Introduction to machine learning in Node.js
summary
Shows runnable Node.js demos that actually classify images with TensorFlow and tensorFlow‑node, fit a Melbourne house pricing model with a decision tree library and csvtojson, and train a tiny brain.js network to spot Trumplike tweets. Includes numbers from a CPU test: synchronous crypto hashing ~9 req/s versus async offloaded ~17 req/s, an engineered zero error on a tiny test set, and working demos using worker_threads and queue scaling that demonstrate what breaks and what scales.
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chapters (21)
- 0:00Intro
- 0:41Tamar Twena-Stern
- 5:31What is Machine Learning
- 6:49Some Machine Learning Applications
- 7:36How To Build Machine Learning Model
- 9:50An Example For A DataSet
- 10:55Step 1 - Choosing A Model To Train
- 12:24More Sophisticated Decision Tree
- 13:53Lets Fit And Predict The Data
- 20:33Evaluate How Accurate Are The Predictions Of Your Model
- 24:37Popular Node.js Machine Learning Libraries
- 25:32TensorFlow.js
- 31:06Brain.js In A Nutshell
- 31:34Neural Networks In A Nutshell
- 31:51What Is A Neuron
- 32:59Basic Unit Of Neural Networks - Artificial Neurons
- 37:58Main Problems
- 38:55Node.js Architecture
- 47:08Prefer Not To Use Child Process
- 50:24Scale Up With Queues
- 51:14Queues VS Worker Threads
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