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خانه » فروشگاه » Machine Learning with Javascript

Machine Learning with Javascript

سرفصل های دوره

Master Machine Learning from scratch using Javascript and TensorflowJS with hands-on projects.


01 - What is Machine Learning
  • 001 Getting Started - How to Get Help
  • 002 Course Resources.html
  • 002 diagrams.zip
  • 003 Join Our Community!.html
  • 004 Solving Machine Learning Problems
  • 005 A Complete Walkthrough
  • 006 App Setup
  • 007 Problem Outline
  • 008 Identifying Relevant Data
  • 009 Dataset Structures
  • 010 Recording Observation Data
  • 011 What Type of Problem

    • 02 - Algorithm Overview
  • 001 How K-Nearest Neighbor Works
  • 002 Lodash Review
  • 003 Implementing KNN
  • 004 Finishing KNN Implementation
  • 005 Testing the Algorithm
  • 006 Interpreting Bad Results
  • 007 Test and Training Data
  • 008 Randomizing Test Data
  • 009 Generalizing KNN
  • 010 Gauging Accuracy
  • 011 Printing a Report
  • 012 Refactoring Accuracy Reporting
  • 013 Investigating Optimal K Values
  • 014 Updating KNN for Multiple Features
  • 015 Multi-Dimensional KNN
  • 016 N-Dimension Distance
  • 017 Arbitrary Feature Spaces
  • 018 Magnitude Offsets in Features
  • 019 Feature Normalization
  • 020 Normalization with MinMax
  • 021 Applying Normalization
  • 022 Feature Selection with KNN
  • 023 Objective Feature Picking
  • 024 Evaluating Different Feature Values

    • 03 - Onwards to Tensorflow JS!
  • 001 Lets Get Our Bearings
  • 002 A Plan to Move Forward
  • 003 Tensor Shape and Dimension
  • 004 Elementwise Operations
  • 005 Broadcasting Operations
  • 006 Logging Tensor Data
  • 007 Tensor Accessors
  • 008 Creating Slices of Data
  • 009 Tensor Concatenation
  • 010 Summing Values Along an Axis
  • 011 Massaging Dimensions with ExpandDims

    • 04 - Applications of Tensorflow
  • 001 KNN with Regression
  • 002 A Change in Data Structure
  • 003 KNN with Tensorflow
  • 004 Maintaining Order Relationships
  • 005 Sorting Tensors
  • 006 Averaging Top Values
  • 007 Moving to the Editor
  • 008 Loading CSV Data
  • 009 Running an Analysis
  • 010 Reporting Error Percentages
  • 011 Normalization or Standardization
  • 012 Numerical Standardization with Tensorflow
  • 013 Applying Standardization
  • 014 Debugging Calculations
  • 015 What Now

    • 05 - Getting Started with Gradient Descent
  • 001 Linear Regression
  • 002 Why Linear Regression
  • 003 Understanding Gradient Descent
  • 004 Guessing Coefficients with MSE
  • 005 Observations Around MSE
  • 006 Derivatives!
  • 007 Gradient Descent in Action
  • 008 Quick Breather and Review
  • 009 Why a Learning Rate
  • 010 Answering Common Questions
  • 011 Gradient Descent with Multiple Terms
  • 012 Multiple Terms in Action

    • 06 - Gradient Descent with Tensorflow
  • 001 Project Overview
  • 002 Data Loading
  • 003 Default Algorithm Options
  • 004 Formulating the Training Loop
  • 005 Initial Gradient Descent Implementation
  • 006 Calculating MSE Slopes
  • 007 Updating Coefficients
  • 008 Interpreting Results
  • 009 Matrix Multiplication
  • 010 More on Matrix Multiplication
  • 011 Matrix Form of Slope Equations
  • 012 Simplification with Matrix Multiplication
  • 013 How it All Works Together!

