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TensorFlow JS Unleashed: Beginners Machine Learning Masterclass
Mammoth Interactive Courses Introduction
00 About Mammoth Interactive (1:12)
Preface
01 How To Learn Online Effectively (13:46)
Source Files
Course Overview
01. What Is Machine Learning (6:39)
02. What You'll Learn (7:12)
03. What Is Tensorflow JS (4:27)
04. Load Tensorflow Object (4:27)
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(Prerequisite) Introduction to JavaScript
01. Introduction To The Course (1:09)
02. Introduction Of The Instructor (0:36)
03. Why Should You Learn Javascript (0:51)
04. Quick Win (0:58)
05. Course Requirements (0:42)
Source Files
(Prerequisite) Variables and Data Types
01. What Will We Learn In This Section (0:43)
02. Variables (10:21)
03. Data Types (5:39)
04. Variable Mutation (6:53)
05. Type Coercion (6:52)
06. Coding Challenge (1:36)
07. Coding Challenge Solution (2:42)
08. Section Summary (0:50)
Source Files
(Prerequisite) Operators
01. What Will We Learn In This Section (0:35)
02. Basic Operator (15:34)
03. Operator Precedence (5:43)
04. Coding Challenge (2:13)
05. Coding Challenge Solution (5:52)
06. Section Summary (0:56)
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(Prerequisite) Conditional Statements
01. What Will We Learn In This Section (0:35)
02. If Else Statements (11:46)
03. Boolean Logic (7:59)
04. Switch Statements (10:53)
05. Truthy And Falsie Values (6:03)
06. Equality Operators (4:55)
07. Coding Challenge (2:25)
08. Coding Challenge Solution (4:54)
09. Section Summary (1:14)
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(Prerequisite) Functions and Arrays
01. What Will We Learn In This Section (0:37)
02. Functions (9:47)
03. Function Statements And Expressions (7:38)
04. Arrays (10:08)
05. Coding Challenge (3:52)
06. Section Summary (1:29)
Source Files
(Prerequisite) Objects
01. What Will We Learn In This Section (0:49)
02. Objects And Properties (9:49)
03. Objects And Methods (12:26)
04. Objects Vs Primitives (16:19)
05. Coding Challenge (0:52)
06. Coding Challenge Solution (5:16)
07. Section Summary (0:44)
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(Prerequisite) Loops
01. What Will We Learn In This Section (0:38)
02. Loops (15:15)
03. Iteration (12:38)
04. Coding Challenge (1:05)
05. Coding Challenge Solution (6:33)
06. Section Summary (0:49)
Source Files
(Prerequisite) JavaScript Execution
01. What Will We Learn In This Section (0:57)
02. JavaScript Parsers And Engines (5:17)
03. Execution Contexts And Execution Stack (2:27)
04. Creation And Execution Phases (6:33)
05. Hoisting (2:14)
06. Scoping (4:53)
07. Scope Chain (3:21)
08. This Keyword (4:15)
09. Coding Challenge (0:47)
10. Coding Challenge Solution (3:22)
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Build Your First Tensors
01. Linear Algebra For Machine Learning (4:44)
02. Build Tensors (13:33)
03. Tensor Utility Methods (9:12)
04. Perform Math With Tensors (9:56)
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Visualize Data
01. Build A Scatter Plot (8:40)
02. Build A Bar Chart (5:33)
03. Build A Histogram (6:38)
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Train a Simple Model
01. Build Sample Data (5:15)
02. Build The Model (11:14)
03. Make A Prediction (7:46)
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Generate and Visualize Data
01. Generate Data (13:38)
02. Visualize Data (16:10)
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Build a Linear Regression Model
01. What Is Linear Regression (7:52)
02. Prepare Training Data (7:10)
03. Build The Model (14:05)
04. Make A Prediction (3:53)
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Visualize Linear Regression with User Input
01. Set Up The Canvas (3:47)
02. Draw A Data Sample (6:20)
03. Create Loss And Prediction Functions (6:00)
04. Collect User Input For Data (8:50)
05. Visualize Linear Regression With Dynamic Data (6:46)
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Visualize Polynomial Regression with User Input
01. Set Up The Canvas (11:00)
02. Visualize P Regression With Dynamic Data (16:32)
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Polynomial Regression
01. Generate Samples (6:21)
02. Generate A Prediction Equation With Weights (6:54)
03. Train The Model (5:25)
04. Visualize Predictions (18:01)
05. Visualize Prediction Error (10:00)
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Nearest Neighbors Image Classification with Tensorflow JS
01. Load Models Into Html (5:51)
02. Train Model On Images (13:13)
03. Make A Prediction (6:58)
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01. Linear Algebra For Machine Learning
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