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种子名称： [FreeCourseSite.com] Udemy - Machine Learning with Javascript
文件类型： 视频
文件数目： 183个文件
文件大小： 6.75 GB
收录时间： 2024-3-15 06:07
已经下载： 3次
资源热度： 74
最近下载： 2024-5-17 17:05

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[FreeCourseSite.com] Udemy - Machine Learning with Javascript.torrent

01 - What is Machine Learning/001 Getting Started - How to Get Help.mp48.35MB

01 - What is Machine Learning/004 Solving Machine Learning Problems.mp462.77MB

01 - What is Machine Learning/005 A Complete Walkthrough.mp463.3MB

01 - What is Machine Learning/006 App Setup.mp48.13MB

01 - What is Machine Learning/007 Problem Outline.mp422.9MB

01 - What is Machine Learning/008 Identifying Relevant Data.mp433.91MB

01 - What is Machine Learning/009 Dataset Structures.mp448.24MB

01 - What is Machine Learning/010 Recording Observation Data.mp412.68MB

01 - What is Machine Learning/011 What Type of Problem.mp416.86MB

02 - Algorithm Overview/001 How K-Nearest Neighbor Works.mp469.73MB

02 - Algorithm Overview/002 Lodash Review.mp448.26MB

02 - Algorithm Overview/003 Implementing KNN.mp459.34MB

02 - Algorithm Overview/004 Finishing KNN Implementation.mp437.96MB

02 - Algorithm Overview/005 Testing the Algorithm.mp444.96MB

02 - Algorithm Overview/006 Interpreting Bad Results.mp425.65MB

02 - Algorithm Overview/007 Test and Training Data.mp427.37MB

02 - Algorithm Overview/008 Randomizing Test Data.mp413.46MB

02 - Algorithm Overview/009 Generalizing KNN.mp438.99MB

02 - Algorithm Overview/010 Gauging Accuracy.mp445.97MB

02 - Algorithm Overview/011 Printing a Report.mp433.29MB

02 - Algorithm Overview/012 Refactoring Accuracy Reporting.mp433.81MB

02 - Algorithm Overview/013 Investigating Optimal K Values.mp4112.41MB

02 - Algorithm Overview/014 Updating KNN for Multiple Features.mp448.83MB

02 - Algorithm Overview/015 Multi-Dimensional KNN.mp431.92MB

02 - Algorithm Overview/016 N-Dimension Distance.mp459.47MB

02 - Algorithm Overview/017 Arbitrary Feature Spaces.mp458.1MB

02 - Algorithm Overview/018 Magnitude Offsets in Features.mp446.23MB

02 - Algorithm Overview/019 Feature Normalization.mp454.09MB

02 - Algorithm Overview/020 Normalization with MinMax.mp454.4MB

02 - Algorithm Overview/021 Applying Normalization.mp438.91MB

02 - Algorithm Overview/022 Feature Selection with KNN.mp457.3MB

02 - Algorithm Overview/023 Objective Feature Picking.mp428.47MB

02 - Algorithm Overview/024 Evaluating Different Feature Values.mp421.01MB

03 - Onwards to Tensorflow JS!/001 Let's Get Our Bearings.mp446.8MB

03 - Onwards to Tensorflow JS!/002 A Plan to Move Forward.mp436.9MB

03 - Onwards to Tensorflow JS!/003 Tensor Shape and Dimension.mp470.3MB

03 - Onwards to Tensorflow JS!/004 Elementwise Operations.mp443.18MB

03 - Onwards to Tensorflow JS!/005 Broadcasting Operations.mp424.22MB

03 - Onwards to Tensorflow JS!/006 Logging Tensor Data.mp410.68MB

03 - Onwards to Tensorflow JS!/007 Tensor Accessors.mp410.92MB

03 - Onwards to Tensorflow JS!/008 Creating Slices of Data.mp427.97MB

03 - Onwards to Tensorflow JS!/009 Tensor Concatenation.mp427.84MB

03 - Onwards to Tensorflow JS!/010 Summing Values Along an Axis.mp430.04MB

03 - Onwards to Tensorflow JS!/011 Massaging Dimensions with ExpandDims.mp427.21MB

