Completed
Document embeddings with CNN52
Class Central Classrooms beta
YouTube videos curated by Class Central.
Classroom Contents
How We Built a Job Recommender SaaS with Deep Learning to Disrupt the Job Market
Automatically move to the next video in the Classroom when playback concludes
- 1 Intro
- 2 We are a team of Machine Learning engineers
- 3 Step 1/2: Use Deep Learning to learn embeddings
- 4 Step 2/2: Use embeddings to recommend jobs
- 5 How do you measure the quality of a list of jobs?
- 6 Evaluation measure for implicit missing feedback
- 7 Why Deep Learning?
- 8 Why use Deep Learning? 2 Useful representations
- 9 Why use Deep Learning? 3 Variable length input
- 10 Word embeddings learn to capture semantics
- 11 JobNet is a cascade of useful representations
- 12 Document embeddings with CNN52
- 13 JobNet's architecture
- 14 Dask orchestrates the full task graph
- 15 Automating deployment with CI/CD
- 16 Reproducible infrastructure & software
