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Welcome

Welcome to the git repository for HDRUK Summer School 2019 Course on Applied Deep Learning!

Here you will find course slides, a reading list, and supporting code.

Getting Started with Code

  • Clone this repo to your machine

  • Install conda (https://conda.io/en/latest/), then run the following command from the root directory of the repository:

conda create -n hdruk_aml python=3.6

  • Now activate your environment:

conda activate hdruk_aml

  • Install the necessary packages:

pip install -r requirements.txt

  • Note that the requirements file may expand from week to week so if you find yourself lacking packages to run a particular script, just re-run the prior command.

For an introduction to Keras, please refer to https://keras.io/getting-started/sequential-model-guide/.

Reading List

The Course is broken in seven sections:

Morning Session:

  1. Introduction: Multi-Layer Perceptrons
  2. Training Networks: Implementations with Keras, Optimisation & Regularisation methods
  3. Convolutional Neural Networks
  4. Embeddings and Recurrent NNs

Afternoon Session:

  1. Deep Generative Models: VAEs & GANs, and their implementation with Tensorflow
  2. Deep Reinforcement Learning
  3. Frontiers in Deep Learning

and for each section we give a here a few papers that either are fundamental in the developement of that strand of DL, or are recent, cutting edge work.

Reading these before the course is not required, but after the course you may find value in flicking through any for the areas about which you would like to know more.

Introduction to Machine Learning

Three papers, first two quite technical that can be skimmed just to get the headline ideas, and then one more approachable paper that is the foundation for deep neural networks. We really just want you to get the take-aways for the first two, and if the last doesn't make much sense, hopefully it will after the morning session.

  1. Approximation by Superposition of a Sigmoid Function, Cybenko (1989) http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.441.7873&rep=rep1&type=pdf

    This shows the remarkable result that a superposition of sigmoidal activation functions can arbitrarily closely match any continuous function, as long as you have infinite units and unbounded weights. This is the result behind people talking about a neural net with one infinitely wide hidden layer being a universal function approximator.

  2. The Lack of A Priori Distinctions Between Learning Algorithms, Wolpert (1996) http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.390.9412&rep=rep1&type=pdf

    Again please only skim this for the headline result, which is probably familiar to those of you with a background in statistics. The result is that, formally speaking, no supervised learning algorithm can be better than any other in the totally general case of all possible supervised learning problems. Of course the world has regularities that we exploit, but nevertheless in general we cannot expect to find one particular machine learning algorithms that will dominate in all tasks.

  3. Deep Sparse Rectifier Neural Networks, Glorot, Bordes and Bengio (2011) http://proceedings.mlr.press/v15/glorot11a/glorot11a.pdf

    Much more recent that the other two. This is the paper that gives us the tool to build very large networks, the RELU activation function. Most of the methods for training models referred to in this paper are now hideously out of date - the field has moved on a lot since 2011. This activation function means that gradient signal can go further through the model.

Training Neural Networks

Three papers, more approachable and relevant to current research than those listen above. We think you all would benefit from skimming through the entirety of each paper.

  1. Understanding Deep Learning Requires Re-Thinking Generalization, Zhang et al. (2017)

    https://arxiv.org/pdf/1611.03530.pdf

    Large enough networks, where the number of parameters exceeds the number of training data-points, can effectively memorize entire "random" datasets. The core concept being described is that of overfitting, where a network fits to random noise present in the training data increasing it's "generalization" error when evaluated on a test dataset.

  2. Visualizing the Loss Landscape of Neural Nets, Li et al. (2017)

    http://papers.nips.cc/paper/7875-visualizing-the-loss-landscape-of-neural-nets.pdf

    This paper clearly illustrates how different neural network architectures can generate smoother loss 'landscapes' to produce models that generalize better and are less likely to be stuck in local minima.

  3. Adam: A Method for Stochastic Optimization, Kingma, Lei Ba (2015)

    https://arxiv.org/pdf/1412.6980.pdf

    Introduces the Adam Optimizer a good out-of-the-box optimizer which requires very little tuning of hyper-parameters. It is also computationally efficient, requiring only first-order gradients and little memory (RAM) requirements. Even though the paper was published 4 years ago, it remains the most popular optimizer.

