1-day Deep Learning with R workshop at RStudio::conf 2019
- Data scientists proficient in R and machine learning.
- Introduce data scientists to the fundamentals of deep learning
- How to implement ANN, CNN and RNN architectures in R using the
keraspackage - How to identify and avoid common pitfalls.
- Hands-on, application of deep learning to solve problems in supervised machine learning (regression and classification using multivariate data, images and text).
The slides shown during the presentation can be found here.
The data sets can be found online here:
I've also made them available on my server in the same format used in the exercises (train, validation and test folders):
- What is a tensor and why use it?
- What is keras and what is its relationship to TensorFlow?
- What is the deep in deep learning? ANNs and densely-connected networks.
- The math of deep learning: Basics of matrix algebra, gradient descent, backpropagarion, chain rule.
- The four stages of Deep learning.
- Parameters and hyper-parameters.
- Functions distinguishing classification and regression: loss and optimizer functions.
- The Boston Housing Price dataset for regression, single-label, multi-class classification and binary classificaiton.
- The UCI Abalone data-set, predict ring number as a categorical or continuous variable.
| Markdown File | Description |
|---|---|
| 0_1_Classic_ML.Rmd | Some context from classical Machine Learning |
| 1_0_Boston_reg.R | Deep Learning for Regression, plain R script |
| 1_1_DL_Basics_Regression.Rmd | Deep Learning for Regression |
| 1_2_DL_Basics_Binary_Classification.Rmd | Deep Learning for Binary Classification |
| 1_3_DL_Basics_Multi-class_Classification.Rmd | Deep Learning for Single-label, Multi-class Classification |
- The training, validation and test sets.
- Four ways of dealing with over-fitting: more data, capacity, dropout, regularization.
- The universal workflow of machine learning.
- Introduction to
tfrunspackage for evaluating and comparing runs
- The UCI Abalone data-set, predict ring number as a categorical or continuous variable.
| Markdown File | Description |
|---|---|
| 2_1_Eval-Optim_validation.Rmd | Appling validation |
| 2_2_Eval-Optim_Overfitting.Rmd | Avoiding over-fitting |
| 2_3_Eval-Optim_Capacity.Rmd | Changing capacity |
| 2_4_Eval-Optim_tfruns.Rmd | Using the tfruns package |
- Requirements of computer vision not met with ANNs.
- New layers: convolution, maximum pooling.
- Revisiting over-fitting.
- CNNs as an extension of densely-connected networks.
- Accessing individual layers of trained models.
- Using pre-trained models to increase accuracy.
- Ethics of machine learning: predicting beyond the bounds of the training set.
- Dog versus cat images - binary classification.
- Malaria histology images -- binary classification.
- The Labradoodle versus fried chicken image dataset.
| Markdown File | Description |
|---|---|
| 3_1_Computer_Vision_Intro.Rmd | Working with images |
| 3_2_Computer_Vision_Augmentation.Rmd | Using Image Augmentation to reduce over-fitting |
| 3_3_Computer_Vision_Optimization.Rmd | Using pre-trained Convnets |
| 3_4_Computer_Vision_Fine-tuning.Rmd | Optimizing pre-rained Convnets |
| 3_5_Computer_Vision_Visualising.Rmd | Visualizing layers |
- Formatting text for neural networks.
- One-hot encoding vs embedding.
- RNNs and LSTM compared to ANNs.
- Reuters Newswire dataset -- single label, multi-class classification with text.
- The IMDB movie sentiment dataset -- binary classification
| Markdown File | Description |
|---|---|
| 4_1_Text_Analysis_One-Hot.Rmd | Basic text analysis using one-hot encoding. |
| 4_2_Text_Analysis_Word-Embeddings.Rmd | Trainig word embeddings. |
| 4_3_Text_Analysis_pre-trained_embeddings.Rmd | Using pre-trained word embeddings. |
| 4_4_Text_Analysis_Simple-RNNs.Rmd | Understanding RNNs. |
| 4_5_Text_Analysis_RNN-on-Reuters.Rmd | Applying RNNs. |
| 4_6_Text_Analysis_LSTMs.Rmd | Applying LSTMs. |