04 - Applications of Tensorflow/001 KNN with Regression.mp419.02MB

04 - Applications of Tensorflow/002 A Change in Data Structure.mp415.11MB

04 - Applications of Tensorflow/003 KNN with Tensorflow.mp459.55MB

04 - Applications of Tensorflow/004 Maintaining Order Relationships.mp436.11MB

04 - Applications of Tensorflow/005 Sorting Tensors.mp429.49MB

04 - Applications of Tensorflow/006 Averaging Top Values.mp447.53MB

04 - Applications of Tensorflow/007 Moving to the Editor.mp426.79MB

04 - Applications of Tensorflow/008 Loading CSV Data.mp468.81MB

04 - Applications of Tensorflow/009 Running an Analysis.mp420.82MB

04 - Applications of Tensorflow/010 Reporting Error Percentages.mp440.68MB

04 - Applications of Tensorflow/011 Normalization or Standardization.mp477.88MB

04 - Applications of Tensorflow/012 Numerical Standardization with Tensorflow.mp440.17MB

04 - Applications of Tensorflow/013 Applying Standardization.mp436.51MB

04 - Applications of Tensorflow/014 Debugging Calculations.mp475.03MB

04 - Applications of Tensorflow/015 What Now.mp417.76MB

05 - Getting Started with Gradient Descent/001 Linear Regression.mp49.7MB

05 - Getting Started with Gradient Descent/002 Why Linear Regression.mp429.91MB

05 - Getting Started with Gradient Descent/003 Understanding Gradient Descent.mp477.88MB

05 - Getting Started with Gradient Descent/004 Guessing Coefficients with MSE.mp470.81MB

05 - Getting Started with Gradient Descent/005 Observations Around MSE.mp421.44MB

05 - Getting Started with Gradient Descent/006 Derivatives!.mp420.72MB

05 - Getting Started with Gradient Descent/007 Gradient Descent in Action.mp489.2MB

05 - Getting Started with Gradient Descent/008 Quick Breather and Review.mp439.05MB

05 - Getting Started with Gradient Descent/009 Why a Learning Rate.mp4148.47MB

05 - Getting Started with Gradient Descent/010 Answering Common Questions.mp429.84MB

05 - Getting Started with Gradient Descent/011 Gradient Descent with Multiple Terms.mp416.76MB

05 - Getting Started with Gradient Descent/012 Multiple Terms in Action.mp4103.27MB

06 - Gradient Descent with Tensorflow/001 Project Overview.mp424.94MB

06 - Gradient Descent with Tensorflow/002 Data Loading.mp419.56MB

06 - Gradient Descent with Tensorflow/003 Default Algorithm Options.mp426.6MB

06 - Gradient Descent with Tensorflow/004 Formulating the Training Loop.mp48.7MB

06 - Gradient Descent with Tensorflow/005 Initial Gradient Descent Implementation.mp467.66MB

06 - Gradient Descent with Tensorflow/006 Calculating MSE Slopes.mp451.06MB

06 - Gradient Descent with Tensorflow/007 Updating Coefficients.mp426.02MB

06 - Gradient Descent with Tensorflow/008 Interpreting Results.mp489.66MB

06 - Gradient Descent with Tensorflow/009 Matrix Multiplication.mp430.58MB

06 - Gradient Descent with Tensorflow/010 More on Matrix Multiplication.mp443.58MB

06 - Gradient Descent with Tensorflow/011 Matrix Form of Slope Equations.mp422.66MB

06 - Gradient Descent with Tensorflow/012 Simplification with Matrix Multiplication.mp460.76MB

06 - Gradient Descent with Tensorflow/013 How it All Works Together!.mp4110.57MB

07 - Increasing Performance with Vectorized Solutions/001 Refactoring the Linear Regression Class.mp445.89MB

07 - Increasing Performance with Vectorized Solutions/002 Refactoring to One Equation.mp463.81MB

07 - Increasing Performance with Vectorized Solutions/003 A Few More Changes.mp458.55MB

07 - Increasing Performance with Vectorized Solutions/004 Same Results Or Not.mp412MB

07 - Increasing Performance with Vectorized Solutions/005 Calculating Model Accuracy.mp456.49MB

07 - Increasing Performance with Vectorized Solutions/006 Implementing Coefficient of Determination.mp447.48MB

