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种子名称： [FreeTutorials.Eu] Udemy - machine-learning-with-javascript
文件类型： 视频
文件数目： 183个文件
文件大小： 10.1 GB
收录时间： 2018-12-24 20:19
已经下载： 3次
资源热度： 165
最近下载： 2024-11-14 02:10

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[FreeTutorials.Eu] 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/002 Solving Machine Learning Problems.mp462.77MB

01 What is Machine Learning/003 A Complete Walkthrough.mp4109.13MB

01 What is Machine Learning/004 App Setup.mp419.27MB

01 What is Machine Learning/005 Problem Outline.mp431.21MB

01 What is Machine Learning/006 Identifying Relevant Data.mp433.91MB

01 What is Machine Learning/007 Dataset Structures.mp448.24MB

01 What is Machine Learning/008 Recording Observation Data.mp432.74MB

01 What is Machine Learning/009 What Type of Problem.mp447.03MB

02 Algorithm Overview/010 How K-Nearest Neighbor Works.mp493.32MB

02 Algorithm Overview/011 Lodash Review.mp464.93MB

02 Algorithm Overview/012 Implementing KNN.mp459.34MB

02 Algorithm Overview/013 Finishing KNN Implementation.mp450.28MB

02 Algorithm Overview/014 Testing the Algorithm.mp444.96MB

02 Algorithm Overview/015 Interpreting Bad Results.mp440.75MB

02 Algorithm Overview/016 Test and Training Data.mp445.2MB

02 Algorithm Overview/017 Randomizing Test Data.mp436MB

02 Algorithm Overview/018 Generalizing KNN.mp438.99MB

02 Algorithm Overview/019 Gauging Accuracy.mp454.01MB

02 Algorithm Overview/020 Printing a Report.mp433.29MB

02 Algorithm Overview/021 Refactoring Accuracy Reporting.mp452.3MB

02 Algorithm Overview/022 Investigating Optimal K Values.mp4129.13MB

02 Algorithm Overview/023 Updating KNN for Multiple Features.mp470.61MB

02 Algorithm Overview/024 Multi-Dimensional KNN.mp444.2MB

02 Algorithm Overview/025 N-Dimension Distance.mp478.87MB

02 Algorithm Overview/026 Arbitrary Feature Spaces.mp471.25MB

02 Algorithm Overview/027 Magnitude Offsets in Features.mp464.06MB

02 Algorithm Overview/028 Feature Normalization.mp472.91MB

02 Algorithm Overview/029 Normalization with MinMax.mp467.04MB

02 Algorithm Overview/030 Applying Normalization.mp445.35MB

02 Algorithm Overview/031 Feature Selection with KNN.mp480.36MB

02 Algorithm Overview/032 Objective Feature Picking.mp465.97MB

02 Algorithm Overview/033 Evaluating Different Feature Values.mp427.97MB

03 Onwards to Tensorflow JS/034 Lets Get Our Bearings.mp476.61MB

03 Onwards to Tensorflow JS/035 A Plan to Move Forward.mp448.65MB

03 Onwards to Tensorflow JS/036 Tensor Shape and Dimension.mp4114.28MB

03 Onwards to Tensorflow JS/037 Elementwise Operations.mp458.36MB

03 Onwards to Tensorflow JS/038 Broadcasting Operations.mp462.05MB

03 Onwards to Tensorflow JS/039 Logging Tensor Data.mp426MB

03 Onwards to Tensorflow JS/040 Tensor Accessors.mp430.46MB

03 Onwards to Tensorflow JS/041 Creating Slices of Data.mp458.91MB

03 Onwards to Tensorflow JS/042 Tensor Concatenation.mp444.13MB

03 Onwards to Tensorflow JS/043 Summing Values Along an Axis.mp441.36MB

03 Onwards to Tensorflow JS/044 Massaging Dimensions with ExpandDims.mp457.01MB

04 Applications of Tensorflow/045 KNN with Regression.mp454.98MB

04 Applications of Tensorflow/046 A Change in Data Structure.mp441.34MB

04 Applications of Tensorflow/047 KNN with Tensorflow.mp478.71MB

04 Applications of Tensorflow/048 Maintaining Order Relationships.mp457.75MB

04 Applications of Tensorflow/049 Sorting Tensors.mp462.84MB

04 Applications of Tensorflow/050 Averaging Top Values.mp458.13MB

