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[JavaScript TensorFlowJS 机器学习 - 暂无字幕 - Udemy] Machine Learning with JavaScript

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2019-09-21 07:07:38
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Master Machine Learning from scratch using Javascript and TensorFlowJS with hands-on projects.
学霸修炼挑战
课程
教学视频
野生技术协会
机器学习
深度学习
JavaScript
学习心得
Tensorflow.js
哈库那玛塔塔i 发消息
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4万人感兴趣
视频选集
（93/183）
001 Getting Started - How to Get Help
00:58
002 Solving Machine Learning Problems
06:05
003 A Complete Walkthrough
09:54
004 App Setup
02:02
005 Problem Outline
02:54
006 Identifying Relevant Data
04:12
007 Dataset Structures
05:48
008 Recording Observation Data
04:00
009 What Type of Problem
04:37
010 How K-Nearest Neighbor Works
08:24
011 Lodash Review
09:57
012 Implementing KNN
07:17
013 Finishing KNN Implementation
05:54
014 Testing the Algorithm
04:49
015 Interpreting Bad Results
04:13
016 Test and Training Data
04:06
017 Randomizing Test Data
03:49
018 Generalizing KNN
03:42
019 Gauging Accuracy
05:19
020 Printing a Report
03:30
021 Refactoring Accuracy Reporting
05:14
022 Investigating Optimal K Values
11:39
023 Updating KNN for Multiple Features
06:37
024 Multi-Dimensional KNN
03:57
025 N-Dimension Distance
09:51
026 Arbitrary Feature Spaces
08:28
027 Magnitude Offsets in Features
05:37
028 Feature Normalization
07:33
029 Normalization with MinMax
07:15
030 Applying Normalization
04:23
031 Feature Selection with KNN
07:48
032 Objective Feature Picking
06:11
033 Evaluating Different Feature Values
02:54
034 Lets Get Our Bearings
07:28
035 A Plan to Move Forward
04:32
036 Tensor Shape and Dimension
12:05
037 Elementwise Operations
08:19
038 Broadcasting Operations
06:48
039 Logging Tensor Data
03:48
040 Tensor Accessors
05:25
041 Creating Slices of Data
07:48
042 Tensor Concatenation
05:29
043 Summing Values Along an Axis
05:14
044 Massaging Dimensions with ExpandDims
07:48
045 KNN with Regression
04:57
046 A Change in Data Structure
04:06
047 KNN with Tensorflow
09:19
048 Maintaining Order Relationships
06:32
049 Sorting Tensors
08:01
050 Averaging Top Values
07:44
051 Moving to the Editor
03:27
052 Loading CSV Data
10:11
053 Running an Analysis
06:11
054 Reporting Error Percentages
06:27
055 Normalization or Standardization
07:34
056 Numerical Standardization with Tensorflow
07:38
057 Applying Standardization
04:02
058 Debugging Calculations
08:15
059 What Now
04:01
060 Linear Regression
02:40
061 Why Linear Regression
04:53
062 Understanding Gradient Descent
13:05
063 Guessing Coefficients with MSE
10:20
064 Observations Around MSE
05:57
065 Derivatives
07:13
066 Gradient Descent in Action
11:47
067 Quick Breather and Review
05:47
068 Why a Learning Rate
17:06
069 Answering Common Questions
03:49
070 Gradient Descent with Multiple Terms
04:44
071 Multiple Terms in Action
10:40
072 Project Overview
06:02
073 Data Loading
05:18
074 Default Algorithm Options
08:33
075 Formulating the Training Loop
03:19
076 Initial Gradient Descent Implementation
09:25
077 Calculating MSE Slopes
06:53
078 Updating Coefficients
03:12
079 Interpreting Results
10:08
080 Matrix Multiplication
07:10
081 More on Matrix Multiplication
06:41
082 Matrix Form of Slope Equations
06:22
083 Simplification with Matrix Multiplication
09:29
084 How it All Works Together
14:02
085 Refactoring the Linear Regression Class
07:41
086 Refactoring to One Equation
08:59
087 A Few More Changes
06:14
088 Same Results Or Not
03:20
089 Calculating Model Accuracy
08:38
090 Implementing Coefficient of Determination