07 - Increasing Performance with Vectorized Solutions/007 Dealing with Bad Accuracy.mp457.68MB

07 - Increasing Performance with Vectorized Solutions/008 Reminder on Standardization.mp418.83MB

07 - Increasing Performance with Vectorized Solutions/009 Data Processing in a Helper Method.mp423.5MB

07 - Increasing Performance with Vectorized Solutions/010 Reapplying Standardization.mp450.2MB

07 - Increasing Performance with Vectorized Solutions/011 Fixing Standardization Issues.mp440.99MB

07 - Increasing Performance with Vectorized Solutions/012 Massaging Learning Rates.mp422.77MB

07 - Increasing Performance with Vectorized Solutions/013 Moving Towards Multivariate Regression.mp484.99MB

07 - Increasing Performance with Vectorized Solutions/014 Refactoring for Multivariate Analysis.mp472.4MB

07 - Increasing Performance with Vectorized Solutions/015 Learning Rate Optimization.mp453.01MB

07 - Increasing Performance with Vectorized Solutions/016 Recording MSE History.mp432.67MB

07 - Increasing Performance with Vectorized Solutions/017 Updating Learning Rate.mp427.96MB

08 - Plotting Data with Javascript/001 Observing Changing Learning Rate and MSE.mp435.39MB

08 - Plotting Data with Javascript/002 Plotting MSE Values.mp439.65MB

08 - Plotting Data with Javascript/003 Plotting MSE History against B Values.mp436.13MB

09 - Gradient Descent Alterations/001 Batch and Stochastic Gradient Descent.mp463.66MB

09 - Gradient Descent Alterations/002 Refactoring Towards Batch Gradient Descent.mp423.6MB

09 - Gradient Descent Alterations/003 Determining Batch Size and Quantity.mp453.93MB

09 - Gradient Descent Alterations/004 Iterating Over Batches.mp426.41MB

09 - Gradient Descent Alterations/005 Evaluating Batch Gradient Descent Results.mp450.7MB

09 - Gradient Descent Alterations/006 Making Predictions with the Model.mp433.83MB

10 - Natural Binary Classification/001 Introducing Logistic Regression.mp48.95MB

10 - Natural Binary Classification/002 Logistic Regression in Action.mp417.76MB

10 - Natural Binary Classification/003 Bad Equation Fits.mp433.59MB

10 - Natural Binary Classification/004 The Sigmoid Equation.mp430.37MB

10 - Natural Binary Classification/005 Decision Boundaries.mp435.04MB

10 - Natural Binary Classification/006 Changes for Logistic Regression.mp43.39MB

10 - Natural Binary Classification/007 Project Setup for Logistic Regression.mp446.88MB

10 - Natural Binary Classification/009 Importing Vehicle Data.mp433.05MB

10 - Natural Binary Classification/010 Encoding Label Values.mp429.85MB

10 - Natural Binary Classification/011 Updating Linear Regression for Logistic Regression.mp454.31MB

10 - Natural Binary Classification/012 The Sigmoid Equation with Logistic Regression.mp423.29MB

10 - Natural Binary Classification/013 A Touch More Refactoring.mp478.65MB

10 - Natural Binary Classification/014 Gauging Classification Accuracy.mp422.32MB

10 - Natural Binary Classification/015 Implementing a Test Function.mp419.98MB

10 - Natural Binary Classification/016 Variable Decision Boundaries.mp458.41MB

10 - Natural Binary Classification/017 Mean Squared Error vs Cross Entropy.mp443.54MB

10 - Natural Binary Classification/018 Refactoring with Cross Entropy.mp438.21MB

10 - Natural Binary Classification/019 Finishing the Cost Refactor.mp442.11MB

10 - Natural Binary Classification/020 Plotting Changing Cost History.mp433.29MB

11 - Multi-Value Classification/001 Multinominal Logistic Regression.mp46.56MB

11 - Multi-Value Classification/002 A Smart Refactor to Multinominal Analysis.mp414.51MB

11 - Multi-Value Classification/003 A Smarter Refactor!.mp428.54MB

11 - Multi-Value Classification/004 A Single Instance Approach.mp474.37MB

11 - Multi-Value Classification/005 Refactoring to Multi-Column Weights.mp430.25MB

