You can find my article describing the individual levels in detail at Towards Data Science. The list is also on GitHub.
Entry level
Data handling
- [ ] Small datasets
- [ ] Simple preprocessing
- [ ] Image data
- [ ] Audio data
- [ ] Time-series data
- [ ] Text data
Machine learning
- [ ] Regression
- [ ] Clustering
- [ ] SVMs
Networks
- [ ] Dense Neural Networks
- [ ] Convolutional Neural Networks
- [ ] Recurrent Neural Networks
Theory
- [ ] Mathematical notation
- [ ] Matrix operations
- [ ] Regression
- [ ] Clustering
- [ ] Convolution
- [ ] Simple metrics
General
- [ ] Learning the toolset
- [ ] Knowing the docs
- [ ] Data analysis
- [ ] Supervised data
- [ ] Working with metadata files
- [ ] Saving and loading models
- [ ] Callbacks
Intermediate level
Data handling
- [ ] Large datasets
- [ ] Imbalanced datasets
- [ ] Complex datasets
- [ ] Augmentations
- [ ] Normalization
- [ ] Generators
- [ ] Custom pipelines
Custom projects
- [ ] Custom image project
- [ ] Custom audio project
- [ ] Custom time-series project
- [ ] Custom text project
Networks
- [ ] Large networks
- [ ] Advanced layers
- [ ] Custom layers
- [ ] Generative networks
- [ ] Language models
Training
- [ ] Transfer learning
- [ ] Fine-tuning
- [ ] Custom embeddings
- [ ] Custom callbacks
- [ ] Data-parallel training
- [ ] Multi-GPU training
- [ ] Custom training loops
- [ ] Training in the cloud
- [ ] TPU training
Theory
- [ ] Backpropagation
- [ ] Activation functions
- [ ] Optimizers
- [ ] Losses
- [ ] Advanced metrics
- [ ] Probabilities
- [ ] Advanced layers
- [ ] Regularization
- [ ] Knowing common problems
General
- [ ] Unsupervised data
- [ ] Creating splits
- [ ] Code versioning
- [ ] Experiment tracking
- [ ] Hyperparameter search
- [ ] Model deployment
- [ ] Problem thinking
- [ ] Contributing to projects
- [ ] Reading papers
Advanced level
Data handling
- [ ] Huge datasets
- [ ] Distributed pipelines
- [ ] Multi-modal datasets
Custom projects
- [ ] Custom generative project
Training
- [ ] Mixed-precision training
- [ ] Multi-worker training
- [ ] Multi-TPU training
- [ ] Model-parallel training
Theory
- [ ] Graph neural networks
- [ ] Open-endedness
- [ ] Evolutionary algorithms
- [ ] Reinforcement learning
- [ ] Beyond computer science
General
- [ ] Efficient code
- [ ] Quantum deep learning
- [ ] Implementing papers
- [ ] Understanding papers
- [ ] Staying up-to-date
- [ ] Research