INTERACTIVE COURSE

Beginner

83 Lessons

6h

Certificate of Completion

AI Explanations

AI Explanations

41 Playgrounds

105 Illustrations

Course Overview

Machine learning is one of the fastest growing fields, and we cannot emphasize enough about its importance. This course aims to teach one of the fundamental concepts of machine learning, i.e., Neural Network. You will learn the basic concepts of building a model as well as the mathematical explanation behind Neural Network and based on that; you will build one from scratch (in Python). You will also learn how to train and optimize your network to achieve a better result.

Course Content

1

Prologue

2

A Little Background

Humans vs. ComputersA Simple Prediction MachineEstimate the Constant IterativelyClassify vs. PredictBuild a Simple ClassifierErrors in the Training ClassifierRefine the Parameters of the Training ClassifierSet Up a Learning Rate in the Training ClassifierLimitations of Linear ClassifiersRepresent Boolean Functions with Linear Classification

3

Let's Get Started!

Neurons: Nature’s Computing MachinesHow Neurons WorkActivation FunctionsReplicate Neurons in an Artificial ModelFollow Signals through a Simple NetworkCalculate Neural Network OutputMatrix MultiplicationCalculate the Inputs for Internal LayersA Three-Layer Example: Working on the Input LayerA Three-Layer Example: Working on the Hidden LayerA Three-Layer Example: Working on the Output Layer

4

Backward Propagation of Error

Learn Weights from More than One NodeError Backpropagation from More Output NodesBackpropagation: Splitting the ErrorBackpropagation: Recombine the ErrorError Backpropagation with Matrix Multiplication

5

Adjusting the Link Weights

Update WeightsEmbrace PessimismGradient Descent AlgorithmTransform the Output into an Error FunctionUse Gradient Descent to Update WeightsChoose the Right Weights IterativelyThe Error Slope between the Input and Hidden LayersWeight Updates CalculatedPrepare Data: Inputs and OutputsPrepare Data: Random Initial Weights

6

A Gentle Start with Python

7 Lessons

7

Neural Network with Python

8 Lessons

8

Testing Neural Network against MNIST Dataset

11 Lessons

9

Some Suggested Improvements

4 Lessons

10

Even More Fun!

4 Lessons

11

Epilogue

1 Lesson

12

Appendix: A Small Guide to Calculus

9 Lessons

COURSE AUTHOR

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