Advanced Machine Learning on Google Cloud

Compare static versus dynamic training and inference.

Manage model dependencies.

Set up distributed training for fault tolerance, replication, and more.

Export models for portability.

Understand at a high-level the types of problems computer vision may solve.

Understand some of the key concepts and model architectures typically used using computer vision.

This course introduces the products and solutions to solve NLP problems on Google Cloud. Additionally, it explores the processes, techniques, and tools to develop an NLP project with neural networks by using Vertex AI and TensorFlow.

• Recognize the NLP products and the solutions on Google Cloud.
• Create an end-to-end NLP workflow by using AutoML with Vertex AI.
• Build different NLP models including DNN, RNN, LSTM, and GRU by using TensorFlow.
• Recognize advanced NLP models such as encoder-decoder, attention mechanism, transformers, and BERT.
• Understand transfer learning and apply pre-trained models to solve NLP problems.

Devise a content-based recommendation engine.

Implement a collaborative filtering recommendation engine.

Build a hybrid recommendation engine with user and content embeddings.

Use reinforcement learning techniques for contextual bandits in the context of recommendations.