課程大綱： Recommender Systems with Python培訓
Introduction
What is AI
Computational Psychology
Computational Philosophy
Machine Learning
Computational learning theory
Computer algorithms for computational experience
Deep Learning
Artificial neural networks
Deep learning vs. machine learning
Preparing the Development Environment
Setting up Python libraries and Apache Spark
Recommendation Systems
Building a recommender engine frameworks
Testing and evaluating algorithms
Collabrative Filtering
Working with user-based and content-based filtering
Working with neighbor-based filtering
Using RBMs
Matrix Factorization
Using and extending PCA
Running and improving SVD
Working with Keras and deep learning neural networks
Scaling with Spark
Using RDDs and dataframes
Setting up clusters on AWS / EC2
Scaling Amazon DSSTNE and SageMaker
Summary and Conclusion
加入高級會員獲得助教答疑