11 - Multi-Value Classification/006 A Problem to Test Multinominal Classification.mp428.84MB

11 - Multi-Value Classification/007 Classifying Continuous Values.mp419.67MB

11 - Multi-Value Classification/008 Training a Multinominal Model.mp441.17MB

11 - Multi-Value Classification/009 Marginal vs Conditional Probability.mp468.35MB

11 - Multi-Value Classification/010 Sigmoid vs Softmax.mp444.6MB

11 - Multi-Value Classification/011 Refactoring Sigmoid to Softmax.mp441.8MB

11 - Multi-Value Classification/012 Implementing Accuracy Gauges.mp421.99MB

11 - Multi-Value Classification/013 Calculating Accuracy.mp411.66MB

12 - Image Recognition In Action/001 Handwriting Recognition.mp48.35MB

12 - Image Recognition In Action/002 Greyscale Values.mp438.45MB

12 - Image Recognition In Action/003 Many Features.mp436.72MB

12 - Image Recognition In Action/004 Flattening Image Data.mp436.99MB

12 - Image Recognition In Action/005 Encoding Label Values.mp438.26MB

12 - Image Recognition In Action/006 Implementing an Accuracy Gauge.mp462.17MB

12 - Image Recognition In Action/007 Unchanging Accuracy.mp47.03MB

12 - Image Recognition In Action/008 Debugging the Calculation Process.mp477.59MB

12 - Image Recognition In Action/009 Dealing with Zero Variances.mp422.96MB

12 - Image Recognition In Action/010 Backfilling Variance.mp416.48MB

13 - Performance Optimization/001 Handing Large Datasets.mp418.46MB

13 - Performance Optimization/002 Minimizing Memory Usage.mp415.15MB

13 - Performance Optimization/003 Creating Memory Snapshots.mp421.77MB

13 - Performance Optimization/004 The Javascript Garbage Collector.mp422.56MB

13 - Performance Optimization/005 Shallow vs Retained Memory Usage.mp446.68MB

13 - Performance Optimization/006 Measuring Memory Usage.mp485.87MB

13 - Performance Optimization/007 Releasing References.mp431.88MB

13 - Performance Optimization/008 Measuring Footprint Reduction.mp426.87MB

13 - Performance Optimization/009 Optimization Tensorflow Memory Usage.mp411.44MB

13 - Performance Optimization/010 Tensorflow's Eager Memory Usage.mp420.13MB

13 - Performance Optimization/011 Cleaning up Tensors with Tidy.mp49.42MB

13 - Performance Optimization/012 Implementing TF Tidy.mp413.67MB

13 - Performance Optimization/013 Tidying the Training Loop.mp441.06MB

13 - Performance Optimization/014 Measuring Reduced Memory Usage.mp411MB

13 - Performance Optimization/015 One More Optimization.mp421.45MB

13 - Performance Optimization/016 Final Memory Report.mp421.11MB

13 - Performance Optimization/017 Plotting Cost History.mp443.33MB

13 - Performance Optimization/018 NaN in Cost History.mp435.86MB

13 - Performance Optimization/019 Fixing Cost History.mp430.58MB

13 - Performance Optimization/020 Massaging Learning Parameters.mp413.55MB

13 - Performance Optimization/021 Improving Model Accuracy.mp433.65MB

14 - Appendix Custom CSV Loader/001 Loading CSV Files.mp45.98MB

14 - Appendix Custom CSV Loader/002 A Test Dataset.mp43.71MB

14 - Appendix Custom CSV Loader/003 Reading Files from Disk.mp46.6MB

14 - Appendix Custom CSV Loader/004 Splitting into Columns.mp46.7MB

14 - Appendix Custom CSV Loader/005 Dropping Trailing Columns.mp47.67MB

14 - Appendix Custom CSV Loader/006 Parsing Number Values.mp424.02MB

14 - Appendix Custom CSV Loader/007 Custom Value Parsing.mp414.02MB

14 - Appendix Custom CSV Loader/008 Extracting Data Columns.mp450.66MB

14 - Appendix Custom CSV Loader/009 Shuffling Data via Seed Phrase.mp432.89MB

14 - Appendix Custom CSV Loader/010 Splitting Test and Training.mp448.31MB