TensorFlow

Learn how to combine TensorFlow.js with Node-RED to create AI-enabled Node-RED applications in various environments.

Leverage AI in your Node.js applications using TensorFlow.js.

In this learning path, you'll learn about deep learning and long-short term memory networks (neural networks) and autoencoders, learn how to create a test physical model based data generator for anomaly detection, and finally learn how to create a deep learning neural network for anomaly detection on time-series data using Keras and TensorFlow.

Learn about basic autoencoder concepts using TensorFlow, then implement them on a Jupyter Notebook.

While each deep learning framework comes with its pros and cons, picking the right deep learning framework based on your individual workload is an essential first step every developer, deep learning practitioner, or data scientist must take.

Create a Jupyter Notebook that contains Python code for defining logistic regression, then use TensorFlow (tf.keras) to implement it.

Use a Jupyter Notebook to create an RNN model based on the LSTM unit to train and benchmark on the Penn Treebank data set, and learn how TensorFlow builds and executes an RNN model for language modeling.

Create a movie recommendation model based on collaborative filtering to get recommendations on movies you might be interested in.

Explore some of the environments and back ends that are available to better run machine learning tasks in Node.js with TensorFlow.js.

This series is designed for anyone interested in quickly getting up to speed with using Node.js with artificial intelligence.