Artificial intelligence is the future. Most of us have a good idea about what it can do. But do we understand what AI really is?
Welcome to our first course on deep learning. We are going to try to answer to fundamental questions:
'What is deep learning?'
and 'What is deep learning?'
Hold your breath: it's maths! And some quite simple mathematics at that. But that's not something we need to worry about. There are brilliant tools out there with which we can assemble AI models with there component parts, like machines. And why are we interested?
Vastly over-parameterised neural networks can solve complex problems like no other approaches.
But we aren't starting at the "most complex". In this course we'll start at the beginning. our key goal is to learn to code neural networks and use them in the wild. We shall start with some recent motivation; and we'll uncover all the concepts and lingo so that you can hold with a deep learning engineer.
Over the next few video lessons we shall cover the following topics:
- ✅ An introduction to deep learning and applications.
- ✅ A deep dive into the prototype neural network: a multi-layered perceptron.
- ✅ Deep learning fundamentals:
- Forward-passing and architecture
- Training routines and backpropagation
- Loss functions, hyperparameters and regularisation
- ✅ Hands-on deployment:
- Building models with PyTorch
- Accelerating deployment with PyTorch Lightning
- ✅ Neural Nets in the wild: - Building surrogates for drug discovery and parameter-space search. - Building classifiers and interrogating hidden layers.
In this video...
We shall take a wide-angled look at deep learning applications across the board. From computer vision; language processing; and reinforcement learning. This is our motivation All of these fields of application rely fundamentally on the basic tools that we shall introduce in this course.