How to create machine learning recommendation systems with deep learning, collaborative filtering, and Python.
InstructorSundog Education by Frank KaneThe curriculum covers a wide spectrum of techniques, including K-Nearest-Neighbors, SVD, and recurrent neural networks for session-based recommendations.
Instruction includes using Apache Spark to handle and compute recommendations across massive datasets.
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These 4 complementary views highlight concrete, legible examples from the course presentation.
This slide illustrates the anatomy of a top-N recommender system, detailing how individual interests and item similarities feed into candidate generation, ranking, and filtering.
This technical diagram illustrates YouTube's candidate generation model, mapping user data through neural network layers to generate recommendation probabilities.
This architecture diagram illustrates the scaling of DSSTNE using Amazon EMR for CPU tasks and Amazon ECS for GPU tasks, coordinating via S3 and Docker containers.
This slide illustrates the architecture of an autoencoder for recommendations (Autorec), displaying input ratings, hidden layers with bias units, and reconstructed outputs.
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One signal from a review submitted before the displayed update suggests explanations may lack the depth required to build strong technical intuition for complex algorithms. This signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected.
A review submitted before the displayed update suggests the curriculum may lack coverage of modern two-stage architectures; while a recent update label is present, it does not prove these specific gaps were addressed.
The curriculum aligns well with the stated goals of developers and computer scientists through its inclusion of Python and deep learning foundations.