46.8 MB
13 - Performance Optimization/005 Shallow vs Retained Memory Usage.mp4 46.7 MB
02 - Algorithm Overview/018 Magnitude Offsets in Features.mp4 46.2 MB
02 - Algorithm Overview/010 Gauging Accuracy.mp4 46.0 MB
07 - Increasing Performance with Vectorized Solutions/001 Refactoring the Linear Regression Class.mp4 45.9 MB
02 - Algorithm Overview/005 Testing the Algorithm.mp4 45.0 MB
11 - Multi-Value Classification/010 Sigmoid vs Softmax.mp4 44.6 MB
06 - Gradient Descent with Tensorflow/010 More on Matrix Multiplication.mp4 43.6 MB
10 - Natural Binary Classification/017 Mean Squared Error vs Cross Entropy.mp4 43.5 MB
13 - Performance Optimization/017 Plotting Cost History.mp4 43.3 MB
03 - Onwards to Tensorflow JS!/005 Elementwise Operations.mp4 43.2 MB
10 - Natural Binary Classification/019 Finishing the Cost Refactor.mp4 42.1 MB
11 - Multi-Value Classification/011 Refactoring Sigmoid to Softmax.mp4 41.8 MB
11 - Multi-Value Classification/008 Training a Multinominal Model.mp4 41.2 MB
13 - Performance Optimization/013 Tidying the Training Loop.mp4 41.1 MB
07 - Increasing Performance with Vectorized Solutions/011 Fixing Standardization Issues.mp4 41.0 MB
04 - Applications of Tensorflow/010 Reporting Error Percentages.mp4 40.7 MB
04 - Applications of Tensorflow/012 Numerical Standardization with Tensorflow.mp4 40.2 MB
08 - Plotting Data with Javascript/002 Plotting MSE Values.mp4 39.7 MB
05 - Getting Started with Gradient Descent/008 Quick Breather and Review.mp4 39.1 MB
02 - Algorithm Overview/009 Generalizing KNN.mp4 39.0 MB
02 - Algorithm Overview/021 Applying Normalization.mp4 38.9 MB
12 - Image Recognition In Action/002 Greyscale Values.mp4 38.4 MB
12 - Image Recognition In Action/005 Encoding Label Values.mp4 38.3 MB
10 - Natural Binary Classification/018 Refactoring with Cross Entropy.mp4 38.2 MB
02 - Algorithm Overview/004 Finishing KNN Implementation.mp4 38.0 MB
12 - Image Recognition In Action/004 Flattening Image Data.mp4 37.0 MB
03 - Onwards to Tensorflow JS!/002 A Plan to Move Forward.mp4 36.9 MB
12 - Image Recognition In Action/003 Many Features.mp4 36.7 MB
04 - Applications of Tensorflow/013 Applying Standardization.mp4 36.5 MB
08 - Plotting Data with Javascript/003 Plotting MSE History against B Values.mp4 36.1 MB
04 - Applications of Tensorflow/004 Maintaining Order Relationships.mp4 36.1 MB
13 - Performance Optimization/018 NaN in Cost History.mp4 35.9 MB
08 - Plotting Data with Javascript/001 Observing Changing Learning Rate and MSE.mp4 35.4 MB
10 - Natural Binary Classification/005 Decision Boundaries.mp4 35.0 MB
01 - What is Machine Learning/008 Identifying Relevant Data.mp4 33.9 MB
09 - Gradient Descent Alterations/006 Making Predictions with the Model.mp4 33.8 MB
02 - Algorithm Overview/012 Refactoring Accuracy Reporting.mp4 33.8 MB
13 - Performance Optimization/021 Improving Model Accuracy.mp4 33.6 MB
10 - Natural Binary Classification/003 Bad Equation Fits.mp4 33.6 MB
10 - Natural Binary Classification/020 Plotting Changing Cost History.mp4 33.3 MB
02 - Algorithm Overview/011 Printing a Report.mp4 33.3 MB
10 - Natural Binary Classification/009 Importing Vehicle Data.mp4 33.0 MB
14 - Appendix Custom CSV Loader/009 Shuffling Data via Seed Phrase.mp4 32.9 MB
07 - Increasing Performance with Vectorized Solutions/016 Recording MSE History.mp4 32.7 MB
02 - Algorithm Overview/015 Multi-Dimensional KNN.mp4 31.9 MB
