Machine Learning with Javascript
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       Video Length : 31h00m0s
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       Tasks Number : 200
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       Students Enrolled : 479
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       Medium Level
  • Curriculum
  • 1. What is Machine Learning?
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      Getting Started - How to Get Help
      10m0s
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      Solving Machine Learning Problems
      10m0s
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      A Complete Walkthrough
      10m0s
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      App Setup
      10m0s
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      Problem Outline
      10m0s
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      Identifying Relevant Data
      10m0s
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      Dataset Structures
      10m0s
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      Recording Observation Data
      10m0s
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      What Type of Problem?
      10m0s
  • 2. Algorithm Overview
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      How K-Nearest Neighbor Works
      10m0s
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      Lodash Review
      10m0s
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      Implementing KNN
      10m0s
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      Finishing KNN Implementation
      10m0s
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      Testing the Algorithm
      10m0s
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      Interpreting Bad Results
      10m0s
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      Test and Training Data
      10m0s
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      Randomizing Test Data
      10m0s
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      Generalizing KNN
      10m0s
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      Gauging Accuracy
      10m0s
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      Printing a Report
      10m0s
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      Refactoring Accuracy Reporting
      10m0s
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      Investigating Optimal K Values
      10m0s
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      Updating KNN for Multiple Features
      10m0s
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      Multi-Dimensional KNN
      10m0s
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      N-Dimension Distance
      10m0s
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      Arbitrary Feature Spaces
      10m0s
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      Magnitude Offsets in Features
      10m0s
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      Feature Normalization
      10m0s
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      Normalization with MinMax
      10m0s
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      Applying Normalization
      10m0s
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      Feature Selection with KNN
      10m0s
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      Objective Feature Picking
      10m0s
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      Evaluating Different Feature Values
      10m0s
  • 3. Onwards to Tensorflow JS!
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      Let's Get Our Bearings
      10m0s
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      A Plan to Move Forward
      10m0s
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      Tensor Shape and Dimension
      10m0s
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      Tensor Dimension and Shapes
      10m0s
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      Elementwise Operations
      10m0s
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      Broadcasting Operations
      10m0s
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      Broadcasting Elementwise Operations
      10m0s
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      Logging Tensor Data
      10m0s
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      Tensor Accessors
      10m0s
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      Creating Slices of Data
      10m0s
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      Tensor Concatenation
      10m0s
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      Summing Values Along an Axis
      10m0s
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      Massaging Dimensions with ExpandDims
      10m0s
  • 4. Applications of Tensorflow
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      KNN with Regression
      10m0s
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      A Change in Data Structure
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      KNN with Tensorflow
      10m0s
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      Maintaining Order Relationships
      10m0s
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      Sorting Tensors
      10m0s
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      Averaging Top Values
      10m0s
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      Moving to the Editor
      10m0s
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      Loading CSV Data
      10m0s
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      Running an Analysis
      10m0s
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      Reporting Error Percentages
      10m0s
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      Normalization or Standardization?
      10m0s
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      Numerical Standardization with Tensorflow
      10m0s
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      Applying Standardization
      10m0s
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      Debugging Calculations
      10m0s
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      What Now?
      10m0s
  • 5. Getting Started with Gradient Descent
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      Linear Regression
      10m0s
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      Why Linear Regression?
      10m0s
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      Understanding Gradient Descent
      10m0s
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      Guessing Coefficients with MSE
      10m0s
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      Observations Around MSE
      10m0s
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      Derivatives!
      10m0s
    • videocam
      Gradient Descent in Action
      10m0s
    • videocam
      Quick Breather and Review
      10m0s
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      Why a Learning Rate?
      10m0s
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      Answering Common Questions
      10m0s
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      Gradient Descent with Multiple Terms
      10m0s
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      Multiple Terms in Action
      10m0s
  • 6. Gradient Descent with Tensorflow
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      Project Overview
      10m0s
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      Data Loading
      10m0s
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      Default Algorithm Options
      10m0s
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      Formulating the Training Loop
      10m0s
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      Initial Gradient Descent Implementation
      10m0s
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      Calculating MSE Slopes
      10m0s
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      Updating Coefficients
      10m0s
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      Interpreting Results
      10m0s
    • videocam
      Matrix Multiplication
      10m0s
    • videocam
      More on Matrix Multiplication
      10m0s
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      Matrix Form of Slope Equations
      10m0s
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      Simplification with Matrix Multiplication
      10m0s
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      How it All Works Together!
      10m0s
  • 7. Increasing Performance with Vectorized Solutions
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      Refactoring the Linear Regression Class
      10m0s
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      Refactoring to One Equation
      10m0s
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      A Few More Changes
      10m0s
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      Same Results? Or Not?
      10m0s
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      Calculating Model Accuracy
      10m0s
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      Implementing Coefficient of Determination
      10m0s
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      Dealing with Bad Accuracy
      10m0s
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      Reminder on Standardization
      10m0s
    • videocam
      Data Processing in a Helper Method
      10m0s
    • videocam
      Reapplying Standardization
      10m0s
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      Fixing Standardization Issues
      10m0s
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      Massaging Learning Rates
      10m0s
    • videocam
      Moving Towards Multivariate Regression
      10m0s
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      Refactoring for Multivariate Analysis
      10m0s
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      Learning Rate Optimization
      10m0s
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      Recording MSE History
      10m0s
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      Updating Learning Rate
      10m0s
  • 8. Plotting Data with Javascript
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      Observing Changing Learning Rate and MSE
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      Plotting MSE Values
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      Plotting MSE History against B Values
      10m0s
  • 9. Gradient Descent Alterations
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      Batch and Stochastic Gradient Descent
      10m0s
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      Refactoring Towards Batch Gradient Descent
      10m0s
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      Determining Batch Size and Quantity
      10m0s
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      Iterating Over Batches
      10m0s
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      Evaluating Batch Gradient Descent Results
      10m0s
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      Making Predictions with the Model
      10m0s
  • 10. Natural Binary Classification
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      Introducing Logistic Regression
      10m0s
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      Logistic Regression in Action
      10m0s
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      Bad Equation Fits
      10m0s
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      The Sigmoid Equation
      10m0s
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      Decision Boundaries
      10m0s
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      Changes for Logistic Regression
      10m0s
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      Project Setup for Logistic Regression
      10m0s
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      Project Download
      10m0s
    • videocam
      Importing Vehicle Data
      10m0s
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      Encoding Label Values
      10m0s
    • videocam
      Updating Linear Regression fro Logistic Regression
      10m0s
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      The Sigmoid Equation with Logistic Regression
      10m0s
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      A Touch More Refactoring
      10m0s
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      Gauging Classification Accuracy
      10m0s
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      Implementing a Test Function
      10m0s
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      Variable Decision Boundaries
      10m0s
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      Mean Squared Error vs Cross Entropy
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      Refactoring with Cross Entropy
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      Finishing the Cost Refactor
      10m0s
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      Plotting Changing Cost History
      10m0s
  • 11. Multi-Value Classification
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      Multinominal Logistic Regression
      10m0s
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      A Smart Refactor to Multinominal Analysis
      10m0s
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      A Smarter Refactor!
      10m0s
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      A Single Instance Approach
      10m0s
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      Refactoring to Multi-Column Weights
      10m0s
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      A Problem to Test Multinominal Classification
      10m0s
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      Classifying Continuous Values
      10m0s
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      Training a Multinominal Model
      10m0s
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      Marginal vs Conditional Probability
      10m0s
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      Sigmoid vs Softmax
      10m0s
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      Refactoring Sigmoid to Softmax
      10m0s
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      Implementing Accuracy Gauges
      10m0s
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      Calculating Accuracy
      10m0s
  • 12. Image Recognition In Action
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      Handwriting Recognition
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      Greyscale Values
      10m0s
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      Many Features
      10m0s
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      Flattening Image Data
      10m0s
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      Encoding Label Values
      10m0s
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      Implementing an Accuracy Gauge
      10m0s
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      Unchanging Accuracy
      10m0s
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      Debugging the Calculation Process
      10m0s
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      Dealing with Zero Variances
      10m0s
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      Backfilling Variance
      10m0s
  • 13. Performance Optimization
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      Handing Large Datasets
      10m0s
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      Minimizing Memory Usage
      10m0s
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      Creating Memory Snapshots
      10m0s
    • videocam
      The Javascript Garbage Collector
      10m0s
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      Shallow vs Retained Memory Usage
      10m0s
    • videocam
      Measuring Memory Usage
      10m0s
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      Releasing References
      10m0s
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      Measuring Footprint Reduction
      10m0s
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      Optimization Tensorflow Memory Usage
      10m0s
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      Tensorflow's Eager Memory Usage
      10m0s
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      Cleaning up Tensors with Tidy
      10m0s
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      Implementing TF Tidy
      10m0s
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      Tidying the Training Loop
      10m0s
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      Measuring Reduced Memory Usage
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      One More Optimization
      10m0s
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      Final Memory Report
      10m0s
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      Plotting Cost History
      10m0s
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      NaN in Cost History
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      Fixing Cost History
      10m0s
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      Massaging Learning Parameters
      10m0s
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      Improving Model Accuracy
      10m0s
  • 14. Appendix: Custom CSV Loader
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      Loading CSV Files
      10m0s
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      A Test Dataset
      10m0s
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      Reading Files from Disk
      10m0s
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      Splitting into Columns
      10m0s
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      Dropping Trailing Columns
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      Parsing Number Values
      10m0s
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      Custom Value Parsing
      10m0s
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      Extracting Data Columns
      10m0s
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      Shuffling Data via Seed Phrase
      10m0s
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      Splitting Test and Training
      10m0s
Authors

