Overview
Python (along with R) has become the dominant language in machine learning and data science. It is now commonly used to fit complex models to messy datasets. PyTorch is an open-source machine learning library for Python, based on Torch, used for applications such as natural language processing. It is primarily developed by Facebook's artificial-intelligence research group, and Uber's Pyro software for probabilistic programming is built on it.
This workshop is delivered by our training partner Jumping Rivers
Outline
Introduction to machine learning
A brief introduction to the topic of machine learning and the machine learning landscape in Python.
Preprocessing
Building Preprocessing pipelines for data.
Introduction to deep learning and PyTorch
An introduction to the PyTorch framework for training deep learning models.
Building a neural network
Defining a class blueprint for a neural network model in PyTorch.
Training
Understanding feedforward networks, the backpropagation algorithm and optimisation.
Supervised learning
Building and training models for both regression and classification tasks. Consideration and exploration of different layers and activation functions within a neural network architecture.
Convolutional neural networks
Learning with image data.
Transfer learning
Leverage the power of existing neural network architectures and weights. This is a powerful technique for training finely tuned models for accomplishing tasks with your own data.
Deep learning landscape
An exploration of other types of deep learning, the tasks that they aim to solve and how we might implement these in PyTorch. For example unsupervised, reinforcement learning.
Requirements
The course assumes familiarity with the python programming language including experience of OOP, writing ones own classes in python. Some knowledge of calculus, matrix algebra and probability would be helpful but not essential.
Course Structure
The course will be a mixture of lecture style session together with practical exercises. Practical exercises will give delegates the opportunity to build their own models on real data sets for tasks such as hand writing recognition and image classification.