13 - Performance Optimization/007 Releasing References.mp4 31.9 MB
06 - Gradient Descent with Tensorflow/009 Matrix Multiplication.mp4 30.6 MB
13 - Performance Optimization/019 Fixing Cost History.mp4 30.6 MB
10 - Natural Binary Classification/004 The Sigmoid Equation.mp4 30.4 MB
11 - Multi-Value Classification/005 Refactoring to Multi-Column Weights.mp4 30.3 MB
03 - Onwards to Tensorflow JS!/012 Summing Values Along an Axis.mp4 30.0 MB
05 - Getting Started with Gradient Descent/002 Why Linear Regression.mp4 29.9 MB
10 - Natural Binary Classification/010 Encoding Label Values.mp4 29.9 MB
05 - Getting Started with Gradient Descent/010 Answering Common Questions.mp4 29.8 MB
04 - Applications of Tensorflow/005 Sorting Tensors.mp4 29.5 MB
11 - Multi-Value Classification/006 A Problem to Test Multinominal Classification.mp4 28.8 MB
11 - Multi-Value Classification/003 A Smarter Refactor!.mp4 28.5 MB
02 - Algorithm Overview/023 Objective Feature Picking.mp4 28.5 MB
03 - Onwards to Tensorflow JS!/010 Creating Slices of Data.mp4 28.0 MB
07 - Increasing Performance with Vectorized Solutions/017 Updating Learning Rate.mp4 28.0 MB
03 - Onwards to Tensorflow JS!/011 Tensor Concatenation.mp4 27.8 MB
02 - Algorithm Overview/007 Test and Training Data.mp4 27.4 MB
03 - Onwards to Tensorflow JS!/013 Massaging Dimensions with ExpandDims.mp4 27.2 MB
13 - Performance Optimization/008 Measuring Footprint Reduction.mp4 26.9 MB
04 - Applications of Tensorflow/007 Moving to the Editor.mp4 26.8 MB
06 - Gradient Descent with Tensorflow/003 Default Algorithm Options.mp4 26.6 MB
09 - Gradient Descent Alterations/004 Iterating Over Batches.mp4 26.4 MB
06 - Gradient Descent with Tensorflow/007 Updating Coefficients.mp4 26.0 MB
02 - Algorithm Overview/006 Interpreting Bad Results.mp4 25.7 MB
06 - Gradient Descent with Tensorflow/001 Project Overview.mp4 24.9 MB
03 - Onwards to Tensorflow JS!/006 Broadcasting Operations.mp4 24.2 MB
14 - Appendix Custom CSV Loader/006 Parsing Number Values.mp4 24.0 MB
09 - Gradient Descent Alterations/002 Refactoring Towards Batch Gradient Descent.mp4 23.6 MB
07 - Increasing Performance with Vectorized Solutions/009 Data Processing in a Helper Method.mp4 23.5 MB
10 - Natural Binary Classification/012 The Sigmoid Equation with Logistic Regression.mp4 23.3 MB
12 - Image Recognition In Action/009 Dealing with Zero Variances.mp4 23.0 MB
01 - What is Machine Learning/007 Problem Outline.mp4 22.9 MB
07 - Increasing Performance with Vectorized Solutions/012 Massaging Learning Rates.mp4 22.8 MB
06 - Gradient Descent with Tensorflow/011 Matrix Form of Slope Equations.mp4 22.7 MB
13 - Performance Optimization/004 The Javascript Garbage Collector.mp4 22.6 MB
10 - Natural Binary Classification/014 Gauging Classification Accuracy.mp4 22.3 MB
11 - Multi-Value Classification/012 Implementing Accuracy Gauges.mp4 22.0 MB
13 - Performance Optimization/003 Creating Memory Snapshots.mp4 21.8 MB
13 - Performance Optimization/015 One More Optimization.mp4 21.5 MB
05 - Getting Started with Gradient Descent/005 Observations Around MSE.mp4 21.4 MB
13 - Performance Optimization/016 Final Memory Report.mp4 21.1 MB
02 - Algorithm Overview/024 Evaluating Different Feature Values.mp4 21.0 MB
04 - Applications of Tensorflow/009 Running an Analysis.mp4 20.8 MB
05 - Getting Started with Gradient Descent/006 Derivatives!.mp4 20.7 MB
13 - Performance Optimization/010 Tensorflow's Eager Memory Usage.mp4 20.1 MB
