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Skip to content Publish AI, ML & data-science insights to a global community of data professionals. Sign in Submit an Article Toggle Mobile Navigation Toggle Search Search Machine Learning Neural Networks for Time-Series Imputation: Tackling Missing Data Part 3: Discover how a simple Keras sequential model can be effective Sara Nobrega Jan 22, 2025 11 min read Share Source: DALL-E. One of the common problems in time-series analysis is missing data. As we have seen in Part 1 , simple imputation techniques or
This explores whether networks carve a hard task into separable pieces on their own — and whether that modularity is something they grow naturally, something you have to force, or something that's qui
Abstract page for arXiv paper 1704.08863: On weight initialization in deep neural networks
TensorFlow is a powerful tool for training neural networks. In this blog post, we'll show you how to use TensorFlow to train a neural network
# Neural Translation of Musical Style ## Introduction Neural networks have been in the spotlight recently. This isn’t a big surprise as they are producing incredible results in a variety of problem spaces. Recently, speech recognition reached human parity with the expressive power of neural networks [ 1 ]. The amazing thing is that they were actually introduced in 1943 but have only now become popular [ 2 ]. This is because of the availability of large amounts of data and GPU parallelisation. These result
Amir Masoud Sefidian
[Note: this material was authored by mentor Gordon Robinson and is the contents of a thread he created on the Coursera Forums of the previous version of the course. I’m bringing it over to the new Discourse platform with…
Hierarchical attention leverages multi-level data structures in neural networks to boost efficiency, expressivity, and interpretability across diverse domains
To make deep neural networks feasible in resource-constrained environments (such as mobile devices), it is beneficial to quantize models by using low-precision weights. One common technique for