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matthewearl.github.io | ||
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aimatters.wordpress.com
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| | | | | A few weeks ago, it was announced that Keras would be getting official Google support and would become part of the TensorFlow machine learning library. Keras is a collectionof high-level APIs in Python for creating and training neural networks, using either Theano or TensorFlow as the underlying engine. Given my previous posts on implementing an... | |
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d2l.ai
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| | | | | [AI summary] This chapter provides an in-depth exploration of recommender systems, covering fundamental concepts and advanced techniques. It begins with an overview of collaborative filtering and the distinction between explicit and implicit feedback. The chapter then delves into various recommendation tasks and their evaluation methods. It introduces the MovieLens dataset as a practical example for building recommendation models. Subsequent sections discuss matrix factorization, AutoRec using autoencoders, personalized ranking with Bayesian personalized ranking and hinge loss, neural collaborative filtering, sequence-aware recommenders, feature-rich models, and deep factorization machines like DeepFM. The chapter concludes with implementation details and ev... | |
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sirupsen.com
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| | | | | [AI summary] The article provides an in-depth explanation of how to build a neural network from scratch, focusing on the implementation of a simple average function and the introduction of activation functions for non-linear tasks. It discusses the use of matrix operations, the importance of GPUs for acceleration, and the role of activation functions like ReLU. The author also outlines next steps for further exploration, such as expanding the model, adding layers, and training on datasets like MNIST. | |
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weaviate.io
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| | | Learn about the different ranking models that are used for better search. | ||