10 - Natural Binary Classification/015 Implementing a Test Function.mp4 20.0 MB
11 - Multi-Value Classification/007 Classifying Continuous Values.mp4 19.7 MB
06 - Gradient Descent with Tensorflow/002 Data Loading.mp4 19.6 MB
04 - Applications of Tensorflow/001 KNN with Regression.mp4 19.0 MB
07 - Increasing Performance with Vectorized Solutions/008 Reminder on Standardization.mp4 18.8 MB
13 - Performance Optimization/001 Handing Large Datasets.mp4 18.5 MB
04 - Applications of Tensorflow/015 What Now.mp4 17.8 MB
10 - Natural Binary Classification/002 Logistic Regression in Action.mp4 17.8 MB
01 - What is Machine Learning/011 What Type of Problem.mp4 16.9 MB
05 - Getting Started with Gradient Descent/011 Gradient Descent with Multiple Terms.mp4 16.8 MB
12 - Image Recognition In Action/010 Backfilling Variance.mp4 16.5 MB
13 - Performance Optimization/002 Minimizing Memory Usage.mp4 15.2 MB
04 - Applications of Tensorflow/002 A Change in Data Structure.mp4 15.1 MB
11 - Multi-Value Classification/002 A Smart Refactor to Multinominal Analysis.mp4 14.5 MB
14 - Appendix Custom CSV Loader/007 Custom Value Parsing.mp4 14.0 MB
13 - Performance Optimization/012 Implementing TF Tidy.mp4 13.7 MB
13 - Performance Optimization/020 Massaging Learning Parameters.mp4 13.5 MB
02 - Algorithm Overview/008 Randomizing Test Data.mp4 13.5 MB
01 - What is Machine Learning/010 Recording Observation Data.mp4 12.7 MB
07 - Increasing Performance with Vectorized Solutions/004 Same Results Or Not.mp4 12.0 MB
11 - Multi-Value Classification/013 Calculating Accuracy.mp4 11.7 MB
13 - Performance Optimization/009 Optimization Tensorflow Memory Usage.mp4 11.4 MB
13 - Performance Optimization/014 Measuring Reduced Memory Usage.mp4 11.0 MB
03 - Onwards to Tensorflow JS!/009 Tensor Accessors.mp4 10.9 MB
03 - Onwards to Tensorflow JS!/008 Logging Tensor Data.mp4 10.7 MB
05 - Getting Started with Gradient Descent/001 Linear Regression.mp4 9.7 MB
13 - Performance Optimization/011 Cleaning up Tensors with Tidy.mp4 9.4 MB
10 - Natural Binary Classification/001 Introducing Logistic Regression.mp4 8.9 MB
06 - Gradient Descent with Tensorflow/004 Formulating the Training Loop.mp4 8.7 MB
12 - Image Recognition In Action/001 Handwriting Recognition.mp4 8.4 MB
01 - What is Machine Learning/001 Getting Started - How to Get Help.mp4 8.3 MB
01 - What is Machine Learning/006 App Setup.mp4 8.1 MB
14 - Appendix Custom CSV Loader/005 Dropping Trailing Columns.mp4 7.7 MB
12 - Image Recognition In Action/007 Unchanging Accuracy.mp4 7.0 MB
14 - Appendix Custom CSV Loader/004 Splitting into Columns.mp4 6.7 MB
14 - Appendix Custom CSV Loader/003 Reading Files from Disk.mp4 6.6 MB
11 - Multi-Value Classification/001 Multinominal Logistic Regression.mp4 6.6 MB
14 - Appendix Custom CSV Loader/001 Loading CSV Files.mp4 6.0 MB
14 - Appendix Custom CSV Loader/002 A Test Dataset.mp4 3.7 MB
10 - Natural Binary Classification/006 Changes for Logistic Regression.mp4 3.4 MB
01 - What is Machine Learning/002 diagrams.zip 789.8 KB
10 - Natural Binary Classification/008 regressions.zip 34.3 KB
05 - Getting Started with Gradient Descent/009 Why a Learning Rate_en.srt 26.5 KB
06 - Gradient Descent with Tensorflow/013 How it All Works Together!_en.srt 22.0 KB
03 - Onwards to Tensorflow JS!/003 Tensor Shape and Dimension_en.srt 20.0 KB
05 - Getting Started with Gradient Descent/003 Understanding Gradient Descent_en.srt 20.0 KB