04 Applications of Tensorflow/051 Moving to the Editor.mp434.33MB

04 Applications of Tensorflow/052 Loading CSV Data.mp489.32MB

04 Applications of Tensorflow/053 Running an Analysis.mp452.5MB

04 Applications of Tensorflow/054 Reporting Error Percentages.mp464.49MB

04 Applications of Tensorflow/055 Normalization or Standardization.mp492.97MB

04 Applications of Tensorflow/056 Numerical Standardization with Tensorflow.mp453.05MB

04 Applications of Tensorflow/057 Applying Standardization.mp441.46MB

04 Applications of Tensorflow/058 Debugging Calculations.mp486.71MB

04 Applications of Tensorflow/059 What Now.mp442.32MB

05 Getting Started with Gradient Descent/060 Linear Regression.mp425.38MB

05 Getting Started with Gradient Descent/061 Why Linear Regression.mp450.34MB

05 Getting Started with Gradient Descent/062 Understanding Gradient Descent.mp4126.76MB

05 Getting Started with Gradient Descent/063 Guessing Coefficients with MSE.mp493.46MB

05 Getting Started with Gradient Descent/064 Observations Around MSE.mp456.11MB

05 Getting Started with Gradient Descent/065 Derivatives.mp477.95MB

05 Getting Started with Gradient Descent/066 Gradient Descent in Action.mp4115.35MB

05 Getting Started with Gradient Descent/067 Quick Breather and Review.mp465.79MB

05 Getting Started with Gradient Descent/068 Why a Learning Rate.mp4187.28MB

05 Getting Started with Gradient Descent/069 Answering Common Questions.mp440.94MB

05 Getting Started with Gradient Descent/070 Gradient Descent with Multiple Terms.mp444.19MB

05 Getting Started with Gradient Descent/071 Multiple Terms in Action.mp4123.15MB

06 Gradient Descent with Tensorflow/072 Project Overview.mp457.04MB

06 Gradient Descent with Tensorflow/073 Data Loading.mp443.48MB

06 Gradient Descent with Tensorflow/074 Default Algorithm Options.mp462.66MB

06 Gradient Descent with Tensorflow/075 Formulating the Training Loop.mp427.67MB

06 Gradient Descent with Tensorflow/076 Initial Gradient Descent Implementation.mp487.92MB

06 Gradient Descent with Tensorflow/077 Calculating MSE Slopes.mp467.13MB

06 Gradient Descent with Tensorflow/078 Updating Coefficients.mp433.86MB

06 Gradient Descent with Tensorflow/079 Interpreting Results.mp4101.71MB

06 Gradient Descent with Tensorflow/080 Matrix Multiplication.mp467.46MB

06 Gradient Descent with Tensorflow/081 More on Matrix Multiplication.mp463.24MB

06 Gradient Descent with Tensorflow/082 Matrix Form of Slope Equations.mp459.6MB

06 Gradient Descent with Tensorflow/083 Simplification with Matrix Multiplication.mp490.79MB

06 Gradient Descent with Tensorflow/084 How it All Works Together.mp4143.82MB

07 Increasing Performance with Vectorized Solutions/085 Refactoring the Linear Regression Class.mp472.71MB

07 Increasing Performance with Vectorized Solutions/086 Refactoring to One Equation.mp484.8MB

07 Increasing Performance with Vectorized Solutions/087 A Few More Changes.mp466.16MB

07 Increasing Performance with Vectorized Solutions/088 Same Results Or Not.mp433.83MB

07 Increasing Performance with Vectorized Solutions/089 Calculating Model Accuracy.mp480.36MB

07 Increasing Performance with Vectorized Solutions/090 Implementing Coefficient of Determination.mp475.78MB

07 Increasing Performance with Vectorized Solutions/091 Dealing with Bad Accuracy.mp471.41MB

07 Increasing Performance with Vectorized Solutions/092 Reminder on Standardization.mp444.49MB