    • 07 - Increasing Performance with Vectorized Solutions
  • 001 Refactoring the Linear Regression Class
  • 002 Refactoring to One Equation
  • 003 A Few More Changes
  • 004 Same Results Or Not
  • 005 Calculating Model Accuracy
  • 006 Implementing Coefficient of Determination
  • 007 Dealing with Bad Accuracy
  • 008 Reminder on Standardization
  • 009 Data Processing in a Helper Method
  • 010 Reapplying Standardization
  • 011 Fixing Standardization Issues
  • 012 Massaging Learning Rates
  • 013 Moving Towards Multivariate Regression
  • 014 Refactoring for Multivariate Analysis
  • 015 Learning Rate Optimization
  • 016 Recording MSE History
  • 017 Updating Learning Rate

    • 08 - Plotting Data with Javascript
  • 001 Observing Changing Learning Rate and MSE
  • 002 Plotting MSE Values
  • 003 Plotting MSE History against B Values

    • 09 - Gradient Descent Alterations
  • 001 Batch and Stochastic Gradient Descent
  • 002 Refactoring Towards Batch Gradient Descent
  • 003 Determining Batch Size and Quantity
  • 004 Iterating Over Batches
  • 005 Evaluating Batch Gradient Descent Results
  • 006 Making Predictions with the Model

    • 10 - Natural Binary Classification
  • 001 Introducing Logistic Regression
  • 002 Logistic Regression in Action
  • 003 Bad Equation Fits
  • 004 The Sigmoid Equation
  • 005 Decision Boundaries
  • 006 Changes for Logistic Regression
  • 007 Project Setup for Logistic Regression
  • 008 Project Download.html
  • 008 regressions.zip
  • 009 Importing Vehicle Data
  • 010 Encoding Label Values
  • 011 Updating Linear Regression for Logistic Regression
  • 012 The Sigmoid Equation with Logistic Regression
  • 013 A Touch More Refactoring
  • 014 Gauging Classification Accuracy
  • 015 Implementing a Test Function
  • 016 Variable Decision Boundaries
  • 017 Mean Squared Error vs Cross Entropy
  • 018 Refactoring with Cross Entropy
  • 019 Finishing the Cost Refactor
  • 020 Plotting Changing Cost History

    • 11 - Multi-Value Classification
  • 001 Multinominal Logistic Regression
  • 002 A Smart Refactor to Multinominal Analysis
  • 003 A Smarter Refactor!
  • 004 A Single Instance Approach
  • 005 Refactoring to Multi-Column Weights
  • 006 A Problem to Test Multinominal Classification
  • 007 Classifying Continuous Values
  • 008 Training a Multinominal Model
  • 009 Marginal vs Conditional Probability
  • 010 Sigmoid vs Softmax
  • 011 Refactoring Sigmoid to Softmax
  • 012 Implementing Accuracy Gauges
  • 013 Calculating Accuracy

    • 12 - Image Recognition In Action
  • 001 Handwriting Recognition
  • 002 Greyscale Values
  • 003 Many Features
  • 004 Flattening Image Data
  • 005 Encoding Label Values
  • 006 Implementing an Accuracy Gauge
  • 007 Unchanging Accuracy
  • 008 Debugging the Calculation Process
  • 009 Dealing with Zero Variances
  • 010 Backfilling Variance

    • 13 - Performance Optimization
  • 001 Handing Large Datasets
  • 002 Minimizing Memory Usage
  • 003 Creating Memory Snapshots
  • 004 The Javascript Garbage Collector
  • 005 Shallow vs Retained Memory Usage
  • 006 Measuring Memory Usage
  • 007 Releasing References
  • 008 Measuring Footprint Reduction
  • 009 Optimization Tensorflow Memory Usage
  • 010 Tensorflows Eager Memory Usage
  • 011 Cleaning up Tensors with Tidy
  • 012 Implementing TF Tidy
  • 013 Tidying the Training Loop
  • 014 Measuring Reduced Memory Usage
  • 015 One More Optimization
  • 016 Final Memory Report
  • 017 Plotting Cost History
  • 018 NaN in Cost History
  • 019 Fixing Cost History
  • 020 Massaging Learning Parameters
  • 021 Improving Model Accuracy

    • 14 - Appendix Custom CSV Loader
  • 001 Loading CSV Files
  • 002 A Test Dataset
  • 003 Reading Files from Disk
  • 004 Splitting into Columns
  • 005 Dropping Trailing Columns
  • 006 Parsing Number Values
  • 007 Custom Value Parsing
  • 008 Extracting Data Columns
  • 009 Shuffling Data via Seed Phrase
  • 010 Splitting Test and Training

    • 15 - Extras
  • 001 Bonus!.html
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    شناسه: 20777
    حجم: 6914 مگابایت
    مدت زمان: 1060 دقیقه
    تاریخ انتشار: ۱۵ مهر ۱۴۰۲
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