07 - Increasing Performance with Vectorized Solutions/013 Moving Towards Multivariate Regression_en.srt 19.1 KB
02 - Algorithm Overview/013 Investigating Optimal K Values_en.srt 18.8 KB
05 - Getting Started with Gradient Descent/007 Gradient Descent in Action_en.srt 18.7 KB
05 - Getting Started with Gradient Descent/012 Multiple Terms in Action_en.srt 17.0 KB
11 - Multi-Value Classification/009 Marginal vs Conditional Probability_en.srt 16.2 KB
02 - Algorithm Overview/016 N-Dimension Distance_en.srt 16.1 KB
06 - Gradient Descent with Tensorflow/008 Interpreting Results_en.srt 16.0 KB
05 - Getting Started with Gradient Descent/004 Guessing Coefficients with MSE_en.srt 15.8 KB
11 - Multi-Value Classification/004 A Single Instance Approach_en.srt 15.8 KB
04 - Applications of Tensorflow/008 Loading CSV Data_en.srt 15.7 KB
02 - Algorithm Overview/002 Lodash Review_en.srt 15.6 KB
01 - What is Machine Learning/005 A Complete Walkthrough_en.srt 15.4 KB
06 - Gradient Descent with Tensorflow/012 Simplification with Matrix Multiplication_en.srt 15.1 KB
04 - Applications of Tensorflow/003 KNN with Tensorflow_en.srt 15.0 KB
07 - Increasing Performance with Vectorized Solutions/002 Refactoring to One Equation_en.srt 14.6 KB
06 - Gradient Descent with Tensorflow/005 Initial Gradient Descent Implementation_en.srt 14.3 KB
13 - Performance Optimization/006 Measuring Memory Usage_en.srt 14.2 KB
02 - Algorithm Overview/017 Arbitrary Feature Spaces_en.srt 13.9 KB
12 - Image Recognition In Action/008 Debugging the Calculation Process_en.srt 13.7 KB
04 - Applications of Tensorflow/014 Debugging Calculations_en.srt 13.6 KB
03 - Onwards to Tensorflow JS!/004 Tensor Dimension and Shapes.html 13.5 KB
07 - Increasing Performance with Vectorized Solutions/005 Calculating Model Accuracy_en.srt 13.4 KB
02 - Algorithm Overview/001 How K-Nearest Neighbor Works_en.srt 13.3 KB
06 - Gradient Descent with Tensorflow/003 Default Algorithm Options_en.srt 13.2 KB
07 - Increasing Performance with Vectorized Solutions/015 Learning Rate Optimization_en.srt 13.1 KB
14 - Appendix Custom CSV Loader/010 Splitting Test and Training_en.srt 12.9 KB
09 - Gradient Descent Alterations/004 Iterating Over Batches_en.srt 12.9 KB
04 - Applications of Tensorflow/005 Sorting Tensors_en.srt 12.8 KB
02 - Algorithm Overview/022 Feature Selection with KNN_en.srt 12.8 KB
03 - Onwards to Tensorflow JS!/005 Elementwise Operations_en.srt 12.7 KB
03 - Onwards to Tensorflow JS!/013 Massaging Dimensions with ExpandDims_en.srt 12.5 KB
09 - Gradient Descent Alterations/006 Making Predictions with the Model_en.srt 12.4 KB
04 - Applications of Tensorflow/006 Averaging Top Values_en.srt 12.4 KB
10 - Natural Binary Classification/005 Decision Boundaries_en.srt 12.4 KB
03 - Onwards to Tensorflow JS!/001 Let's Get Our Bearings_en.srt 12.3 KB
07 - Increasing Performance with Vectorized Solutions/007 Dealing with Bad Accuracy_en.srt 12.3 KB
07 - Increasing Performance with Vectorized Solutions/006 Implementing Coefficient of Determination_en.srt 12.2 KB
07 - Increasing Performance with Vectorized Solutions/014 Refactoring for Multivariate Analysis_en.srt 12.2 KB
04 - Applications of Tensorflow/012 Numerical Standardization with Tensorflow_en.srt 12.1 KB
10 - Natural Binary Classification/016 Variable Decision Boundaries_en.srt 12.1 KB
07 - Increasing Performance with Vectorized Solutions/001 Refactoring the Linear Regression Class_en.srt 12.0 KB