Convolutional Neural Networks

Paper 1 is foundational in the history of DL - this paper reignited DL research. Paper 2 is a more recent paper showing real-world uses of modern computer vision. Paper 3 gives an early and still very useful method for trying to interpret what is going on inside a conv net.

  1. ImageNet Classification with Deep Convolutional Neural Networks; Krizhevsk, Sutskever and Hinton; 2012

    https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf

    This is another foundational paper in the recent renaissance of neural networks. The convolutional neural network proposed here, AlexNet, demonstrated a step-change in image classification performance compared to previous approaches.

  2. Dermatologist-level classification of skin cancer with deep neural networks; Esteva et al; 2017

    https://www.nature.com/articles/nature21056

    A recent application paper, showing that convolutional neural networks can reach human-level performance at classifying skin cancers

  3. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps; Simonyan, Vedaldi and Zisserman; 2014

    https://arxiv.org/abs/1312.6034

    How can we understand how neural networks come to their decision? This paper propagates gradients into the input in various ways to try to get a handle on what parts of an image are most important to the prediction.

Recurrent Neural Networks

  1. A Critical Review of Recurrent Neural Networks for Sequence Learning; Lipton et al.; 2015 https://arxiv.org/abs/1506.00019

    A brief and approachable review of recurrent neural networks. Sections 3 and 4 are probably the most important.

  2. Sequence to Sequence Learning with Neural Networks; Sutskever, Vinyals and Le.; 2014 https://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf

    An early and highly influencial paper using recurrent neural networks for machine translation

  3. Neural Machine Translation by Jointly Learning to Align and Translate; Bahdanau, Cho and Bengio; 2015 https://arxiv.org/pdf/1409.0473.pdf

    In this paper we relax the assumption that the word orders between languages match closely, and instead learn to align.

Variational Autoencoders and Generative Adversarial Networks

  1. Auto-Encoding Variational Bayes; Kingma and Welling; 2013

    https://arxiv.org/pdf/1312.6114

    One of the two contemporaneous papers proposing VAEs (the other being Rezende et al. 2014). Shows how to scale up variational inference to large models and large datasets.

  2. BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling; Maaloe et al. 2019

    https://arxiv.org/pdf/1902.02102.pdf

    The current state of the art for image generation for VAE-like models. Quite Byzantine in structure, but the results are impressive - normally VAE draws/reconstructions are quite blurry but this model's are sharp.

  3. Generative Adversarial Nets; Goodfellow et al.; 2014

    https://arxiv.org/abs/1406.2661

    The foundational paper proposing GANs. Probably the most important new model structure in deep learning. Despite ongoing challenges in training them reliably, GAN-like models are state of the art for numerous data-generation tasks.

  4. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks; Zhu et al.; 2018

    https://arxiv.org/pdf/1703.10593

    Introduces 'CycleGan', which gives impressive 'style transfer' performance. Key insight is that we want the composition of both 'translation' networks to give us back our input.

Deep Reinforcement Learning

  1. Human-level control through deep reinforcement learning; Mnih et al; 2015

https://www.nature.com/articles/nature14236

This paper sets the foundation for much of deep reinforcement learning by introducing Deep Q Networks (DQN). Here, neural networks are used to map state action pairs to rewards, thereby estimating the ‘Q’ function of classical reinforcement learning.

  1. Rainbow: Combining Improvements in Deep Reinforcement Learning; Hessel et al; 2017

https://arxiv.org/pdf/1710.02298.pdf

This paper fruitfully combines many of the recent modifications to the original DQN to achieve state of the art performance.

Frontiers in Deep Learning

  1. Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics; Kendall et al; 2018

https://arxiv.org/pdf/1705.07115.pdf

The authors grapple with the complicated task of training neural networks to minimise multiple regression and classification objectives. They do so by weighting multiple loss functions by considering the homoscedastic uncertainty of each task.