Unleash Machine Learning- Build Artificial Neuron in Python

Unleash Machine Learning: Build Artificial Neuron in Python, available at $19.99, has an average rating of 4.25, with 28 lectures, 1 quizzes, based on 144 reviews, and has 1989 subscribers.

You will learn about Build from scratch your own Artificial Neural Network Know the fundamentals of Machine Learning and ANN Train your ANN using 3 different datasets with increasing complexity Predict the correct output using your trained ANN Evaluate the accuracy of your predictions Use scikit-learn, numpy and opencv This course is ideal for individuals who are SHOULD NOT: beginners in Python or SHOULD NOT: experts in Machine Learning or SHOULD: students that want to begin Machine Learning with concepts and tools or SHOULD: students who want to learn and gain insights into why Artificial Neural Networks are such a powerful and unique tool It is particularly useful for SHOULD NOT: beginners in Python or SHOULD NOT: experts in Machine Learning or SHOULD: students that want to begin Machine Learning with concepts and tools or SHOULD: students who want to learn and gain insights into why Artificial Neural Networks are such a powerful and unique tool.

Enroll now: Unleash Machine Learning: Build Artificial Neuron in Python

Summary

Title: Unleash Machine Learning: Build Artificial Neuron in Python

Price: $19.99

Average Rating: 4.25

Number of Lectures: 28

Number of Quizzes: 1

Number of Published Lectures: 28

Number of Published Quizzes: 1

Number of Curriculum Items: 29

Number of Published Curriculum Objects: 29

Original Price: $129.99

Quality Status: approved

Status: Live

What You Will Learn

Build from scratch your own Artificial Neural Network

Know the fundamentals of Machine Learning and ANN

Train your ANN using 3 different datasets with increasing complexity

Predict the correct output using your trained ANN

Evaluate the accuracy of your predictions

Use scikit-learn, numpy and opencv

Who Should Attend

SHOULD NOT: beginners in Python

SHOULD NOT: experts in Machine Learning

SHOULD: students that want to begin Machine Learning with concepts and tools

SHOULD: students who want to learn and gain insights into why Artificial Neural Networks are such a powerful and unique tool

Target Audiences

SHOULD NOT: beginners in Python

SHOULD NOT: experts in Machine Learning

SHOULD: students that want to begin Machine Learning with concepts and tools

SHOULD: students who want to learn and gain insights into why Artificial Neural Networks are such a powerful and unique tool

Cars that drive themselves hundreds of miles with no accidents?

Algorithms that recognize objects and faces from images with better performance than humans?

All possible thanks to Machine Learning!

In this course you will begin Machine Learning by implementing and using your own Artificial Neuronal Networkfor beginners.

In this Artificial Neuronal Network course you will:

1. understand intuitively and mathematically the fundamentals of ANN
2. implement from scratch a multi layer neuronal network in Python
3. load and visually explore different datasets
4. transform the data
5. train you network and use it to make predictions
6. measure the accuracy of your predictions
7. use machine learning tools and techniques

Jump in directly:

All sourcecode and notebooks on public GitHub

Apply Machine Learning: section 4

Implement the ANN: section 3

Full ride: section 1, 2, 3, 4

Course Curriculum

Chapter 1: Introduction

Lecture 1: Overview

Lecture 2: Github ANN Course repository

Chapter 2: Neuron

Lecture 1: Biological Neuron

Lecture 2: Artificial Neuron

Lecture 3: Compute a logical function

Lecture 4: Linear Separability

Lecture 5: Compute another logical function

Lecture 6: Trick to remove the inside threshold

Lecture 7: Weights

Lecture 8: Decision boundary

Lecture 9: Perceptron learning

Chapter 3: Implementation

Lecture 1: Top down design

Lecture 2: Predict (forward)

Lecture 3: Train part 1

Lecture 4: Train part 2

Lecture 5: The XOR problem

Lecture 6: Add hyperbolic tangent activation function

Lecture 7: Refactor activation function

Lecture 8: Improve weight initialization

Lecture 9: Intuition XOR is hard

Lecture 10: Intuition ANN == universal approximator

Lecture 11: Approximate a strange function example

Chapter 4: Applications

Lecture 1: Iris Classifier

Lecture 2: Digits Classifier

Lecture 3: Save and Load functionality

Lecture 4: Save and Load FIX

Lecture 5: MNIST classifier

Chapter 5: Valuable Resources

Lecture 1: AIception and AIcrafters Resources

Instructors

Razvan Pistolea
Source Code Painter

Rating Distribution

1 stars: 5 votes

2 stars: 12 votes

3 stars: 15 votes

4 stars: 47 votes

5 stars: 65 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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