03 - Onwards to Tensorflow JS!/010 Creating Slices of Data_en.srt 12.0 KB
10 - Natural Binary Classification/013 A Touch More Refactoring_en.srt 11.9 KB
04 - Applications of Tensorflow/011 Normalization or Standardization_en.srt 11.9 KB
02 - Algorithm Overview/019 Feature Normalization_en.srt 11.9 KB
12 - Image Recognition In Action/006 Implementing an Accuracy Gauge_en.srt 11.9 KB
09 - Gradient Descent Alterations/001 Batch and Stochastic Gradient Descent_en.srt 11.7 KB
05 - Getting Started with Gradient Descent/006 Derivatives!_en.srt 11.5 KB
10 - Natural Binary Classification/011 Updating Linear Regression for Logistic Regression_en.srt 11.5 KB
06 - Gradient Descent with Tensorflow/009 Matrix Multiplication_en.srt 11.3 KB
03 - Onwards to Tensorflow JS!/006 Broadcasting Operations_en.srt 11.0 KB
02 - Algorithm Overview/020 Normalization with MinMax_en.srt 11.0 KB
02 - Algorithm Overview/003 Implementing KNN_en.srt 10.9 KB
04 - Applications of Tensorflow/004 Maintaining Order Relationships_en.srt 10.9 KB
10 - Natural Binary Classification/002 Logistic Regression in Action_en.srt 10.9 KB
11 - Multi-Value Classification/008 Training a Multinominal Model_en.srt 10.7 KB
03 - Onwards to Tensorflow JS!/007 Broadcasting Elementwise Operations.html 10.4 KB
13 - Performance Optimization/004 The Javascript Garbage Collector_en.srt 10.4 KB
07 - Increasing Performance with Vectorized Solutions/017 Updating Learning Rate_en.srt 10.4 KB
02 - Algorithm Overview/014 Updating KNN for Multiple Features_en.srt 10.3 KB
07 - Increasing Performance with Vectorized Solutions/003 A Few More Changes_en.srt 10.2 KB
12 - Image Recognition In Action/009 Dealing with Zero Variances_en.srt 10.1 KB
11 - Multi-Value Classification/010 Sigmoid vs Softmax_en.srt 10.1 KB
06 - Gradient Descent with Tensorflow/006 Calculating MSE Slopes_en.srt 10.0 KB
06 - Gradient Descent with Tensorflow/010 More on Matrix Multiplication_en.srt 10.0 KB
04 - Applications of Tensorflow/010 Reporting Error Percentages_en.srt 10.0 KB
04 - Applications of Tensorflow/009 Running an Analysis_en.srt 9.8 KB
02 - Algorithm Overview/023 Objective Feature Picking_en.srt 9.7 KB
06 - Gradient Descent with Tensorflow/011 Matrix Form of Slope Equations_en.srt 9.7 KB
06 - Gradient Descent with Tensorflow/001 Project Overview_en.srt 9.6 KB
01 - What is Machine Learning/004 Solving Machine Learning Problems_en.srt 9.5 KB
13 - Performance Optimization/005 Shallow vs Retained Memory Usage_en.srt 9.5 KB
01 - What is Machine Learning/009 Dataset Structures_en.srt 9.5 KB
09 - Gradient Descent Alterations/003 Determining Batch Size and Quantity_en.srt 9.5 KB
10 - Natural Binary Classification/007 Project Setup for Logistic Regression_en.srt 9.4 KB
12 - Image Recognition In Action/005 Encoding Label Values_en.srt 9.4 KB
02 - Algorithm Overview/004 Finishing KNN Implementation_en.srt 9.3 KB
05 - Getting Started with Gradient Descent/005 Observations Around MSE_en.srt 9.3 KB
09 - Gradient Descent Alterations/005 Evaluating Batch Gradient Descent Results_en.srt 9.2 KB
02 - Algorithm Overview/018 Magnitude Offsets in Features_en.srt 9.2 KB
07 - Increasing Performance with Vectorized Solutions/010 Reapplying Standardization_en.srt 9.1 KB
10 - Natural Binary Classification/017 Mean Squared Error vs Cross Entropy_en.srt 9.1 KB
05 - Getting Started with Gradient Descent/008 Quick Breather and Review_en.srt 9.1 KB