07 Increasing Performance with Vectorized Solutions/093 Data Processing in a Helper Method.mp437.17MB

07 Increasing Performance with Vectorized Solutions/094 Reapplying Standardization.mp457.96MB

07 Increasing Performance with Vectorized Solutions/095 Fixing Standardization Issues.mp447.84MB

07 Increasing Performance with Vectorized Solutions/096 Massaging Learning Rates.mp436.44MB

07 Increasing Performance with Vectorized Solutions/097 Moving Towards Multivariate Regression.mp4121.42MB

07 Increasing Performance with Vectorized Solutions/098 Refactoring for Multivariate Analysis.mp482.35MB

07 Increasing Performance with Vectorized Solutions/099 Learning Rate Optimization.mp476.69MB

07 Increasing Performance with Vectorized Solutions/100 Recording MSE History.mp451.94MB

07 Increasing Performance with Vectorized Solutions/101 Updating Learning Rate.mp462.14MB

08 Plotting Data with Javascript/102 Observing Changing Learning Rate and MSE.mp445.83MB

08 Plotting Data with Javascript/103 Plotting MSE Values.mp461.39MB

08 Plotting Data with Javascript/104 Plotting MSE History against B Values.mp447.8MB

09 Gradient Descent Alterations/105 Batch and Stochastic Gradient Descent.mp477.23MB

09 Gradient Descent Alterations/106 Refactoring Towards Batch Gradient Descent.mp455.11MB

09 Gradient Descent Alterations/107 Determining Batch Size and Quantity.mp466.08MB

09 Gradient Descent Alterations/108 Iterating Over Batches.mp467.45MB

09 Gradient Descent Alterations/109 Evaluating Batch Gradient Descent Results.mp466.23MB

09 Gradient Descent Alterations/110 Making Predictions with the Model.mp479.49MB

10 Natural Binary Classification/111 Introducing Logistic Regression.mp423.44MB

10 Natural Binary Classification/112 Logistic Regression in Action.mp461.07MB

10 Natural Binary Classification/113 Bad Equation Fits.mp455.39MB

10 Natural Binary Classification/114 The Sigmoid Equation.mp445.44MB

10 Natural Binary Classification/115 Decision Boundaries.mp479.18MB

10 Natural Binary Classification/116 Changes for Logistic Regression.mp412.49MB

10 Natural Binary Classification/117 Project Setup for Logistic Regression.mp459.4MB

10 Natural Binary Classification/119 Importing Vehicle Data.mp438.95MB

10 Natural Binary Classification/120 Encoding Label Values.mp448.58MB

10 Natural Binary Classification/121 Updating Linear Regression fro Logistic Regression.mp470.29MB

10 Natural Binary Classification/122 The Sigmoid Equation with Logistic Regression.mp432.77MB

10 Natural Binary Classification/123 A Touch More Refactoring.mp487.42MB

10 Natural Binary Classification/124 Gauging Classification Accuracy.mp436.7MB

10 Natural Binary Classification/125 Implementing a Test Function.mp454.71MB

10 Natural Binary Classification/126 Variable Decision Boundaries.mp468.31MB

10 Natural Binary Classification/127 Mean Squared Error vs Cross Entropy.mp460.2MB

10 Natural Binary Classification/128 Refactoring with Cross Entropy.mp449.45MB

10 Natural Binary Classification/129 Finishing the Cost Refactor.mp449.09MB

10 Natural Binary Classification/130 Plotting Changing Cost History.mp442.95MB

11 Multi-Value Classification/131 Multinominal Logistic Regression.mp425MB

11 Multi-Value Classification/132 A Smart Refactor to Multinominal Analysis.mp449.97MB

11 Multi-Value Classification/133 A Smarter Refactor.mp438.29MB

11 Multi-Value Classification/134 A Single Instance Approach.mp4103.55MB

11 Multi-Value Classification/135 Refactoring to Multi-Column Weights.mp448.49MB