Kevin Gautama is a systems design and programming engineer with 16 years of expertise in the fields of electrical and electronics and information technology.

He teaches at the Hanoi University of Industry in the period 2003-2011 and he has a certificate of vocational training by the Ministry of Industry and Commerce and the Hanoi University of Industry.

From extensive design experience through numerous engineering projects, the author founded the Enziin Academy.

The Enziin Academy is a startup in the field of educational, it's core goal is to training design engineers in the fields technology related.

The Enziin Academy is headquartered in Stockholm-Sweden with an orientation operating multi-lingual and global.

The author's skills in IT:

  • Implementing the application infrastructure on Amazon's cloud computing platform.
  • Linux server system administration (Sysadmin).
  • Design load balancing and content distribution system.
  • MySQL database administration.
  • C/C++/C# Programming
  • Ruby and Ruby on Rails Programming
  • Python and Django Programming
  • The WPF/C# on the .NET Framework Programming
  • The PHP/JAVA Programming
  • Machine Learning and Expert System.
  • Internet of Things.

The author's skills in the fields of electric and electronic:

  • The design of popular CPU / MCU systems.
  • Design FPGA / CPLD system (Xilinx - Altera).
  • Design and programming of DSP systems (Texas Instruments).
  • Embedded ARM system design.
  • The RTOS Programming
  • Design and programming electronic power systems.
  • PLC - inverter - sensor - electric control cabinet industrial.
  • Control systems distributed connection with Server.

Read more...

Machine Learning with Javascript


Machine Learning with Javascript

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