07 - Increasing Performance with Vectorized Solutions/011 Fixing Standardization Issues_en.srt 9.0 KB
12 - Image Recognition In Action/004 Flattening Image Data_en.srt 9.0 KB
14 - Appendix Custom CSV Loader/009 Shuffling Data via Seed Phrase_en.srt 9.0 KB
03 - Onwards to Tensorflow JS!/011 Tensor Concatenation_en.srt 8.9 KB
03 - Onwards to Tensorflow JS!/009 Tensor Accessors_en.srt 8.8 KB
08 - Plotting Data with Javascript/002 Plotting MSE Values_en.srt 8.6 KB
10 - Natural Binary Classification/003 Bad Equation Fits_en.srt 8.6 KB
13 - Performance Optimization/003 Creating Memory Snapshots_en.srt 8.6 KB
10 - Natural Binary Classification/015 Implementing a Test Function_en.srt 8.6 KB
14 - Appendix Custom CSV Loader/008 Extracting Data Columns_en.srt 8.5 KB
11 - Multi-Value Classification/002 A Smart Refactor to Multinominal Analysis_en.srt 8.5 KB
07 - Increasing Performance with Vectorized Solutions/016 Recording MSE History_en.srt 8.5 KB
10 - Natural Binary Classification/018 Refactoring with Cross Entropy_en.srt 8.4 KB
03 - Onwards to Tensorflow JS!/012 Summing Values Along an Axis_en.srt 8.3 KB
04 - Applications of Tensorflow/001 KNN with Regression_en.srt 8.3 KB
09 - Gradient Descent Alterations/002 Refactoring Towards Batch Gradient Descent_en.srt 8.2 KB
02 - Algorithm Overview/010 Gauging Accuracy_en.srt 8.2 KB
01 - What is Machine Learning/011 What Type of Problem_en.srt 8.1 KB
12 - Image Recognition In Action/002 Greyscale Values_en.srt 8.0 KB
06 - Gradient Descent with Tensorflow/002 Data Loading_en.srt 8.0 KB
02 - Algorithm Overview/012 Refactoring Accuracy Reporting_en.srt 8.0 KB
05 - Getting Started with Gradient Descent/002 Why Linear Regression_en.srt 7.9 KB
11 - Multi-Value Classification/005 Refactoring to Multi-Column Weights_en.srt 7.9 KB
11 - Multi-Value Classification/011 Refactoring Sigmoid to Softmax_en.srt 7.9 KB
13 - Performance Optimization/019 Fixing Cost History_en.srt 7.7 KB
05 - Getting Started with Gradient Descent/011 Gradient Descent with Multiple Terms_en.srt 7.7 KB
03 - Onwards to Tensorflow JS!/002 A Plan to Move Forward_en.srt 7.6 KB
13 - Performance Optimization/002 Minimizing Memory Usage_en.srt 7.5 KB
11 - Multi-Value Classification/006 A Problem to Test Multinominal Classification_en.srt 7.5 KB
13 - Performance Optimization/018 NaN in Cost History_en.srt 7.4 KB
13 - Performance Optimization/010 Tensorflow's Eager Memory Usage_en.srt 7.4 KB
02 - Algorithm Overview/021 Applying Normalization_en.srt 7.4 KB
10 - Natural Binary Classification/004 The Sigmoid Equation_en.srt 7.4 KB
02 - Algorithm Overview/005 Testing the Algorithm_en.srt 7.3 KB
08 - Plotting Data with Javascript/003 Plotting MSE History against B Values_en.srt 7.3 KB
13 - Performance Optimization/001 Handing Large Datasets_en.srt 7.2 KB
10 - Natural Binary Classification/012 The Sigmoid Equation with Logistic Regression_en.srt 7.2 KB
11 - Multi-Value Classification/007 Classifying Continuous Values_en.srt 7.2 KB
10 - Natural Binary Classification/019 Finishing the Cost Refactor_en.srt 7.2 KB
10 - Natural Binary Classification/010 Encoding Label Values_en.srt 7.1 KB
07 - Increasing Performance with Vectorized Solutions/008 Reminder on Standardization_en.srt 7.0 KB
14 - Appendix Custom CSV Loader/007 Custom Value Parsing_en.srt 6.9 KB
10 - Natural Binary Classification/009 Importing Vehicle Data_en.srt 6.9 KB
