Machine Learning, Deep Learning Neural Networks in Matlab

Machine Learning, Deep Learning & Neural Networks in Matlab, available at $44.99, has an average rating of 3.85, with 22 lectures, based on 142 reviews, and has 677 subscribers.

You will learn about How neural networks emulate the brain How to artificially represent neural networks The biological fundamentals behind neural networks and deep learning How to represent and manipulate neural networks with matrices The basics behind training neural networks and the cost function How to use gradient descent and learning intuition The maths and calculus behind forward and back propagation How to represent forward and back propagation in matrix form How to write forward and back propagation algorithms Understand and master the mathematics and algorithms behind deep learning and neural networks The structure of the MNIST database and how to use and extract data from it How to program and use the Sigmoid and Leaky Relu activation functions in MATLAB How to create a Neural Network in Matlab How to create Neural Network training and testing algorithms in Matlab How to use the MNIST database to make a neural network able to read handwritten numbers in images This course is ideal for individuals who are Anyone interested in Artificial Intelligence or Anyone interested in Machine Learning or Anyone interested in Deep Learning or Annyone interested in Neural Networks or Anyone who wants to learn MATLAB while applying it to Deep Learning and Neural Networks or Anyone interested in creating Artificial Intelligence able to recognize handwritten numbers or Anyone interested in using and understanding the MNIST database to train in Neural Networks or Anyone interested to expand his knowledge in Data Science within his current career or as a new career or Anyone interested in entering the Data Science industry as a developer or an entrepreneur or Anyone who want to create value in their projects or businesses bu deeply understanding and leveraging data science and deep learning It is particularly useful for Anyone interested in Artificial Intelligence or Anyone interested in Machine Learning or Anyone interested in Deep Learning or Annyone interested in Neural Networks or Anyone who wants to learn MATLAB while applying it to Deep Learning and Neural Networks or Anyone interested in creating Artificial Intelligence able to recognize handwritten numbers or Anyone interested in using and understanding the MNIST database to train in Neural Networks or Anyone interested to expand his knowledge in Data Science within his current career or as a new career or Anyone interested in entering the Data Science industry as a developer or an entrepreneur or Anyone who want to create value in their projects or businesses bu deeply understanding and leveraging data science and deep learning.

Enroll now: Machine Learning, Deep Learning & Neural Networks in Matlab

Summary

Title: Machine Learning, Deep Learning & Neural Networks in Matlab

Price: $44.99

Average Rating: 3.85

Number of Lectures: 22

Number of Published Lectures: 22

Number of Curriculum Items: 22

Number of Published Curriculum Objects: 22

Original Price: 44.99

Quality Status: approved

Status: Live

What You Will Learn

How neural networks emulate the brain

How to artificially represent neural networks

The biological fundamentals behind neural networks and deep learning

How to represent and manipulate neural networks with matrices

The basics behind training neural networks and the cost function

How to use gradient descent and learning intuition

The maths and calculus behind forward and back propagation

How to represent forward and back propagation in matrix form

How to write forward and back propagation algorithms

Understand and master the mathematics and algorithms behind deep learning and neural networks

The structure of the MNIST database and how to use and extract data from it

How to program and use the Sigmoid and Leaky Relu activation functions in MATLAB

How to create a Neural Network in Matlab

How to create Neural Network training and testing algorithms in Matlab

How to use the MNIST database to make a neural network able to read handwritten numbers in images

Who Should Attend

Anyone interested in Artificial Intelligence

Anyone interested in Machine Learning

Anyone interested in Deep Learning

Annyone interested in Neural Networks

Anyone who wants to learn MATLAB while applying it to Deep Learning and Neural Networks

Anyone interested in creating Artificial Intelligence able to recognize handwritten numbers

Anyone interested in using and understanding the MNIST database to train in Neural Networks

Anyone interested to expand his knowledge in Data Science within his current career or as a new career

Anyone interested in entering the Data Science industry as a developer or an entrepreneur

Anyone who want to create value in their projects or businesses bu deeply understanding and leveraging data science and deep learning

Target Audiences

Anyone interested in Artificial Intelligence

Anyone interested in Machine Learning

Anyone interested in Deep Learning

Annyone interested in Neural Networks

Anyone who wants to learn MATLAB while applying it to Deep Learning and Neural Networks

Anyone interested in creating Artificial Intelligence able to recognize handwritten numbers

Anyone interested in using and understanding the MNIST database to train in Neural Networks

Anyone interested to expand his knowledge in Data Science within his current career or as a new career

Anyone interested in entering the Data Science industry as a developer or an entrepreneur

Anyone who want to create value in their projects or businesses bu deeply understanding and leveraging data science and deep learning

AI is omnipresent in our modern world. It is in your phone, in your laptop, in your car, in your fridge and other devices you would not dare to think of. After thousands of years of evolution, humanity has managed to create machines that can conduct specific intelligent tasks when trained properly. How? Through a process called machine learning or deep learning, by mimicking the behaviour of biological neurons through electronics and computer science. Even more than it is our present, it is our future, the key to unlocking exponential technological development and leading our societies through wonderful advancements.