11 Multi-Value Classification/136 A Problem to Test Multinominal Classification.mp448.45MB

11 Multi-Value Classification/137 Classifying Continuous Values.mp444.55MB

11 Multi-Value Classification/138 Training a Multinominal Model.mp466.08MB

11 Multi-Value Classification/139 Marginal vs Conditional Probability.mp495.18MB

11 Multi-Value Classification/140 Sigmoid vs Softmax.mp462.75MB

11 Multi-Value Classification/141 Refactoring Sigmoid to Softmax.mp448.87MB

11 Multi-Value Classification/142 Implementing Accuracy Gauges.mp428.71MB

11 Multi-Value Classification/143 Calculating Accuracy.mp431.3MB

12 Image Recognition In Action/144 Handwriting Recognition.mp424.69MB

12 Image Recognition In Action/145 Greyscale Values.mp455.34MB

12 Image Recognition In Action/146 Many Features.mp444.76MB

12 Image Recognition In Action/147 Flattening Image Data.mp457.76MB

12 Image Recognition In Action/148 Encoding Label Values.mp462MB

12 Image Recognition In Action/149 Implementing an Accuracy Gauge.mp479.94MB

12 Image Recognition In Action/150 Unchanging Accuracy.mp420.3MB

12 Image Recognition In Action/151 Debugging the Calculation Process.mp489.04MB

12 Image Recognition In Action/152 Dealing with Zero Variances.mp447.9MB

12 Image Recognition In Action/153 Backfilling Variance.mp425.72MB

13 Performance Optimization/154 Handing Large Datasets.mp444.46MB

13 Performance Optimization/155 Minimizing Memory Usage.mp438.18MB

13 Performance Optimization/156 Creating Memory Snapshots.mp449.05MB

13 Performance Optimization/157 The Javascript Garbage Collector.mp455.8MB

13 Performance Optimization/158 Shallow vs Retained Memory Usage.mp456.89MB

13 Performance Optimization/159 Measuring Memory Usage.mp496.63MB

13 Performance Optimization/160 Releasing References.mp435.98MB

13 Performance Optimization/161 Measuring Footprint Reduction.mp443.3MB

13 Performance Optimization/162 Optimization Tensorflow Memory Usage.mp418.53MB

13 Performance Optimization/163 Tensorflows Eager Memory Usage.mp446.81MB

13 Performance Optimization/164 Cleaning up Tensors with Tidy.mp424.26MB

13 Performance Optimization/165 Implementing TF Tidy.mp437.59MB

13 Performance Optimization/166 Tidying the Training Loop.mp445.99MB

13 Performance Optimization/167 Measuring Reduced Memory Usage.mp418.11MB

13 Performance Optimization/168 One More Optimization.mp427.49MB

13 Performance Optimization/169 Final Memory Report.mp436.24MB

13 Performance Optimization/170 Plotting Cost History.mp447.59MB

13 Performance Optimization/171 NaN in Cost History.mp446.37MB

13 Performance Optimization/172 Fixing Cost History.mp446.77MB

13 Performance Optimization/173 Massaging Learning Parameters.mp422.55MB

13 Performance Optimization/174 Improving Model Accuracy.mp455.01MB

14 Appendix Custom CSV Loader/175 Loading CSV Files.mp415.85MB

14 Appendix Custom CSV Loader/176 A Test Dataset.mp49.58MB

14 Appendix Custom CSV Loader/177 Reading Files from Disk.mp418.59MB

14 Appendix Custom CSV Loader/178 Splitting into Columns.mp420.34MB

14 Appendix Custom CSV Loader/179 Dropping Trailing Columns.mp418.4MB

14 Appendix Custom CSV Loader/180 Parsing Number Values.mp431.36MB

14 Appendix Custom CSV Loader/181 Custom Value Parsing.mp436.72MB

14 Appendix Custom CSV Loader/182 Extracting Data Columns.mp457.27MB

14 Appendix Custom CSV Loader/183 Shuffling Data via Seed Phrase.mp452.14MB

14 Appendix Custom CSV Loader/184 Splitting Test and Training.mp475.65MB