The Visual Guide on How Neural Networks Learn from Data
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The Visual Guide on How Neural Networks Learn from Data

The BEST Resource for Understanding Neural Networks and How They Learn
4.5 (12 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
78 students enrolled
Created by Mauricio Maroto
Last updated 8/2017
English
Curiosity Sale
Current price: $10 Original price: $195 Discount: 95% off
30-Day Money-Back Guarantee
Includes:
  • 2.5 hours on-demand video
  • 18 Articles
  • 41 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Understand what Neural Networks (NNs) are all about
  • Step-by-Step in-Motion NN files for you to KEEP
  • Adjust Templates to your requirements => SEE what's going on!
  • See how Neural Networks LEARN (graphic and dynamic files)
  • Understand KEY concepts in NN's (Gradient Descent, Backpropagation and more)
  • Know Types of Neural Networks, Designs and Advanced Topics
View Curriculum
Requirements
  • 1. Excel and PowerPoint (Office 2010+)
  • 2. Maths (Basics)
  • 3. A Plus: Calculus and Derivatives (Optional, not required)
Description

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NEW! Trophy Awards for Key Section Achievements!

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Some Student Reviews are:

"This is the best example and explanation I could find about the internal working of NN (...)" (August 2017)

"This is a unique way of explaining and illustrating the operation of a simple ANN." (July 2017)

"Excellent course! It teaches you the basic of Neural Network in an easy to understand way (...)"  (June 2017)

"Great starting point to learn ANNs!" (June 2017)

"v[ery] good explaination" (May 2017)

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Hi. Thanks for showing interest in this course!

What makes this course special: 

  • Step-by-Step Neural Network Learning Process,
  • Master topics like Fundamentals, Objectives, Required Datasets, Weights, Biases, Nodes, Activation functions, Feed-Forward Passes, Predictions, Losses, Gradient Descent, Learning, Backpropagation and more!
  • Plus, personalized feedback and help. You ask, I answer directly!


This is your BEST resource for Neural Networks (NN) learning! A must for understanding special concepts and not get lost in computing your own NNs:


✅ First:

You'll start the Neural Networks Primer with Fundamentals, Objectives, Data and more:

  • Learn concepts using analogies for maximum learning, so you will be fully covered. 
  • Learning how NNs learn will be easy with this Primer under your sleeve!


✅ Second

You'll continue the NN Primer with Learning, Backpropagation and Predictions and more topics

  • In an easy and intuitive way, you will understand how they work, 
  • This is fundamental in the NN Learning Process. 
  • At the end of this section, you will have mastered the NN Primer!
  • Now, you are ready for the Step-by-Step (in-Motion) sections!


✅ Third:

You'll start the in-Motion section with Inputs, Weights, Biases, Activations, Nodes and Feed-Forward Passes:

  • See how they work inside an NN,
  • Step-by-step templates, so you can follow every detail, 
  • These files will be dynamic, so you'll understand how NNs work as numbers will be updated on-the-fly and right in front of your eyes.


✅ Forth:

You'll continue with the in-Motion section with NN Learning, Backpropagation, Tuning and Prediction:

  • You will understand how NNs learn from the data. 
  • This all part of the dynamic templates you get to keep. 
  • You'll do several examples along the way for maximum learning. 
  • Lastly, you'll see what NNs do to make the best predictions.


✅ Fifth:

You'll finish the in-Motion section by doing a complete rundown on everyting you've learned so far:

  • You'll see how all NN inner components work for learning and prediction. 
  • Pay close attention at how all parts adjust, making the NN learn in front of your eyes. 
  • After this section, you will be fully versed on how NNs learn!


✅ Sixth:

I will devote a section for more additional knowledge and resources for continous learning. And then, I will conclude with some Final Words.


What are the Requirements?

  • The only thing you'll need for this course is: Excel and PowerPoint: It is that easy! 
  • You will also need to bring your Basic Maths too,
  • If you bring your Calculus (Derivatives) knowledge, that will be a big plus for you (but not required),


What are some of the Benefits?

  • As it is usual in my courses, you will get all files and spreadsheets for all lectures. 
  • This way you can replicate everything I do immediately after each lecture.
  • Neural Networks are the new thing today. 
  • With it, you can explore and engage Artificial Intelligence, which I recommend you to dive in as it's part of the future. 
  • Plus, it's very rewarding and fun too!
  • New content coming in the near future, let me know yout thoughts.


