This page documents the design process and motivation of this lesson material.
Lesson Title: An Introduction to Deep Learning
Target audience
The main audience of this carpentry lesson is PhD students that have little to no experience with deep learning. In addition, we expect them to know basics of statistics and machine learning.
Learner Profiles
Ann from Meteorology
Ann has collected 2-3 GB of structured image data from several autonomous microscope on baloon expeditions into the atmostphere within her PhD programme. Each image has a time stamp to it which can be related to the height of the baloon at this point and the current weather conditions. The images are unstructured and she would like to detect from the images if the baloon traversed a cloud or not. She has tried to do that with standard image processing methods, but the image artifacts to descriminate are somewhat diverse. Ann has used machine learning on tabular data before and would like to use Deep Learning for the images at hand. She saw collaborators in another lab do that and would like to pick up this skill.
Barbara from Material Science
Barbara just started her PostDoc in Material Science. Her new group has a large amount of scanning electron miscroscope images stored which exhibit several metals when exposed to a plasma. The team also made the effort to highlight solid deposits in these images and thus obtained 20,000 images with such annotations. Barbara performed some image analysis before and hence has the feeling that Deep Learning may help her in this task. She saw her labmates use ML algorithms for this and is motivated to finally understand these approaches.
Dan from Life Sciences
Dan produced a large population of bacteria that were subject to genetic alterations resulting in 10 different phenotypes. The latter can be identified by different colors, shapes and movement speed under a fluorescence microscope. Dan has not a lot of experience with image processing techniques to segment these different objects, but used GUI based tools like fiji and others. He has recorded 50-60 movies of 30 minutes each. 10 of these movies have been produced with one type of phenotype only. Dan doesn’t consider himself a strong coder, but would need to identify bacteria of the phenotypes in the dataset. He is interested to learn if Deep Learning can help.
Eric from Pediatrics Science
Eric ran a large array of clinical trials in his hospital to improve children pharmaceutics for treating a common (non-lethal) virus. He obtained a table that lists the progression of the treatment for each patient, the dose of the drug given, whether the patient was in the placebo group or not, etc. As the table has more than 100 000 rows, Eric is certain that he can use ML to cluster the rows in one column where the data taking was inconsistent. Eric has touched coding here and there where necessary, but never saw it necessary to learn coding. His cheatsheet is his core wisdom with code. So his supervisor invited him to take a course on ML as “this is the tech of these days!” as his boss said.
Notes
- Probably have overhyped expectations of deep learning.
- They don’t know if it’s the right tool for their situations.
- They have no idea what it takes to actually do deep learning.
- Want to quickly have some useful skills for their own data.
Required Pre-Knowledge
- Python – Previous programming experience in Python is required (Refer to Python Data Carpentry Lesson)
- Pandas – Knowledge of the Pandas Python package
- Basic Machine Learning Knowledge – Data cleaning, train & test split, overfitting & underfitting, metrics (accuracy, recall, etc.),
Learning objectives
Overview
After following this lesson, learners will be able to:
- Prepare input data for use for deep learning
- Design and train a Deep Neural Network
- Troubleshoot the learning process
- Measure the performance of the network
- Visualizing data and results
- Re-use existing network architectures with and without pre-trained weights
The following offers more details to each learning objective based on Bloom’s Taxonomy. For hints on how to use this approach, see lesson 15 of the instructor training
Prepare input data for use for deep learning
This includes cleaning data, filling missing values, normalizing, and transforming categorical columns into dummy encoding.
After this module, learners can …
- define a checklist for data analysis steps before applying Deep Learning to the data
- describe criteria by which to judge good or bad data, e.g. how a column’s values should be distributed
- execute a min-max normalization on floating point data
- sketch how to insert missing timestamps or literal values (i.e. factors or non-numeric entries)
- implement a transformation of categorical values into a numerical encoding (
int8) - argue for or against strategies to normalize data
- formulate techniques to prepare (clean) data for training a deep learning network
Design and train a Deep Neural Network
This includes knowledge of when to different types of layers
After this module, learners can …
- list/repeat the three ingredients to a feed forward network: input, hidden layers, output
- classify/categorize parts of a feed forward network when presented a network architecture (as from
keras.model.summary()) - describe a fully connected (dense) layer
- describe a convolutional layer
- describe a max pooling layer
- describe an activation function
- describe a softmax layer
- argue against abundant use of the sigmoid function (exploding/vanishing gradients)
- calculate the output data shape of an image when transformed by a fixed convolutional layer
- interpret errors with convolutional layers
- execute a 3 layer network on the MNIST data (or similar)
- differentiate a dense layer and a convolutional layer
- experiment with values of dense layer and a convolutional layer
- select a layer type depending on the input data
- develop a 5 layer network that comprises both layer types
Monitoring and Troubleshooting the learning process
Often when designing neural networks training will not automatically work very well. This requires setting the parameters of the training algorithm correctly, modifying the design of the network or changing the data pre-processing. After training, the performance of the network should be checked to prevent overfitting.
After this module, learners can …
- define precision and recall/accuracy for a classification task
- state that cross-validation is used in Deep Learning too
- describe how to split a dataset into training/test/validation set
- describe how Drop-Out Layers work
- execute a plot to draw the loss per epoch for training and test set
- compare values of precision and recall
- differentiate a overfitting network from a well-behaved network
- detect when a network is underfitting or overfitting
- design countermeasures for overfitting (e.g. more dropout layers, reduce model size)
- design countermeasures for underfitting (e.g. larger model)
- critique a provided network design
Visualizing Data and Results
Within each episode how to visualize data and results
After this module, learners can …
- identify important plots to create at the end of training (provide selected samples and their prediction)
- execute plotting of important variables during training (loss, ROC)
- use tensorboard and related callbacks during training
- examine the results of a partners network
- critique the results of a partners network
Re-use existing network architectures with and without pre-trained weights
Re-use of architectures is common in deep learning. Especially when using pre-trained weights (transfer-learning) it can also be very powerful.
After this module, learners can …
- describe what transfer learning stands for
- explain in what situations transfer learning is beneficial
- solve common issues of transfer learning (such as different resolutions of the original training and the training at hand)
- test training under different data shape mitigation strategies
- relate training time of a de-novo networt and a pretrained one
- relate prediction quality of a de-novo networt and a pretrained one