07:45
091 Dealing with Bad Accuracy
07:48
092 Reminder on Standardization
04:37
093 Data Processing in a Helper Method
03:40
094 Reapplying Standardization
05:58
095 Fixing Standardization Issues
05:37
096 Massaging Learning Rates
03:16
097 Moving Towards Multivariate Regression
11:45
098 Refactoring for Multivariate Analysis
07:29
099 Learning Rate Optimization
08:05
100 Recording MSE History
05:22
101 Updating Learning Rate
06:42
102 Observing Changing Learning Rate and MSE
04:18
103 Plotting MSE Values
05:22
104 Plotting MSE History against B Values
04:23
105 Batch and Stochastic Gradient Descent
07:18
106 Refactoring Towards Batch Gradient Descent
05:08
107 Determining Batch Size and Quantity
06:03
108 Iterating Over Batches
07:49
109 Evaluating Batch Gradient Descent Results
05:42
110 Making Predictions with the Model
07:38
111 Introducing Logistic Regression
02:28
112 Logistic Regression in Action
06:32
113 Bad Equation Fits
05:32
114 The Sigmoid Equation
04:32
115 Decision Boundaries
07:48
116 Changes for Logistic Regression
01:12
117 Project Setup for Logistic Regression
05:52
119 Importing Vehicle Data
04:28
120 Encoding Label Values
04:19
121 Updating Linear Regression fro Logistic Regression
07:09
122 The Sigmoid Equation with Logistic Regression
04:28
123 A Touch More Refactoring
07:47
124 Gauging Classification Accuracy
03:28
125 Implementing a Test Function
05:17
126 Variable Decision Boundaries
07:17
127 Mean Squared Error vs Cross Entropy
05:47
128 Refactoring with Cross Entropy
05:09
129 Finishing the Cost Refactor
04:37
130 Plotting Changing Cost History
03:25
131 Multinominal Logistic Regression
02:20
132 A Smart Refactor to Multinominal Analysis
05:08
133 A Smarter Refactor
03:46
134 A Single Instance Approach
09:51
135 Refactoring to Multi-Column Weights
04:40
136 A Problem to Test Multinominal Classification
04:38
137 Classifying Continuous Values
04:42
138 Training a Multinominal Model
06:20
139 Marginal vs Conditional Probability
09:57
140 Sigmoid vs Softmax
06:09
141 Refactoring Sigmoid to Softmax
04:43
142 Implementing Accuracy Gauges
02:37
143 Calculating Accuracy
03:16
144 Handwriting Recognition
02:11
145 Greyscale Values
05:12
146 Many Features
03:30
147 Flattening Image Data
06:07
148 Encoding Label Values
05:45
149 Implementing an Accuracy Gauge
07:27
150 Unchanging Accuracy
01:56
151 Debugging the Calculation Process
08:13
152 Dealing with Zero Variances
06:17
153 Backfilling Variance
02:37
154 Handing Large Datasets
04:15
155 Minimizing Memory Usage
04:51
156 Creating Memory Snapshots
05:16
157 The Javascript Garbage Collector
06:50
158 Shallow vs Retained Memory Usage
05:51
159 Measuring Memory Usage
08:30
160 Releasing References
03:15
161 Measuring Footprint Reduction
03:51
162 Optimization Tensorflow Memory Usage
01:32
163 Tensorflows Eager Memory Usage
04:41
164 Cleaning up Tensors with Tidy
02:49
165 Implementing TF Tidy
03:32
166 Tidying the Training Loop
03:58
167 Measuring Reduced Memory Usage
01:35
168 One More Optimization
02:36
169 Final Memory Report
02:45
170 Plotting Cost History
04:04
171 NaN in Cost History
04:20
172 Fixing Cost History
04:47
173 Massaging Learning Parameters
01:41
174 Improving Model Accuracy
04:28
175 Loading CSV Files
02:07
176 A Test Dataset
02:01
177 Reading Files from Disk
03:09
178 Splitting into Columns
02:55
179 Dropping Trailing Columns
02:31
180 Parsing Number Values
03:37
181 Custom Value Parsing
04:21
182 Extracting Data Columns
05:36
183 Shuffling Data via Seed Phrase
05:14
184 Splitting Test and Training
07:45

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