Lastly, you can post questions or doubts, and I’ll answer to you personally. 

I hope you find this course as useful as I have creating it! 


I’ll see you inside,


-M.A. Mauricio M.

Who is the target audience?
  • Once and For All => Learn and Understand Step-by-Step How Neural Networks work with this course
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Curriculum For This Course
55 Lectures
02:46:53
+
Introduction
3 Lectures 01:55
A foreword and Initial Tips
01:18

How to go through this course
00:33

BONUS COUPON: For any course!
00:03
+
Neural Networks PRIMER: Fundamentals, Objective and Data and more
6 Lectures 32:49


Our first graphic NEURAL NETWORK
05:07

What is the objective of a NEURAL NETWORK?
04:56

What type of data is good for NEURAL NETWORKS?
03:49

What are Training, Validation and Test datasets?
05:24

Quiz
6 questions
+
Neural Networks PRIMER: Learning, Overfitting and Prediction and more
7 Lectures 25:44
What's a Feed-Forward Pass (FFP)?
07:42

What are Epochs?
02:54

How good are NEURAL NETWORKS at Prediction?
04:05

Quick Exercise 1
00:02

How do NEURAL NETWORKS Learn?
05:38

What types of NEURAL NETWORKS are out there?
05:18

Quiz
4 questions

1st Trophy Achieved
00:05
+
Neural Networks IN-MOTION: Inputs, Weights, Biases, Activations and Predictions
12 Lectures 30:22

How do Inputs (x) look like?
02:38

How do Nodes (n) look like?
03:18

How do Weights (w) look like? And how to initialize them
05:17

A Quick Note 1
00:09

How do Biases (b) look like?
01:38

How do Activation functions (f) look like?
03:28

How do Activation functions Derivative (f') look like?
04:11

FFP: from Inputs to Nodes 1 and 2
05:01

FFP: from Nodes 1,2 to Node 3 (Prediction output)
03:28

Quick Exercise 2
00:04

Quiz
6 questions

2nd Trophy Achieved
00:04
+
Neural Networks IN-MOTION: Losses, Backpropagation, Learning and Tuning
16 Lectures 55:26

What's the Loss associated?
06:32

Quick Exercise 3
00:05

Set an initial Learning Rate
02:20

Gradient Descent and Backpropagation
06:04

Deriving Formulas for Node 3 (Optional)
07:19

Backpropagation: Computing Change at Node 3
03:20

Deriving Formulas for Node 1 (Optional)
05:48

Backpropagation: Computing Change at Node 1
04:24

Deriving Formulas for Node 2 (Optional)
01:54

Backpropagation: Computing Change at Node 2
02:21

3rd Trophy Achieved
00:07

Let the Neural Network Learn
06:12

Compare new Results by Learning
02:32

Let the Neural Network Learn more and Compare
05:03

4th Trophy Achieved
00:08

Quiz
5 questions
+
Neural Networks IN-MOTION: Complete Rundown of NN Learning
9 Lectures 20:03
The Prediction (Y^) and Loss (L)
06:44

Quick Exercise 4
00:10

The Weights (w)
04:28

Quick Exercise 5
00:14

The Biases (b)
03:02

Quick Exercise 6
00:13

Quick Exercise 7
00:06

Complete NN Rundown
04:57

5th Trophy Achieved
00:07
+
Neural Networks: Further Knowledge
1 Lecture 00:08
Further Knowledge is here
00:08
+
Final Words
1 Lecture 00:30
Final Words
00:30
About the Instructor
Mauricio Maroto
4.3 Average rating
150 Reviews
2,419 Students
4 Courses
Economist Data Scientist +2,000 students and growing!

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Mauricio Maroto holds a Master in Industrial Economics from Carlos III University in Madrid, Spain. 

He has with extensive experience in Data Analysis and Visualization. He's proficient at Python, STATA and Microsoft Excel and he likes Machine Learning, Prediction, Optimization Methods and similar quantitative topics. He gained all experience through work, private consultancies and personal projects.

He blogs about Machine Learning, Energy, Technology and related topics at medium website.

He believes "Code is a fundamental skill" and "innovation brings progress to everyone". He loves teaching, passing knowledge and just making life easier. 

Mauricio's other passions are his family, friends, his pets and he loves to play soccer, whether indoors or outdoors. He also goes out for a run on weekends (not lately). He also loves topics such as Entrepreneurship, Cryptocurrencies and Science.