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!