- be proficient in the use of PyTorch and Lightning for accelerated AI deployment.
- be able to unpack the insides of a deep neural network.
- understand architecture design and the building blocks of a neural network.
- have deployed training algorithms to tune models to new datasets.
In this course we shall be taking some first steps in applying and understanding deep neural networks. We'll be asking:
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What is deep learning and neural networks?
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How can we use them, train them, build them?
And most importantly we'll give examples of deployment with industry standard python packages: 'PyTorch' and 'Lightning'.
But to warm us up...
A revolution in amongst us. Deep learning holds the keys to unlocking the reams of unstructured data which is constantly collected in the modern world. With applications in the fields of computer vision--object detection, classification--to natural language processing--knowledge from text and large language models--we have only recently started building the correct tools to approach and solve big problems in industry.
With vast reservoirs of data, the past champions in machine learning and statistics struggle to draw inference without manual engineering of the data. In deep learning, we develop models that can learn subtle patterns in vast datasets.
And the applications are numerous:
- Reinforcement Learning
- Computer Vision
- Natural Language Processing (inc. Large Language Models)
to name but a few. To begin developing in these fields, you need to know the fundamentals. Bother theoretical and practical. And that's what we'll cover in this course.
In this course, you will get to grips with:
- Neural network architecture and 'learning' algorithms.
- Training and validation approaches: loss functions, hyperparameters, tips & tricks.
- Using the frameworks built into both PyTorch and Lightning for smooth training and development.
- Applying end-to-end models in applications in the wild. We have one example looking as optimisation in drug discovery and another in image classification.
This course a gentle introduction to the topic. It sits alongside The Kernel Trick and Tree-based algorithms. And you could warm up with Tim's intro course, Getting started in machine learning.
Once you've made it to the other side, Freddy has created a brilliant end-to-end MLOps course where you shall train a deep neural network and deploy it with React; it's called Using TensorFlow to Build a Production-Ready Neural Network-Powered Web App.
