How winstonchu498/machine_learning_examples works
This repository is a collection of independent machine-learning course examples, not a single application. The learner’s broad workflow is to choose a course folder, run an example, and examine its model or result; individual scripts cover training, inference, visualization, and evaluation. The graph maps the major documented subject areas and shows only a few source-supported runtime relationships. Many examples were not sampled, so their internal wiring is intentionally omitted.
Main entry points: a3c/main.py A3C runner · a2c/main.py A2C runner · worker.py A3C worker · app.py Prediction API
Components 28
ML foundations
- Supervised models
supervised_class - Regression models
linear_regression_class - Support vector machines
svm_class - Ensemble methods
supervised_class2 - Clustering and embeddings
unsupervised_class - Bayesian methods
bayesian_ml
Deep learning
- Neural networks
ann_class2 - Keras examples
keras_examples
Language and vision
- Convolutional networks
cnn_class - Advanced vision
cnn_class2 - NLP examples
nlp_class - Advanced NLP
nlp_class3 - Recurrent networks
rnn_class - Hidden Markov models
hmm_class
Decision systems
- Reinforcement learning
rl - A3C runner
rl2/a3c/main.py - A3C worker
rl2/a3c/worker.py - A3C networks
rl2/a3c/nets.py - A2C runner
rl3/a2c/main.py - A2C learner
rl3/a2c/a2c.py - Parallel environments
rl3/a2c/subproc_vec_env.py
Applied ML
- Recommender systems
recommenders - A/B test client
ab_testing/client.py - Prediction API
supervised_class/app.py
Other components
- Course learner
- Ad test service
- Prediction caller
- Serialized model
Connections 24
- Course learner → Supervised models: explores examples
- Course learner → Regression models: explores examples
- Course learner → Support vector machines: explores examples
- Course learner → Ensemble methods: explores examples
- Course learner → Clustering and embeddings: explores examples
- Course learner → Bayesian methods: explores examples
- Course learner → Neural networks: explores examples
- Course learner → Keras examples: explores examples
- Course learner → Convolutional networks: explores examples
- Course learner → Advanced vision: explores examples
- Course learner → NLP examples: explores examples
- Course learner → Advanced NLP: explores examples
- Course learner → Recurrent networks: explores examples
- Course learner → Hidden Markov models: explores examples
- Course learner → Reinforcement learning: explores examples
- Course learner → Recommender systems: explores examples
- A3C runner → A3C worker: creates workers
- A3C runner → A3C networks: creates global networks
- A3C worker → A3C networks: creates local networks
- A2C runner → Parallel environments: creates environments
- A2C runner → A2C learner: starts learning
- A/B test client → Ad test service: requests ads and reports clicks
- Prediction caller → Prediction API: posts features
- Prediction API → Serialized model: loads model