have substantive expertise, you can do machine learning with math and statistics knowledge and hacking skills drewconway.com/zia/2013/3/26/the-data-science-venn-diagram
share the same code both in frontend and backend (Isomorphic JavaScript) ‣ You can use TypeScript ‣ You can provide interactive UI ‣ You don’t need to install runtime ‣ You can utilize the client side resources
share the same code both in frontend and backend (Isomorphic JavaScript) ‣ You can use TypeScript ‣ You can provide interactive UI ‣ You don’t need to install runtime ‣ You can utilize the client side resources - You can save money
‣ You don’t find enough information on the web ‣ Latest algorithms and classifiers are sometimes not available ‣ There are not sufficient libraries for evaluation metrics, data splitting and etc.
‣ You don’t find enough information on the web ‣ Latest algorithms and classifiers are sometimes not available ‣ There are not sufficient libraries for evaluation metrics, data splitting and etc.
from sklearn.cross_validation import train_test_split from sklearn.grid_search import GridSearchCV from sklearn.metrics import classification_report ... X_train, X_test, y_train, y_test = train_test_split(X, y) ... clf = GridSearchCV(...) … report = classification_report(y_test, y_pred) … Although each function is just a few lines of code, you miss it when you lose it
science ‣ Use Python if you are developing a machine learning app from scratch ‣ If you are familiar with JavaScript, you don’t necessarily have to switch to Python ‣ If you want to utilize client side resources, use JavaScript ‣ If you are using TensorFlow, you can train model in Python and predict in JavaScript
Natural ‣ ml.js ‣ math.js - Keras / TensorFlow compatible library with GPU acceleration - Simple and easy neural networks library with GPU acceleration - General natural language facilities with tokenizing and etc - Compilation of machine learning and numerical analysis libraries - Extensive math library for statistics and matrix operations
Natural ‣ ml.js ‣ math.js - Keras / TensorFlow compatible library with GPU acceleration - Simple and easy neural networks library with GPU acceleration - General natural language facilities with tokenizing and etc - Compilation of machine learning and numerical analysis libraries - Extensive math library for statistics and matrix operations