As amazing as it sounds, it is not off limits to you, to the contrary!

We are both engineers, currently designing and marketing advanced ultra light electric vehicles. Albert is a Mechanical engineer specializing in advanced robotics and Eliott is an Aerospace Engineer specializing in advanced space systems with past projects completed in partnership with the European Space Agency.

The aim of this course is to teach you how to fully, and intuitively understand neural networks, from their very fundamentals. We will start from their biological inspiration through their mathematics to go all the way to creating, training and testing your own neural network on the famous MNIST database.

It is important to note that this course aims at giving you a complete and rich understanding of neural networks and AI, in order to give you the tools to create your own neural networks, whatever the project or application. We do this by taking you through the theory to then apply it on a very hands-on MATLAB project, the goal being for you to beat our own neural network’s performance!

This course will give you the opportunity to understand, use and create:

How to emulate real brains with neural networks.

How to represent and annotate neural networks.

How to build and compute neural networks with matrices.

Understand and master the mathematics and algorithms behind deep learning and neural networks.

Train and test neural networks on any data set.

How to use the MNIST handwritting numbers training and testing datasets.

Import the MNIST data in MATLAB.

Create a complete neural network in MATLAB including forward and backwards propagation with both Leaky Relu and Sigmoid activation functions.

Train and test your own neural network on the MNIST database and beat our results (95% success rate).

We will thoroughly detail and walk you through each of these concepts and techniques and explain down to their fundamental principles, all concepts and subject-specific vocabulary. This course is the ideal beginner, intermediate or advanced learning platform for deep learning and neural networks, from their fundamentals to their practical, hands-on application. Whatever your background, whether you are a student, an engineer, a sci-fi addict, an amateur roboticist, a drone builder, a computer scientist, a business or sports person or anyone with an interest in data science and machine learning, at the end of this course, you will be capable of creating brains within machines!

If you have questions at any point of your progress along the course, do not hesitate to contact us, it will be our pleasure to answer you within 24 hours!

If this sounds like it might interest you, for your personal growth, career or academic endeavours, we strongly encourage you to join! You won’t regret it!

Course Curriculum

Chapter 1: The Theory: From its Biological Inspiration to Deep Learning

Lecture 1: Introduction to Deep Learning and Neural Networks

Lecture 2: Emulating the brain

Lecture 3: Artificial Neural Network Representation

Lecture 4: Matrix Representation of Neural Networks

Lecture 5: Nomenclature and Notations

Lecture 6: Training Basics and the Cost Function

Lecture 7: Gradient Descent and Learning Intuition

Lecture 8: Backpropagation Calculus and Mathematics

Lecture 9: Backpropagation Algorithm

Lecture 10: Backpropagation in Matrix Form

Lecture 11: Algorithm Implementation

Lecture 12: Putting it All Together

Chapter 2: MATLAB: create, train and test your neural network with the MNIST database

Lecture 1: The MNIST Handwritten Numbers Database

Lecture 2: Extracting the Images from the Database into MATLAB

Lecture 3: Extracting the Relevant Image Labels from the MNIST Database into MATLAB

Lecture 4: The Sigmoid and Leaky Relu Activation Functions

Lecture 5: Initializing the Neural Network

Lecture 6: Neural Network Forward Propagation

Lecture 7: Neural Network Backwards Propagation

Lecture 8: Neural Network Training

Lecture 9: Creating a Testing Forward Propagation Algorithm

Lecture 10: Training and Testing our Neural Network using the MNIST database

Instructors

Eliott Wertheimer
Aerospace Engineer and Founder @FuroSystems

Albert Nassar
Mechanical Engineer and Founder @FuroSystems

Rating Distribution

1 stars: 4 votes

2 stars: 7 votes

3 stars: 26 votes

4 stars: 54 votes

5 stars: 51 votes

Frequently Asked Questions

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

Can I take my courses with me wherever I go?

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