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LENS Machine Learning School 2021

A host for the tutorial material for the machine learning school 2021. This school took place in the week commencing 15 Feburary 2021. Lecture recordings, where taken, are available at

This School is a collaboration of working group 4 as part of the League of Advanced European Neutrons Sources (LENS) but was also supported by the STFC's Scientific Machine Learning Group (SciML) group and the Jülich Supercomputing Centre (JSC).

o To use this material, if you are unfamiliar with git we reccommend donloading the entire repository (the green code button and download .zip file)
o To run the tutorials on colab ( you will need a google acount, then select github when prompted for a notebook and insert this repository ( which should then find all of the notebooks
o Alot of the notebooks have "lecturer editions" with answers, or answers hidden at the bottom of the page if you get stuck
o A slack workspace has also been created for this school ( pleae join the conversation

Lecture 1: Introduction to deep learning and neural networks (Jos Cooper)

o Terminology

o The perceptron

o Fundamentals of deep learning: neural networks, nodes, weights, biases, activation functions, backpropogation and some of the maths behind it

o Introduction to Tensorflow, Pytorch, and Keras

Lecture 2: Dense neural networks and regression (Jos Cooper)

o Supervised learning

o Epochs, metrics, batch processing

o Training, validation, testing, prediction

Lecture 3: Convolutional neural networks and classification (Emmanouela Rantsiou)

o Filters, convolution, layers

o Connections, activations, down sampling

o Training, classification, metrics

o Pre-processing

o Augmentation, regularization

o Hyper-parameter tuning

o Transfer learning

Lecture 4: Traditional ML methods (Andrew McCluskey)

o Decision trees

o Gradient boosting

o Principle component analysis (PCA)

o Bayesian model selection

Lecture 5: Image segmentation (Anders Kaestner)

o Object detection

o Tomography

o SegNet and/or ResNet

o Semi-supervised learning

Lecture 6: Recurrent neural networks (Gagik Vardanyan)

o Time series

o Simple RNNs


o GRUs

Lecture 7:Generative Adversarial Networks, GANs (Kuangdai Leng)

o Introduction to generative models: VAEs and GANs

o GANs: basics and practice

Lecture 8: Natural language processing and speech recognition (Gagik Vardanyan & Guanghan Song)

o Semantic space, word-to-vec

o NNTK, spacey

o Machine translation, seq-to-seq methods

Lecture 9: Uncertainty and attention (Mario Teixeira Parente)

o Bayesian methods

o Gaussian attention / spatial transformers

Lecture 10: Unsupervised learning - clustering (Marina Ganeva)

Part 1

o Introduction

o Clustering

o Manifold learning

Part 2

o Reinforcement learning


A host for the tutorial material for the machine learning school 2021







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