01 - What is Machine Learning/008 Identifying Relevant Data_en.srt 6.9 KB
13 - Performance Optimization/017 Plotting Cost History_en.srt 6.8 KB
13 - Performance Optimization/021 Improving Model Accuracy_en.srt 6.8 KB
08 - Plotting Data with Javascript/001 Observing Changing Learning Rate and MSE_en.srt 6.8 KB
03 - Onwards to Tensorflow JS!/008 Logging Tensor Data_en.srt 6.7 KB
04 - Applications of Tensorflow/013 Applying Standardization_en.srt 6.7 KB
04 - Applications of Tensorflow/002 A Change in Data Structure_en.srt 6.6 KB
02 - Algorithm Overview/006 Interpreting Bad Results_en.srt 6.6 KB
13 - Performance Optimization/008 Measuring Footprint Reduction_en.srt 6.5 KB
04 - Applications of Tensorflow/015 What Now_en.srt 6.4 KB
02 - Algorithm Overview/015 Multi-Dimensional KNN_en.srt 6.4 KB
02 - Algorithm Overview/007 Test and Training Data_en.srt 6.3 KB
13 - Performance Optimization/013 Tidying the Training Loop_en.srt 6.3 KB
01 - What is Machine Learning/010 Recording Observation Data_en.srt 6.3 KB
07 - Increasing Performance with Vectorized Solutions/009 Data Processing in a Helper Method_en.srt 6.1 KB
11 - Multi-Value Classification/003 A Smarter Refactor!_en.srt 6.1 KB
05 - Getting Started with Gradient Descent/010 Answering Common Questions_en.srt 6.1 KB
02 - Algorithm Overview/008 Randomizing Test Data_en.srt 5.9 KB
02 - Algorithm Overview/009 Generalizing KNN_en.srt 5.9 KB
13 - Performance Optimization/012 Implementing TF Tidy_en.srt 5.6 KB
10 - Natural Binary Classification/020 Plotting Changing Cost History_en.srt 5.6 KB
14 - Appendix Custom CSV Loader/006 Parsing Number Values_en.srt 5.5 KB
10 - Natural Binary Classification/014 Gauging Classification Accuracy_en.srt 5.5 KB
04 - Applications of Tensorflow/007 Moving to the Editor_en.srt 5.4 KB
12 - Image Recognition In Action/003 Many Features_en.srt 5.4 KB
07 - Increasing Performance with Vectorized Solutions/004 Same Results Or Not_en.srt 5.4 KB
11 - Multi-Value Classification/013 Calculating Accuracy_en.srt 5.3 KB
06 - Gradient Descent with Tensorflow/007 Updating Coefficients_en.srt 5.2 KB
02 - Algorithm Overview/011 Printing a Report_en.srt 5.2 KB
06 - Gradient Descent with Tensorflow/004 Formulating the Training Loop_en.srt 5.1 KB
13 - Performance Optimization/007 Releasing References_en.srt 5.1 KB
01 - What is Machine Learning/007 Problem Outline_en.srt 5.0 KB
07 - Increasing Performance with Vectorized Solutions/012 Massaging Learning Rates_en.srt 4.8 KB
14 - Appendix Custom CSV Loader/003 Reading Files from Disk_en.srt 4.6 KB
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14 - Appendix Custom CSV Loader/004 Splitting into Columns_en.srt 4.5 KB
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14 - Appendix Custom CSV Loader/005 Dropping Trailing Columns_en.srt 4.1 KB
10 - Natural Binary Classification/001 Introducing Logistic Regression_en.srt 4.0 KB
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15 - Extras/001 Bonus!.html 3.9 KB
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14 - Appendix Custom CSV Loader/002 A Test Dataset_en.srt 3.0 KB
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13 - Performance Optimization/020 Massaging Learning Parameters_en.srt 2.8 KB
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10 - Natural Binary Classification/006 Changes for Logistic Regression_en.srt 2.0 KB
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